PSI 2013

PSI Ψ The McGill Undergraduate Psychology Journal Issue III March 2013

Journal Coordinator Kiray Jones-Mollerup

Editor-in-Chief Linda Yu

Cover Design Mark Saffran

Editorial Board Lauren Agnew Bethsheba Ananng Huda Charkatli Mirra-Margarita Ianeva Ellie Shuo Jin Melanie Levy Beth Mansell Caroline Neel Rawan Nimeh William Prud’Homme Mark Saffran Stephanie Scala Yolanda Song

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Letter from the Editors As the editors-in-chief of the 2012-2013 edition of Psi, we are beyond thrilled to bring you the third issue of the McGill Undergraduate Psychology Journal. This year marks the first year that the journal is perfectly bound, and we feel that this momentous step in publishing is representative of the quality of work published this year. We were overjoyed with the level of interest in the journal, and we feel that the quantity of submissions allowed us to select the highest quality work from McGill students. Beyond this, we were able to publish a significantly greater number of papers this year, thus allowing us to better showcase the unique range of talent present at McGill University in Montreal, QC, Canada.

This year’s edition of Psi aimed to incorporate more original undergraduate work in order to draw attention to the diverse array of undergraduate research carried out in the field of psychology at McGill. Beyond incorporating original research, the journal also includes reviews from a variety of subsections of psychology; these include, but are not limited to, developmental psychology, history of psychology, social psychology, and neuropsychology.

We would like to acknowledge the incredible work done on this year’s edition of Psi by our team of dedicated editors. Without your commitment to the journal, publication would not have been possible. We sincerely appreciate all your efforts, from bake sales and brainstorming to late night meetings and last minute edits.

Without further ado, here is the 2012-2013 edition of Psi. We truly hope you enjoy reading it as much as we enjoyed creating it.

Sincerely, Kiray Jones-Mollerup, Journal Coordinator Linda Yu, Editor-in-Chief

On the Cover The cover of this year’s edition of Psi is loosely inspired by modern neuroimaging technology, and features the translucent profile of a person with a clearly defined brain.

The left-profile of a human head is reminiscent of phrenology, serving as a glimpse of the humble origins of the field of psychology. The lines defining the person’s profile are not uniform, and remind us that our neuropsychological knowledge is still largely limited and coming into focus. The resemblance to a modern brain scan reminds us of the enormous developments made each year in the field of psychology. Finally, the brain is brightly illuminated, a neon sign of the force that drives all human life.

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Contents

Positive Self-Regard and the Collective Self: Individualism and Collectivism as They Relate to Self-Efficacy on a Group Cognitive Task Joshua Jackson

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Environmental Factors Contributing to PTSD Prevalence in LowIncome Women Meaghan Kennedy

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The Effect of Coaching on Children’s Lie-Telling to Conceal Another’s Transgression Stephanie Cirnu

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The Influence of Contingency and Cue Configuration on Human Causal Learning Alexandra Tighe

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Mentoring Programs for Lower-Income Youth with Externalizing Symptoms: An Examination of the Moderators and Mediators of Effectiveness Carly Surchin Mental Health Courts: Do They Benefit Mentally Ill Persons in the Criminal Justice System?

Hubie Yu

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How Perceived Collective Autonomy and Distinctiveness of Cultural Customs Influence Motivation to Follow These Customs Midori Nishioka

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The Effect of Alcohol on Behavioural Inhibition of High-Impulsive and Low-Impulsive Individuals Stephanie Scala

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Early Lies: A Study of Pre-School Children’s Lie Telling and Executive Functioning Laura Penalosa

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Forward Models in Healthy Subjects and Schizophrenics Jaclyn Marcovitz

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Social Networks Christian Guay

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Activating Group I Metabotropic Glutamate Receptors (mGluRs) in the Dorsal Hippocampus During the Retention Interval Accelerates Forgetting of Location Memory Jeongho Lyu

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Positive Self-Regard and the Collective Self: Individualism and Collectivism as They Relate to Self-Efficacy on a Group Cognitive Task Joshua Jackson, Nathan Wong, Catherine Dick Supervisor: Mark Baldwin

Abstract Recent research in cross-cultural psychology has posited that the need for positive selfregard is not a universal need, but an individualistic construct. This research challenges such an assumption and examines the potential limitations of self-esteem measures that only measure personal identity, disregarding collective identity. In our study, we asked subjects to report on performance on a cognitive task in either a group context or as individuals. We found that people who endorsed collective values reported higher levels of self-efficacy following group tasks in comparison to low collectivists. A similar pattern of results was found for levels of flow, suggesting that collectivists operated optimally in a group context, while the reverse was true for those lower in collectivism. The implications of such a discrepancy on current assumptions in cross-cultural research are discussed.

The universal need for positive self-regard has been one of the most readily endorsed assumptions in psychological research (Allport, 1955; Epstein, 1973; James, 1890, Maslow, 1943; Rogers, 1951; Steele, 1988; Tesser, 1988). However, more recent work has argued that such a need is a construct rooted in American culture and tied to the values of freedom of choice, individual control, personal expression, individual responsibility, and independence (Fiske et al., 1997; Greenfield, 1997; Heine et al., 1999). Accordingly, East Asian cultures that emphasize self-criticism, effort, selfdiscipline, and reliance on others, such as Japan, would not share the same desire for positive self-regard (Heine et al., 1999). In a culture where the self is contingent on a network of relationships, a desire to be

viewed positively simply may not carry the same utility.

Such an assertion initially seems to be supported by research by Yamaguchi et al. (2007), which found that even though children from all East Asian countries outperformed American children, American children reported higher selfevaluations of their math and science abilities than did students from China, Korea, and Japan. However, on a test of implicit self-esteem as measured by the Implicit Associations Test (IAT; Greenwald & Farnham, 2000; Greenwald, McGhee, & Schwartz, 1998), there were no significant differences between East Asian and Western cultures. Such a finding not only suggests that positive implicit self-esteem may be a cultural universal, but also that positive self-regard

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may be a more fundamental need than cross-cultural research currently suggests.

An important concern in addressing one’s true need for positive selfregard is the difference in results between the IAT and the Rosenberg Self-Esteem scale (Rosenberg 1965). Research has suggested that there are two distinct streams of self-concept, namely personal identity and social identity (Tajfel, 1981; Tajfel & Turner, 1979, 1986; Turner, 1982). However, current explicit selfesteem measures are incomplete in this sense because they only measure selfregard concerning personal identity (Luhtanen & Crocker 1992). Social identity, which refers to those aspects of identity rooted in membership in social groups, has been termed as an indicator of collective self-esteem (Triandis et al., 1990). Personal self-esteem and collective self-esteem may both be important predictors of the biases and heuristics people use in relation to their self-concept.

Evidence has suggested that personal selfesteem is an important moderator in the tendency to engage in self-enhancement and self-serving biases (Alloy & Abramson, 1979, 1982; Crocker et al., 1987; Luhtanen & Crocker, 1992). It is thus entirely possible that while personal selfesteem tends to enhance self-serving biases, collective self-esteem may be an important moderator in collective biases and heuristics such as in-group bias and other collective or group-level biases.

This recent line of research asserts that a person’s identity as either a collectivist or an individualist could play a role in determining the type of biases they hold and the contexts in which they experience the highest self-esteem.

Research by Iyengar and Lepper (1999) has already explored this difference in a classroom setting. Much like self-esteem, personal choice was previously labeled as a

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universally positive mechanism (Cordova & Lepper, 1996; Zuckerman et al. 1978).

However, Iyengar and Lepper found that when East Asian students were under the impression that members of their in-group had chosen the nature of their cognitive task, they both persevered longer and rated the task as significantly more enjoyable (Iyengar & Lepper, 1999). This work on the meaning of choice illustrates that motivation may have different origins amongst individualist and collectivist cultures, but that it does not relate to differences in the conception of selfesteem.

The purpose of our research was to examine whether collectivists and individualists have different conceptions of positive self-regard, which might account for differences in explicit self-esteem as measured by the Rosenberg Self-Esteem scale. To test such a theory we used posttask measures to prime either collectivist or individualist conceptions and then measured self-efficacy to assess whether subjects showed discrepant self-efficacy according to either a personal or a collective self-concept. In addition to evaluating perceived efficacy on cognitive tasks, we also evaluated the potential for learning and integration.

Csikszentmihalyi’s (1993) concept of flow is a perfect medium for this.

Csikszentmihalyi operationally defines flow as the combination of high challenge and high ability. We hypothesized that individualistic-oriented subjects should give themselves higher self-efficacy ratings following a cognitive task. However, if post-task questions were framed in terms of the participant’s group, collectivists would give higher ratings. Because the group task was non-hierarchical and cooperative in nature, one would expect horizontal collectivists to report the highest self-efficacy, as they could identify

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most with a non-hierarchical in-group (Triandis, 1995). Horizontal collectivism is a form of collectivism that stresses decentralization and egalitarianism. It is contrasted with vertical collectivism, which is based on hierarchical structures of power and conformity. Following the same trend as self-efficacy, collectivists would report greater flow while performing cognitive tasks in a group condition rather than alone, while individualists would report higher flow following the alone condition.

Methods Fifty-four subjects were recruited either from the McGill subject pool or by word of mouth. Participants recruited through word of mouth were not given any knowledge as to the experimental hypothesis, but were given the same basic recruitment script as appeared on the subject pool description page. Participants were all McGill students. Prior to testing we administered the Auckland Individualism and Collectivism scale (Shulruf, Dixon & Hattie, 2003), and the Rosenberg Self-Esteem Scale (Rosenberg, 1965) to gauge self-esteem and levels of individualism and collectivism. Due to some unpredicted circumstances, some subjects completed the measure after completing the lab component of our study, but were not debriefed until they had completed both components.

The Auckland Individualism and Collectivism Scale included three dimensions of individualism: responsibility (acknowledging one’s responsibility for actions), uniqueness (distinction of self from others), and competitiveness. It also defined two dimensions of collectivism: advice (seeking advice from people one feels close to), and harmony (seeking to avoid conflict). We coded the scale for individualism vs. collectivism, as well as horizontal vs.

vertical attitudes. When coding for the individualism and collectivism we used two different schemes. In the first, we used a median split to assess subjects as either high or low in collectivism and individualism. In the second method, each participant’s individualism score was subtracted from his or her collectivism score. If this difference was positive, they were coded as a collectivist (n=18).

Otherwise, they were coded as an individualist (n=26).

Following the premeasure, participants completed a series of riddles in a controlled lab environment.

Participants completed these riddles either in a group setting or alone. Each subject was allotted 8 minutes per condition to complete the series of riddles, in any order they chose. Groups varied from 3-6 people, but no less, as we established that a condition with 2 people would not properly evoke a collectivistic context. The order in which subjects completed the group and individual tasks was counterbalanced, but the order of the riddles remained the same. Such a precaution controlled for both a practice effect and any discrepancy in the difficulty of the riddles in the two sets. Before being presented with the riddles in the group condition, participants filled in the identification numbers of their group members. This question enabled us to gauge which subjects were in each group, which would indicate whether characteristics of specific group members affected the group dynamic as a whole.

Following each riddle condition participants were asked to evaluate their performance. Questions were the same for both conditions. They respectively measured: (a) level of flow as measured by each participant’s self-assessed level of challenge and ability on the task, (b) each subject’s self-efficacy on the task, and (c)

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measures of dominance and co-operation on the task, related to horizontal and vertical collectivism and individualism.

For the riddles themselves, we selected those that were conceptually challenging and counterintuitive. Such a selection was designed to minimize any sort of performance effect, and to prevent a large increase of ability in the group condition. Essentially, we selected riddles that would be no easier to solve while cooperating with a group than doing individually. Furthermore, we were not interested in the actual performance on the cognitive task but in the participants’ self-ratings of self-efficacy in either a group or individual context. Thus, we did not measure the number of correct answers given by participants, but were careful to frame our post-task questions with regard to each subject’s condition. Such a frame included asking the participants in the group condition about their group’s performance on the riddles, rather than evaluating their individual performance and contribution to the group.

Results Analysis was performed on 44 subjects. Using both coding systems of collectivism/individualism, the explicit self-esteem difference between collectivists and individualists found by Yamaguchi et al. (2007) was replicated. High collectivists (n=20, M = 1.82, SD = .1) were found to have lower self-esteem than low collectivists (n=23, M = 2.26, SD = 0.11); F(1,39) = 8.703, p = .005. Additionally, collectivist subjects (M = 1.861, SD = 0.12) reported marginally lower selfesteem than individualist subjects (M = 2.156, SD = 0.1); F(1,41) = 3.67, p = .062.

Before looking at differences between individualists and collectivists on the posttask measure, one must acknowledge that subjects in the group condition (M = 3.34, SD = 0.48) reported significantly more

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Figure 1. Collectivist and individualist self-esteem.

self-efficacy than subjects completing the task alone (M = 3.08, SD = 0.57); F(1,32)= 10.4, p = .002. Despite this main effect, there was a marginally significant interaction between each subject’s degree of collectivism and their efficacy in either a group or individual cognitive task; F(1,41) = 3.228, p = .080. Consistent with our hypothesis, this interaction effect showed that collectivist subjects reported higher self-efficacy in the group context (M = 3.4, SD = 0.53) than in the alone condition (M = 2.99, SD = 0.66), while those low in self efficacy experienced no difference in self efficacy between the group context (M = 3.28, SD = 0.42) and alone (M = 3.17, SD = 0.43). After controlling for self-esteem, this interaction became significant; F(1,40) = 6.34, p =.016.

In addition to measures of selfefficacy, there were also significant

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differences between subjects’ individualism and collectivism and post-task reports of flow. Participants experienced greater flow in the group context (M = 3.47, SD = 0.48) than alone (M = 3.34, SD = 0.43); F(1,41) = 3.75, p = .059. However, this main effect was produced by the interaction of flow with each subject’s degree of collectivism, F(1,42) = 6.608, p = .01. Subjects lower in collectivism experienced lower levels of flow in the group context (M = 3.23, SD = 0.44) than individually (M = 3.54, SD = 0.55), while those high in collectivism showed a nonsignificant increase in flow in the group context (M = 3.43, SD = 0.41) rather than in the individual context (M = 3.39, SD = 0.43). With respect to horizontal and vertical collectivism, horizontal collectivists (M = 3.445, SD = 0.1) reported a non-significant trend, reporting higher self-efficacy after doing a group task than non-horizontal collectivists (M = 3.229, SD = 0.1); F(1,39) = 2.17, p = .149).

As a final note, there was a significant correlation between self-efficacy scores in each condition; r(1,41) = .430, p = .002). This means that there was a moderate performance effect due to people’s ability to answer the riddles.

Discussion Although prior research would have us believe that explicit self-esteem differences stem from a discrepancy in the need for positive self-regard between individualist and collectivist cultures, our current findings challenge these expectations (Heine et al., 1999). They demonstrate that self-efficacy can vary based on the degree to which one endorses the collective self rather than the personal self. Contexts priming in-group biases rather than self-serving ones may produce very different scores of self-efficacy

between groups that highly value a collective self and those who do not.

An important first step in our study was to replicate Yamaguchi et al.'s (2007) observation that collectivist and individualist subjects gave significantly discrepant ratings of explicit self-esteem as measured by the Rosenberg. This reinforces the theory that levels of collective and personal identity reliably predict different scores on current explicit self-esteem measures. It also means that our sample was an accurate representation of individualists and collectivists, which gives substantial external validity to our findings. Using the median split coding scheme, our study found that subjects low in collectivism gave higher ratings of selfefficacy when reporting in an individual context, while the reverse was true for those high in collectivism. The same trend was found for flow, where those low in collectivism had radically higher ratings of flow in the individual context than in the group context, whereas the trend for high collectivists was in the opposite direction.

This work adds to a growing body of recent research that has explored the different processes of thinking amongst individualists and collectivists. Recently, Luhtanen and Crocker (1992) developed a scale designed to measure collective selfesteem within the context of a social identity. The scale is comprised of questions measuring private self-esteem, public self-esteem, and community membership. Our research demonstrating the limitations of self-esteem scales that only take into account private identity suggests that new self-esteem scales would do well to take questions from current explicit self-esteem measures such as the Rosenberg, but also from scales such as Luhtanen and Crocker’s.

Other such explorations have looked at different biases amongst

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individualist and collectivist cultures. Krull et al. (1999) found that 83 out of 96 Chinese and American subjects displayed correspondence bias when inferring essayists’ attitudes. However, Chinese participants were most susceptible to bias when the essay was on the importance of environmental differences in behaviour, while Americans were most susceptible when the essay dealt with the importance of biological differences. Other studies have shown evidence that collectivists are just as disposed to the correspondence bias when the attribution relates to the function of a group. East Asians will be more likely to attribute the failure of a group project to the people in the group, while Americans will attribute failure more frequently to an individual failing to complete the project.

Similarly, individualists would deem a fireman who failed to save a child from a burning building as more blame-worthy while collectivists would be more likely to assign blame if the event involved a team of firemen (Menon & Morris, 1999). Such findings challenged the long held view that correspondence bias does not exist amongst collectivists and that people in Western cultures are simply more prone to make attribution errors (Choi et al., 1999; Mattila & Patterson, 2004).

Our study contributes to this stream of research, furthering the effort towards a fine-tuned understanding of patterns of cultural differences in cognition. We hope to further dispel sweeping claims about broad differences in fundamental thought processes and needs of people between individual and collective communities. Patterns of cultural difference such as differences in explicit self-esteem are not general across domains but often specific to the type of actor in question.

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Limitations and Future Research Our use of self-efficacy as the dependent measure poses a question about the generalizability of our study. The construct of self-efficacy is much narrower than the construct of self-esteem. It relates more to one’s belief in one’s ability to succeed in specific situations, while selfesteem is a more global measure of wellbeing (Luszcynska & Schwarzer, 2005).

Additionally, self-efficacy does not necessarily pertain to actual ability, while self-esteem is often a reliable predictor of many measures of well-being in Western society. However, upon recognizing the differences in the two constructs, one must also appreciate that both self-esteem and self-efficacy both play a large role in how one approaches goals, tasks, and challenges (Bandura, 1977). One’s selfefficacy is also a more stable component than is often perceived and often plays a large role in the formation of self-concept (Bandura, 1986; McAdams, 2006). In our study, self-efficacy measures were much more appropriate in a situation where an in-group dynamic was primed through the use of cognitive tasks. Despite the semantic differences between self-esteem and selfefficacy, the same features of competence, autonomy, and relatedness drive both constructs (Deci & Ryan 2000; Kasser & Ryan 1993). We therefore believe that the impact we observed of collectivist attitudes on self-efficacy in different contexts can be generalized to self-esteem differences amongst collectivists and individualists.

However, there are two more significant limitations of our research that future studies should address. The first is in relation to the riddles participants answered. As mentioned previously, the riddles were both conceptually difficult and counter-intuitive so as to protect against potential performance effects (see Endnotes). However, despite this there

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was still a moderate correlation between conditions, which no doubt weakened the power of our manipulation. Moreover, the fact that people reported radically more self-efficacy following the group condition is a further limitation of our study, as an optimal paradigm would show no absolute differences between the group condition and the individual one. Our sample characteristics also represent a limitation of our study. While it is true that we found a balance between collectivists and individualists amongst our sample, an optimal study would pursue a representative sample comprised of subjects who were raised in both collectivist East Asian cultures and individualist American cultures.

Our study nonetheless represents an important contribution to the dynamic field of cross-cultural psychology and hopefully supplies evidence in support of the argument that the need for positiveregard is a universal and fundamental phenomenon.

Given our study’s limitations, it would be important to do a replication study that applied a previously established cognitive task, such as Raven’s Advanced Progressive Matrices, to a sample more representative of collectivist cultures (Raven 1998). Furthermore, a follow up study could incorporate Luhtanen and Crocker’s (1992) scale of collective self-esteem, as well as the Rosenberg Scale, in order to predict ratings of self-efficacy in different contexts.

Such an inclusion would not only increase reliability for this young measure of collective-esteem but could also compare the stability between measures of both collective and personal explicit self-esteem and self-efficacy. This would add credibility to the theory that self-efficacy is driven by many of the same psychological mechanisms as is self-esteem.

Finally, new research should move towards the construction of a more comprehensive self-esteem scale that can make cross-cultural predictions. Our research, when studied alongside Yamaguchi et al.'s (2007) findings, makes a strong case illuminating the limitations of current explicit self-esteem measures, such as the Rosenberg, for cultures that value a more interdependent self.

Endnotes i) Psyc 351 Recruitment Script: “This study is comprised of a collection of short studies created by PSYC351 students as part of a larger research assignment. Participants will complete a subset of these short studies, all of which are related to the area of social and personality psychology. After signing up, participants will be sent a link to a very short online personality questionnaire (approx. 5 minutes long) that they will complete before coming into the lab.

During the lab session, participants will complete more personality scales, and will answer questions about their attitudes and perceptions of various stimuli (e.g., photographs).” ii) Auckland Individualism and Collectivism Scale (Items are presented with an agreement scale of 14) Items 1. I discuss job or study-related problems with my parents.

2. I consult my family before making an important decision.

3. Before taking a major trip, I consult with most members of my family and many friends.

4. It is important to consult close friends and get their ideas before making a decision.

5. Even when I strongly disagree with my

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group members, I avoid an argument.

6. I hate to disagree with others in my group.

7. It is important to make a good impression on one’s manager.

8. In interacting with superiors, I am always polite.

9. It is important to consider the needs of those who work above me.

10. I sacrifice my self-interest for the benefit of my group.

11. I reveal personal things about myself.

12. I have the feeling that my relationships with others are more important than my own accomplishments.

13. I like to live close to my good friends.

14. To me, pleasure is spending time with my superiors.

15. To me, pleasure is spending time with others.

16. I help acquaintances, even if it is inconvenient.

17. I define myself as a competitive person.

18. I enjoy working in situations involving competition with others.

19. Without competition, it is not possible to have a good society.

20. Competition is the law of nature.

21. I consider myself as a unique person separate from others.

22. I enjoy being unique and different from others.

23. I see myself as “my own person.”

24. I take responsibility for my own actions.

25. It is important for me to act as an independent person.

26. Being able to take care of myself is a primary concern for me.

27. I consult with my superior on workrelated matters.

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28. I prefer to be self-reliant rather than depend on others.

29. It is my duty to take care of my family, even when I have to sacrifice what I want.

30. When faced with a difficult personal problem, it is better to decide for myself, than follow the advice of others. iii) Sample Riddles from Cognitive Task 1. If three cats catch three mice in three minutes, how many cats would be needed to catch 100 mice in 100 minutes?

2. A man is trapped in a room with only two possible exits: two doors.

Through the first door, there is a room constructed from magnifying glass. The blazing sun instantly fries anyone or anything that enters. Through the second door, there is a fire-breathing dragon.

How does the man escape?

3. You have a 3 gallon jug and a 5 gallon jug. You need to measure out exactly 7 gallons of water. How do you do it?

4. Can you divide a cake in 8 pieces with 3 cuts?

5. How can you physically stand behind your father while he is standing behind you?

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Triandis, H.C., McCusker, C., & Hui, C.H. (1990). Multimethod probes of individualism and collectivism.

Journal of Personality and Social Psychology. 59(5). 1006-1020.

Yamaguchi, S., Greenwald, A., Banaji, M. R., Murakami, F., Chen, D., Shiomura, K., … Krendl, A. (2007). Apparent Universality of Positive Implicit Self-Esteem.

Psychological Science, 18(6), 498-500.

Zuckerman, M., Porac, J., Lathin, D., Smith, R., & Deci, E. L. (1978).

On the importance of selfdetermination for intrinsically motivated behavior. Personality and Social Psychology Bulletin, 4, 44-46.

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Environmental Factors Contributing to PTSD Prevalence in Low-Income Women Meaghan Kennedy

Although 61% of men and 51% of women will experience at least one traumatic event in their lifetime, most will go on to recover. Yet 5% of men and 10% of women, in a nationally representative sample, will go on to develop posttraumatic stress disorder (PTSD) following trauma, revealing that women are twice as likely to suffer from PTSD as men (Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). A PTSD diagnosis is defined as experiencing or witnessing of a life-threatening traumatic event that elicited feelings of horror, terror, and fear, and is characterized by re-experiencing, avoidance, numbing, and extreme arousal (American Psychiatric Association, 2000).

Women suffer from PTSD symptoms for longer periods than men, as evidenced by the fact that the median time to PTSD remission is 48 months in women, compared to 12 months in men (Breslau et al., 1998). The disorder prevalence is thus clearly gendered, marking women as a higher-risk group.

However, Bassuk, Buckner, Perloff, and Bassuk (1998) reported that the lifetime prevalence of PTSD among lowincome women was 35%, over three times that of women of all ages reported by Kessler et al. (1995) in the National Comorbidity Survey. Multiple studies with

samples of low-income women reported PTSD rates higher than the national average, ranging from 22%-34.4% (Alim et al., 2006; Davis, Ressler, Schwartz, Stephens, & Bradley, 2008; Gill, Szanton, Taylor, Page, & Campbell, 2009). These results are in line with evidence from large epidemiological studies, which have documented that individuals of low socioeconomic status are at higher risk for both psychopathology and trauma exposure (Breslau et al., 1998; Kessler, Chiu, Demler, & Walters, 2005). This highlights the fact that low-income women are a high-risk group for this highly debilitating disorder, in addition to being at risk for a whole host of other environmental factors directly linked to PTSD such as chronic exposure to community violence, early adversity, sexual assault, and inadequate medical resources (Breslau et al., 1998; Gill et al., 2009). Given severe disadvantages already facing this population, there is an impetus to examine the risk factors facing lowincome women, and to determine which factors offer the best chance for intervention.

It is important to note that there are numerous pathways to developing the disorder, as no one type of trauma or experience of previous adversity leads

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reliably to PTSD (Breslau et al., 1998).

While the research literature has shown that a non-negligible portion of the variance is due to individual factors, such as genetic variance and subjective immediate reactions to the traumatic event, the literature on individual factors contributing to the disorder is more limited than evidence for environmental risk factors at this time regarding the lowincome female population. To date, there have only been two twin studies focusing on females in a large community sample, neither noting any significant results regarding heritability directly related to the low-income women population. In one such study, Stein, Jang, Taylor, Vernon, and Livesley (2002) found evidence for moderate genetic contributions to assaultive, but not non-assaultive, trauma, hypothesizing that genes indirectly influence the likelihood of experiencing trauma via personality traits. Sartor et al.

(2011) concluded that additive genetic variance, due to differences between genes, accounted for 71% of the variance in developing PTSD, but only 28% of the variance in personality-linked trauma.

Finally, a meta-analysis by Ozer, Best, Lipsey, and Weiss (2003) revealed that the subjective individual-level factor of peritraumatic dissociation (altered sense of reality) immediately post trauma had a moderate-to-large influence on developing the disorder, and may lead to greater avoidance coping.

What are the main issues accounting for higher prevalence rates in low-income women for posttraumatic stress disorder, and which factors are contributing the most? It would be futile to assume that there exists one root cause of the disorder; therefore multiple risk factors and various global issues must be examined instead. The purpose of this paper is to pinpoint the most important

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environmental risk factors leading to PTSD, which we may effectively intervene on and consider in treatment recommendations. The rationale is that research on individual factors is somewhat less conclusive, and therefore less informative for strong interventions. It is also more effective to intervene on environmental factors at this point in time, as modern technology is not sufficient for intervening on genetic factors. Also, interventions on individual factors, such as peritraumatic dissociation, would not reach as many low-income women at risk as a more large-scale intervention on environmental factors.

This investigation will examine three major factors related to the elevated risk for PTSD in low-income women: higher exposure to traumas that lead to PTSD, greater initial vulnerability to developing the disorder, and problems accessing appropriate treatment.

Conclusions will indicate which types of interventions would prove most effective in reducing the prevalence of PTSD in low-income women, and will offer brief recommendations for adapting PTSD treatment and to better suit the needs of this disadvantaged population.

I.

Do low-income women experience more traumas that are predictive of PTSD?

One proxy for higher exposure to violence and trauma is urban residence, as evidenced by rates of PTSD in urban women that are two to four times higher than the general population rate for women (Kessler et al., 1995). In the Moving to Opportunity (MTO) social experiment in the 1990s, which aimed to move families from high-poverty neighborhoods to low-poverty neighborhoods, the effects of community violence on female residents were

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examined (De Souza Briggs, Popkin, & Goering, 2010). The authors noted that the Gordon and Riger (1989) term “female fear”, or “the fear of sexual harassment, coercion, and rape”, is higher in low-income communities (De Souza Briggs et al., 2010, p. 94). The following qualitative account from their study displays this reality: So far, since I’ve been here, I’ve never heard no gunshots, no none of that. That was a big thing that I, I don’t know, I didn’t realize it, but once you’ve grown up in a neighborhood and that’s something you heard on a daily basis, you don’t know that that’s not how it’s supposed to be (p. 91).

Importantly, girls whose families moved due to a MTO voucher reported lower levels of female fear and harassment from males (De Souza Briggs et al., 2010). One drawback of this examination into the female fear is that genetic measures were not taken, which would indicate whether innate factors that contribute to personality were leading to greater exposure among these females. Another issue is the fact that the actual levels of assaultive victimization experienced by girls in the experimental group versus the control group were not measured, which would distinguish between the effects of threatened violence versus actual experienced trauma. However, Kingsley, & Pettit (2008) indicated that high-risk urban neighborhoods in MTO were objectively less safe, with violent crime rates twice that of the low-poverty neighborhoods, and a study of girls living in high-risk urban neighborhoods by Menard and Huizinga (2001) found that more than one-third were victimized over a one-year period.

The prevalence of violence and threat of assault in urban, high-poverty neighborhoods is particularly relevant because in Gill et al. (2009)’s sample of

low-income women seeking medical assistance in an inner-city clinic, most of the cases of PTSD involved an assaultive event as the precursor to the disorder.

These included (in order of frequency) rape or sexual assault, child sexual abuse, intimate partner violence (IPV), and physical abuse (not related to IPV). In contrast, those women who experienced trauma but did not develop PTSD, the event was most often the unexpected death of a family member or close friend, or witnessing assault on another person; assaultive violence were far less frequently identified in this group. Of course, as the sample was recruited from a healthcareseeking urban population, these results should be interpreted with caution due to the possibility of random error from a unique treatment-seeking subgroup.

The two trauma types that research has shown to confer the highest risk of developing PTSD in women are both assaultive in nature – IPV and sexual assault, particularly rape. El-Bassel, Gilbert, Witte, Wu, and Chang (2011) found that women with PTSD were more likely than those who did not meet criteria to have a past experience of intimate partner violence. Another study by Green (2006) of low-income female patients in family planning noted the prevalence of IPC amongst low-income women found that half the sample had experienced domestic violence, and more than one third had been raped and/or experienced childhood abuse. Yet again, the sampling method of these studies suffers from the same faults as that of Gill et al. (2009), in that it consisted of low-income minority women recruited from an urban emergency department, and therefore likely differs in important ways from women with who do not seek healthcare.

Many low-income women may lack insurance that covers the costs of

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treatment, making them unlikely to seek it out. The study by Gill et al. (2009) also suffers from the caveat of having no higher-income female reference group that may point out other factors accounting for the high rates of IPV, and increase generalizability of the findings.

Vogel and Marshall (2001) sought to account for these variables in their study examining prevalence of PTSD in women with a history of IPV, by using a large community sample, equally representing ethnic backgrounds, and examining three types of partner abuse (threats of violence, acts of violence, and sexual aggression). The authors found that 47% of women who had experienced moderate violence had high numbers of PTSD symptoms, and that the highest symptom group experienced more abuse on all three measures of IPV (Vogel and Marshall, 2001). Importantly, the authors determined that there were no ethnic differences across levels of PTSD symptoms, indicating that SES may contribute more than ethnicity to exposure to abuse and development of symptoms.

Unfortunately, not many studies examining PTSD in low-income women utilize such strict methodological measures, so further research should focus on replicating these findings. Also, data was gathered via self-report, which means that the authors could not rule out issues with retrospective recall as a confounding factor.

Many researchers also note that rape confers a high risk of developing PTSD (Breslau et al., 1998; Gill et al., 2009; Kessler et al., 1995). Breslau et al.

(1998) found that, although men face higher exposure to trauma overall, it is women’s higher exposure to sexual assault (compared to men) that contributes to their higher prevalence of PTSD. This epidemiological study was cross-sectional

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and therefore could not establish that rape was reliably the causal trauma for PTSD in women. Vogel and Marshall (2001) found high reports of rape amongst their low-income female sample, as did other studies examining this population (Gill et al., 2009; Davis et al., 2008; Vranceanu, Hobfoll, & Johnson, 2007). Vogel and Marshall (2001) also found that the rate of PTSD symptom report was almost the same in women who experienced moderate partner violence and rape (63%) as women who experienced just severe violence and no rape (65%); the highest symptoms came with both severe violence and rape (71%). These results indicate that the PTSD-symptom inducing potential of partner rape is on par with that of severe assaultive violence, further establishing it as a reliable risk factor for developing PTSD. Severity of trauma in general is associated with more PTSD and a greater amount of symptoms, and also appears to be more correlated with low SES than ethnicity (Green, 2006; Vogel & Marshall, 2001).

A final risk factor related to trauma exposure is the amount of trauma. Revictimization is highly predictive of developing posttraumatic stress disorder (Follette, Polusny, Bechtle, & Naugle, 1996)]. In three national female samples, re-victimized respondents were 4.3 to 8.2 times more likely to develop PTSD in the past six months than respondents who were never victimized (Walsh et al., 2012).

In a study by Schumm, Hobfoll, and Keogh (2004), participants’ early experience of physical and or sexual assault in childhood were more likely to trigger adult re-victimization through rape, resulting in severe adult PTSD. Gill et al. (2009) reported women in the high symptom group had abuse histories two to three times worse than women with few

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symptoms. Again, generalization of these results is restricted by the fact that both studies sampled directly from low-income women in inner city clinics, raising questions as to how the distribution of trauma type would look in a reference group of higher-income women. However, Breslau et al. (1998)’s results from a large community sample also indicated that women were more likely to develop PTSD following assaultive violence, although men from the sample were more likely than women to experience assaultive violence in general.

Do low-income women have a higher initial vulnerability for developing PTSD? Given that many people exposed to significant trauma will not, in fact, go on to develop posttraumatic stress disorder, it begs the question: who is prone to developing PTSD, and are low-income populations more susceptible to begin with than the general population? One factor that is notably high in low-income women is the presence of co-morbid mental disorders. In a case-control longitudinal study, Bassuk et al. (1998) found that 47% of their extremely low-income sample had two or more lifetime disorders (disorders that persist, in essence, for a lifetime), and only 31% had none. Of the respondents with two or more disorders, 89% had a substance use disorder, 85% had PTSD, 73% had depression, and these disorders were disproportionately represented compared to the general female population sample in the National Comorbidity Survey (Kessler et al., 1995).

The authors noted that these rates were high compared to Kessler et al.

(1995)’s reported rates of these disorders in a nationally representative sample. Breslau et al. (1998) also found that preexisting anxiety and/or depression played a small but significant part in the gender

differences in PTSD prevalence, with comorbidity causing higher incidences of PTSD and existing more in women than men. In a meta-analysis, Najavits, Weiss, and Shaw (1997) found a high incidence of co-morbidity between post-traumatic stress disorder (PTSD) and substance abuse, particularly among females, who show rates of co-morbidity between 30% and 59%. This combination most commonly resulted from previous, repetitive childhood physical and/or sexual assault. Rates of co-morbidity for men are two to three times lower, and typically stem from combat or crime trauma, a finding that is important in distinguishing that low-income women are more at risk for a co-morbid PTSD and substance abuse diagnosis.

Another significant environmental risk factor in low-income women for developing PTSD is childhood maltreatment, particularly childhood sexual abuse, with up to two fifths of lowincome women with PTSD reporting past experience of child abuse in one study (Bassuk, Dawson, Perloff, & Weinreb, 2001; El-Bassel et al., 2011). Bassuk et al.

(2001) reported that, in general, extremely poor women with long-term PTSD were more likely to have spent childhood in environments of more violence, threat, and anger than women without the disorder, and that 79% of the low-income, non-homeless women had been victims of assaultive violence at one point in their lives. The results of this study are based on low-income and homeless mothers, however, thus restricting generalizations to childless women. Schumm et al. (2004), as noted before, found that both childhood physical and sexual assault had direct influence on the development of PTSD, and indirect effects that were mediated by rape in adulthood.

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Schumm et al. (2004) also found that another risk factor, interpersonal resource loss, was directly related to PTSD symptoms, and the authors hypothesized that early adversity may have impacted later interpersonal resources, as well as the severity of PTSD symptoms following trauma in adulthood (rape). The results of Vranceanu et al. (2007) give additional support for the direct effects of one aspect of interpersonal resources, social support, on levels of PTSD symptoms in a sample of women with an annual income of $15,000 or less, many of whom were unemployed. Specifically, levels of posttrauma social support in adulthood were found to mediate the relationship between experiencing child maltreatment and adult PTSD. Childhood maltreatment was also found to have direct effects on later PTSD. Vranceanu et al. (2007)’s study had strengths in its broad ethnic sampling strategy, resulting “in a community sample with equal numbers of EuropeanAmerican and African-American women” (p. 8). However, the study was limited by its cross-sectional design, as it was not clear exactly how the interchange between stress, social support, and PTSD symptoms would change over time in women with the disorder. The sample, 100 women recruited from an inner-city medical clinic, was small, and consisted of only healthcare-seeking low-income women. The findings of Vranceanu et al.

(2007) were nevertheless supported by those of Ozer, et al. (2003), who reported that more social support was associated with lower likelihood of later PTSD symptoms, also noting that factors operating closer in time to that actual trauma (proximal factors) were more strongly related to later developing PTSD than more distal factors (r =.40 versus r =.20). This has relevance for later

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discussion regarding interventions and treatment, as intervening on proximal factors will be more effective.

Interestingly, Vranceanu et al. (2007) also tested the impact of life stress on PTSD, and found that stress only indirectly impacts later PTSD. From these results, the authors hypothesized that stress only acts “via deteriorating women’s already impaired social support,” thus increasing the risk for later developing the disorder. There is only limited support, provided in a meta-analysis by Brewin, Andrews, and Valantines (2000), that life stress, particularly when experienced posttrauma, places individuals at a higher risk of going on to develop PTSD. However, the authors did not find specific effects for low-income women in this review. Also important to note is the fact that neither study tested how genetic factors may also mediate the association between child maltreatment and adult PTSD, an important limitation that indicates there is more to mediation of this relationship than simply interpersonal resources.

II.

Are low-income women less likely to receive treatment after developing PTSD?

Although the preceding two issues contribute to a good portion of the high prevalence of posttraumatic stress disorder in low income women, it is important to mention a third crucial problem: this population may be facing very real issues regarding access to effective treatment.

Davis et al. (2008) investigated both individual and institutional barriers to treatment in low-income urban AfricanAmerican women, finding that higher levels of both types of barriers were associated with higher PTSD symptoms.

Common individual barriers were lack of transportation, funds and daily life stressors, while common institutional barriers included unfamiliarity with

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and/or ineligibility for treatment services.

These barriers were not entirely due to personal disapproval of seeking mental health treatment, as 43.9% of participants rated at least one mental health service as important. Despite the desire for treatment, only 13.3% of the sample reported receiving previous PTSD treatment. Generalization of the study results are limited, however, due to the sample consisting of only one ethnicity and urban residency. Despite this, the review of barriers was very comprehensive.

Chung et al. (2012) sought to gather qualitative data from health professionals treating uninsured, predominantly poor and minority patients in inner-city clinics to understand the access issue further.

Although the sample was not restricted to clinicians treating females only, the authors did not note this as a limitation for interpreting the results for low-income women. The main results reported by health professionals were that trauma is normalized and not considered to be a mental health problem in their patients with trauma histories.

“Many people…don’t view themselves as being mentally ill” one physician reported.

“People with posttraumatic stress had a bad thing happen to them. And many…grew up in a socioeconomic climate of - get over it. Get on with it.”

(Chung et al., 2012). However, the data provided may not have been the opinion of experienced health professionals, as the authors reported that around half of the interviewed clinicians had been working in their setting for less than two years. This highlights yet another issue in many innercity community clinics, which is that of high staff turnover is common due to provider burnout. Maintaining a trusting patient-provider relationship is difficult when staff turnover and patient instability

are combined (Chung et al., 2012).

Problematic relationships with health care providers were reported by women in the Bassuk et al. (2001) study on extremely poor women, who perceived this as an additional barrier to care.

A third consideration is that PTSD symptoms may be manifesting as physical symptoms in disadvantaged populations such as low-income women, resulting in underreporting and less access of mental health treatment (Gill et al., 2009). A study on women seeking treatment from an inner-city emergency department reports that women with a lifetime history of PTSD reported more current and past physical health conditions, even after controlling for demographics such as ethnicity (Gill et al., 2009). These results indicate that low-income, urban residence accounts for increased physical symptoms, removing the important confound of ethnicity in studies on PTSD in lowincome women. These women had more clinic visits each year, and consistently reported worse appraisals of personal health. Bassuk et al. (2001) also reported that low-income women experience more bodily pain (controlling for demographics) and more chronic health conditions than the national average, as reported in the National Comorbidity Survey. (Kessler et al., 1995).

El-Bassel et al. (2011) found that emergency departments were the primary source of medical care for low-income, urban women, as high costs and inadequate insurance remained barriers to screening or seeking treatment elsewhere.

Although primary care was accessed by 86% of the low-income female victims of child abuse in their sample as the main source of health care, only one third had discussed abuse with their primary care provider (McCauley et al., 1997). This phenomenon is problematic, for if the

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patients do not decide to discuss abuse history with general practitioners or nurse practitioners, many of them will have no other input from health professionals should a psychiatric problem related to abuse arise. The importance of clinician views is further supported by the findings of Meredith et al. (2009), which showed that factors such as level of integration of primary care and mental health services affect recognition and management of PTSD by health care professionals. These results are especially pertinent, because the authors systematically sampled from a broad range of healthcare providers across two large states (New York and New Jersey), meaning that these issues remain outside of clinics in urban settings. Seeing as some forms of psychotherapy are available to lower-income clients through publically-funded insurance, it raises a huge issue that physical health providers may be missing crucial diagnoses that could lead to much-needed psychotherapy for PTSD.

III.

Conclusions and implications for intervention and treatment adaptation

The research literature on PTSD to date has not clearly established whether social causation or social selection play a larger role in the prevalence of PTSD in low-income women, although certain longitudinal studies have shown that risk factors such as child maltreatment and community violence precede the disorder and are correlates of low socioeconomic status. While factors associated with poverty often precede PTSD symptoms, the disorder itself can also promote a downward drift into poverty, such as by leading women to miss significant amounts of workdays due to physical and psychological health problems. Future implications for research in this field

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include employing longitudinal designs that ideally begin pre-trauma in high-risk populations, such as low-income women, and look at temporal ordering of risk factors, mediators, and moderators of PTSD. The study by Vranceanu et al.

(2007) illustrates the importance of distinguishing mediators, such as social support, for filling in crucial gaps in the research literature on how the disorder develops in low-income women.

Retrospective self-reports, while useful for determining some questions of temporal order, are flawed when considering PTSD because those suffering from the disorder may experience reduced capabilities to remember specific details of past trauma, and asking such questions raises the risk of re-traumatization (Ozer & Weiss, 2004, p. 171). Another option would be to test interventions using a partially experimental design. This approach would be able to answer questions of causality, without breaking obvious ethical guidelines by exposing research participants to trauma and extreme stress, and would provide the most insight into providing future effective interventions. Such interventions must also emphasize mediators, such as social support, that have shown some promise in accounting for the relationship between PTSD symptoms, so as to continue building the empirical support for such factors and better inform future interventions (Ozer & Weiss, 2004).

Given all the evidence presented in this paper for the multifaceted issues surrounding higher prevalence rates of PTSD in low-income women, how may we best intervene and adapt treatment so as to help this disadvantaged population?

A brief review of this evidence reveals that low-income women, by virtue of their environments, are exposed to greater amounts of trauma across a wide range of

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categories, particularly residents in low SES, violent neighborhoods, and are more likely to experience multiple traumas over the course of their lifetime. In addition, this population is more vulnerable for developing PTSD following adult trauma, with contributing factors being a higher prevalence of past adversity than national population samples, and a present lack of support resources. Low-income women are also more likely to have preexisting anxiety and/or depression, and co-morbid substance abuse, often linked to a history of child maltreatment. Finally, low-income women, especially in urban areas, encounter greater individual and institutional barriers to care, and receive inadequate screening and discussion about past abuse by primary care providers. The task of lowering prevalence rates in this disadvantaged population is indeed daunting, but clear recommendations of both micro-level efficacious treatments and macro-level policy and institutional interventions can be articulated.

One crucial first step for intervention will be to increase awareness among primary care providers regarding how women experience PTSD symptoms (e.g. through physical symptoms) and which high-risk populations are most likely to experience the disorder (e.g. urban, lowincome women with certain pre-existing psychiatric condition and/or past child maltreatment) (Ozer & Weiss, 2004).

Prevalence among individuals seeking healthcare services is triple the average rate, based on results by Gill et al. (2009), and therefore better screening in primary care would result in much higher rates of quality mental health treatment access for low-income women. Also, education around PTSD following a positive screen in primary care may assist mental health professionals in reducing the normalization of trauma in low SES

patients seen in Chung et al. (2012)’s study, and could bring down barriers to care associated with this pre-conceived notion. Dual-diagnoses are common, so screening for PTSD should also look for co-morbid anxiety, depression, and substance abuse, and vice versa (Bassuk et al., 1998). As substance abuse is the disorder especially co-morbid with PTSD, treatment manuals for PTSD should also consider incorporating elements of substance abuse treatment, as it is likely that a client in substance abuse rehabilitation will require PTSD treatment as well (Najavits et al., 1997).

Interventions must also act on factors that make low-income women more initially vulnerable to developing PTSD, such as a history of child maltreatment and, as the mediator of this association, social support. Policy makers should be constantly engaged in enforcing laws against child abuse, funding interventions that attempt to stop the abuse before it begins, and targeting highrisk populations for child maltreatment intervention. In those women who have already suffered the effects of child maltreatment, an intervention aimed at increasing levels of social support among interpersonal resources would be most beneficial, given the evidence presented in this review (Vranceanu et al., 2007).

Intervention must always keep in mind the importance of investigating mediators, because going directly after the mechanism driving an association has the potential for far larger effects than simply targeting a general risk factor for the disorder.

Although many current interventions for PTSD focus on individual-level treatment, there should be a greater amount of large-scale interventions that focus on primary prevention of the overall exposure of low-

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income women to adversity. Seeing as how this initial review discovered a large amount of risk for exposure to debilitating trauma amongst low-income women, often as a result of adverse social and political conditions, primary prevention of PTSD must involve some social and political changes in the community or nation. As Ozer & Weiss (2004) concluded, “perhaps more than any other psychological disorder, PTSD forces consideration of advocacy and political action as primary (universal) prevention tools” (p. 172). The authors noted that domestic violence and community violence, two environmental traumas common in the literature on PTSD in lowincome women, must be targeted by public health policy. Despite its failings, efforts like the MTO experiment did see some success in lowering female fear and increasing feelings of safety and security amongst mothers (De Souza et al., 2010).

Low-income women have unique needs for treatment of PTSD, given the greater chronic adversity they are likely to face. Bassuk et al. (2001) recommended a treatment plan that focuses on safety and sensitivity, and also a team approach coordinated by a case manager, so as to provide optimal support. In a metaanalysis on the efficacy of over 40 randomized control trials studying treatments for PTSD, Cloitre (2009) found the most evidence for efficacy for exposure and cognitive restructuring therapies, as compared to waitlist controls or treatment as usual (supportive counseling) for a variety of traumas discussed in this investigation, including childhood abuse and rape. One drawback noted was attrition, which could be as high as 30% in some of the studies included in the review and was linked to the avoidance nature of the disorder by the author’s estimation, Kubany et al. (2004) notably tested one

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treatment for PTSD in victims of assaultive violence, Cognitive Trauma Therapy for Battered Women (CTT-BW), which focuses on relieving guilt-related cognitions to extremely good results, with treatment gains maintained at a 3-month follow up (Kubany et al., 2004).

As mentioned by Meredith et al.

(1997), primary care providers should always have a level of integration with mental health teams to best facilitate access for low-income women with PTSD symptoms. Greater communication in general, whether between policy makers and researchers or mental health professionals and primary care providers, will go a long way in lowering the burden of posttraumatic stress disorder on lowincome women. This review has shown that the research is there for creating helpful solutions to the problem of PTSD in this high-risk population, but is not being fully utilized at the current moment.

Combining evidence-based interventions with reducing barriers to efficacious treatment will work to narrow the crippling disparity in prevalence of PTSD between low-income women and other populations, and assist in providing more equitable mental outcomes in our society.

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Bassuk, E.L., Buckner, J.C., Perloff, J.N., & Bassuk, S.S. (1998). Prevalence of mental health and substance abuse disorders among homeless and lowincome mothers. The American Journal of Psychiatry. 155: 1561-1564.

Bassuk, E.L., Dawson, R., Perloff, J.N., & Weinreb, L.F. (2001). Post-traumatic stress disorder in extremely poor women: implications for health care clinicians. Journal of American Medical Women’s Association. 56: 79-85.

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Davis, R. G., Ressler, K. J., Schwartz, A. C., Stephens, K. J., & Bradley, R. G. (2008). Treatment barriers for low-

income, urban African Americans with undiagnosed posttraumatic stress disorder. Journal of traumatic stress, 21(2), 218-222. doi: 10.1002/jts.20313.

De Souza Briggs, X., Popkin, S.J., & Goering, J. (2010). Moving to Opportunity: The story of an American experiment to fight ghetto poverty (pp. 86108). New York: Oxford University Press.

El-Bassel, N., Gilbert, L., Witte, S., Wu, E., & Chang, M. (2011). Intimate partner violence and HIV among drug-involved women: Contexts linking these two epidemicschallenges and implications for prevention and treatment. Substance use & misuse, 46(2-3), 295-306. doi:10.3109/10826084.2011.523296 Gill, J. M., Szanton, S., Taylor, T. J., Page, G. G., & Campbell, J. C. (2009). Medical conditions and symptoms associated with posttraumatic stress disorder in lowincome urban women. Journal of Women's Health, 18(2), 261-267. doi:10.1089/jwh.2008.0914 Gordon, M. T., & Riger, S. (1991). The female fear: The social cost of rape.

University of Illinois Press.

Green, B. L. (2006). Defining trauma: terminology and generic stressor dimensions1. Journal of Applied Social Psychology, 20(20), 1632-1642. doi:10.1111/j.15591816.1990.tb01498.x Kessler, R.C., Sonnega, A., Bromet, E., Hughes, M., & Nelson, C.B. (1995).

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Kessler, R. C., Chiu, W.C., Demler, O., & Walters, E.E. (2005). Prevalence,

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severity, and comorbidity of 12month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62: 617-627.

Kingsley, G. T., & Pettit, K. L. (2008).

Have MTO families lost access to opportunity neighborhoods over time? Three-City Study of Moving to Opportunity Brief, 2.

Kubany, E. S., Hill, E. E., Owens, J. A., Iannce-Spencer, C., McCaig, M. A., Tremayne, K. J., & Williams, P. L. (2004). Cognitive trauma therapy for battered women with PTSD (CTTBW). Journal of Consulting and Clinical Psychology; Journal of Consulting and Clinical Psychology, 72(1), 3-18. doi:10.1037/0022-006X.72.1.3 McCauley, J., Kern, D. E., Kolodner, K., Dill, L., Schroeder, A. F., DeChant, H. K., & Bass, E. B. (1997). Clinical characteristics of women with a history of childhood abuse. JAMA: the journal of the American Medical Association, 277(17), 1362-1368. doi:10.1001/jama.1997.0354041004 0028 Menard, S., & Huizinga, D. (2001).

Repeat victimization in a high-risk neighborhood sample of adolescents.

Youth & Society, 32(4), 447-472. doi: 10.1177/0044118X01032004003 Meredith, L. S., Eisenman, D. P., Green, B. L., Basurto-DĂĄvila, R., Cassells, A., & Tobin, J. (2009).

System factors affect the recognition and management of post-traumatic stress disorder by primary care clinicians.

Medical care, 47(6), 686. doi: http://dx.doi.org/10.1097%2FML R.0b013e318190db5d Najavits, L. M., Weiss, R. D., & Shaw, S. R. (1997). The link between substance abuse and posttraumatic

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stress disorder in women. The American journal on addictions, 6(4), 273283. doi:10.1111/j.15210391.1997.tb00408.x Ozer, E. J., Best, S. R., Lipsey, T. L., & Weiss, D. S. (2003). Predictors of posttraumatic stress disorder and symptoms in adults: a meta-analysis.

Psychological bulletin, 129(1), 52. doi: 10.1037/0033-2909.129.1.52 Ozer, E. J., & Weiss, D. S. (2004). Who develops posttraumatic stress disorder?

Current Directions in Psychological Science, 13(4), 169-172. doi:10.1111/j.09637214.2004.00300.x Sartor, C. E., McCutcheon, V. V., Pommer, N. E., Nelson, E. C., Grant, J. D., Duncan, A. E., & Heath, A. C. (2011). Common genetic and environmental contributions to post-traumatic stress disorder and alcohol dependence in young women. Psychological medicine, 41(07),1497-1505. doi:10.1017/S0033291710002072 Schumm, J. A., Hobfoll, S. E., & Keogh, N. J. (2004). Revictimization and interpersonal resource loss predicts PTSD among women in substance-use treatment. Journal of Traumatic Stress, 17(2), 173-181. doi:10.1023/B:JOTS.0000022624.5 3181.21 Stein, M. B., Jang, K. L., Taylor, S., Vernon, P. A., & Livesley, W. J. (2002). Genetic and environmental influences on trauma exposure and posttraumatic stress disorder symptoms: a twin study. American Journal of Psychiatry, 159(10), 16751681. doi:10.1176/appi.ajp.159.10.1675 Vogel, L. C., & Marshall, L. L. (2001).

PTSD symptoms and partner abuse:

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Low income women at risk.

Journal of Traumatic Stress, 14(3), 569584. doi:10.1023/A:1011116824613 Vranceanu, A. M., Hobfoll, S. E., & Johnson, R. J. (2007). Child multitype maltreatment and associated depression and PTSD symptoms: The role of social support and stress.

Child Abuse & Neglect, 31(1), 7184. doi:10.1016/j.chiabu.2006.04.010 Walsh, K., Danielson, C. K., McCauley, J. L., Saunders, B. E., Kilpatrick, D. G., & Resnick, H. S. (2012).

National prevalence of posttraumatic stress disorder among sexually revictimized adolescent, college, and adult householdresiding women.

Archives of General Psychiatry, 69(9), 935-942. doi:10.1001/archgenpsychiatry.201 2.132 Â

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The Effect of Coaching on Children’s Lie-Telling to Conceal Another’s Transgression Stephanie Cirnu Supervisors: Sarah Yachison, M.A. & Victoria Talwar, Ph.D.

Abstract This study investigated the effects of age and different levels of coaching on children’s lietelling behavior to conceal another person’s transgression. Children aged four to seven (N=137) witnessed a confederate break a ball and were coached to conceal the transgression in preparation for an interview about the event. Children in the light coaching condition were given two practice questions about the lie, and those in the heavy coaching condition were given a cover story and three practice questions. All children were then first asked one open-ended question, followed by four close-ended questions about the event by a second research assistant. Results were analyzed using chi-square tests, with age and coaching condition as the independent variables. Age was not found to have a significant effect on any measures of lie-telling behavior. Coaching condition placement significantly affected children’s attempt to lie during the interview in response to the open-ended question, specifically the use of the cover story. Coaching condition did not significantly affect children’s successful maintenance of the lie in response to the follow-up close-ended questions, as most children semantically leaked the truth about the transgression. Legal implications are discussed.

Children’s lie-telling behavior has been extensively studied both in the context of social and cognitive development (Gombos, 2006; Talwar & Lee, 2008), as well as the legal and educational systems where children are often called upon to give their version of alleged events (Lyon, Malloy, Quas, & Talwar, 2008; Quas, Davis, Goodman, & Myers, 2007; Talwar, Lee, Bala, & Lindsay, 2004; Warren, Dodd, Raynor, & Peterson, 2012). Children are able to lie to conceal their own transgressions as young as preschool age; and their ability to successfully lie, or consistently maintain an

initial false statement, increases with age (Talwar & Lee, 2008). Children have also been shown to be capable of concealing an adult’s transgression, carrying forensic as well as developmental implications (Lyon et al., 2008).

Many social factors have been shown to influence children’s lie-telling behavior, such as taking an oath, being threatened, or being coached (Lyon et al., 2008; Quas et al., 2007; Talwar et al., 2004; Warren et al., 2012). Coaching, or the act of teaching a child to lie, is of particular interest, as children may be coached to conceal another’s transgression

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in a variety of legal settings, most often in court testimonies pertaining to cases of abuse or family violence (Bala, Lee, Lindsay, & Talwar, 2010; Talwar et al., 2004). Children are often coached by parents or lawyers prior to providing their testimony in such cases (Warren et al., 2012). A study of deception detection by Warren et al. (2012) found that 74% of lies about a fictitious injury coached by children’s parents were judged as true by lay judges, which could lead to very grave consequences in genuine court situations.

It is therefore important to identify the specific conditions under which adults are able to influence lie-telling in children, as these conditions pose a potential risk in trusting children who may be lying to conceal another’s wrongdoing. A better understanding of the effects of coaching on children’s lie-telling behavior is vital for assessing the value of a child’s testimony of an alleged event and the potential consequences of relying on a child’s statement in a legal setting (Talwar et al., 2004; Warren et al., 2012).

The present study seeks to investigate the influence of different degrees of coaching on four to seven year old children’s lie-telling to conceal another person’s transgression. As children’s lietelling ability has been correlated with the stage of their cognitive development (Carlson, Moses, Hix, 1998; Gombos, 2006; Talwar & Lee, 2008), the present study also investigates the effect of age on lie-telling behavior to ensure that participants’ age did not constrain their ability to lie. Though this line of research has serious forensic implications, we could not ethically coach children to lie about alleged abuse for the purpose of this experiment; instead, children witnessed a research assistant break a toy, and then were coached to lie about the transgression.

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The development of children’s lie-telling behavior Research has shown that children are able to lie and intentionally deceive others as early as three years of age, as evidenced by the temptation resistance paradigm in which children peeked at an object they were forbidden to look at and then spontaneously lied to conceal their transgression (Talwar et al., 2004; Talwar & Lee, 2008). While lie-telling itself has been shown to emerge in preschool, the ability to successfully lie, defined as producing an initial false statement and consistently maintaining the lie in further statements, develops in mid-childhood (Talwar & Lee, 2008). Talwar and Lee’s (2008) study used the temptation resistance paradigm to demonstrate that semantic leakage control - the ability to maintain consistency between statements and give plausible explanations pertaining to the initial lie - becomes more developed as children get older, making them more successful liars as they age.

As the cognitive factors that play a role in lie-telling ability develop, children’s lie-telling ability becomes more advanced (Gombos, 2006; Talwar & Lee, 2008).

Older children, having developed the cognitive processes required to produce and maintain lies such as theory of mind and executive functioning, out-lie younger children generally lacking these cognitive processes (Gombos, 2006; Talwar & Lee, 2008). Theory of mind is defined by Gombos (2006) as the ability to understand that others have beliefs, desires, and intentions different from one’s own. In the context of lying, it is only when a child possesses a first-order belief understanding of others’ minds, or the ability to infer another’s thoughts and intentions, that they can implant a false belief in the mind of another, a skill found in children as young as three years of age

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(Talwar & Lee, 2008). Theory of mind is also implicated in the successful maintenance of a lie when questioned, as children must posses a second-order belief understanding of others’ minds, or the ability to infer that a given person can infer the thoughts of another, to correctly answer repeated follow-up questions of the initial lie (Talwar & Lee, 2008). This has been shown to emerge at around six years of age (Talwar & Lee, 2008).

Also crucial in the development of children’s successful lying behavior is executive functioning, a cognitive system which includes the processes of working memory and inhibitory control (Gombos, 2006). Working memory is the cognitive process that holds and processes information, and inhibitory control is the process whereby interfering thoughts or actions, such as the truth about the transgression, are suppressed (Talwar & Lee, 2008). Both working memory and inhibition are shown to improve as children age (Carlson et al., 1998; Gombos, 2006; Talwar & Lee, 2008). A study by Carlson et al. (1998) found that inhibitory control is crucial to deception and false beliefs, and improves with age, as four-year-olds perform significantly better on deceptive pointing tasks than threeyear-olds.

Children’s lie-telling to conceal another’s transgression While we are aware that children are able to lie to conceal their own transgressions (Talwar & Lee, 2008), it is of interest to study their lie-telling behavior in relation to concealing another’s transgression, as false testimonies may result in grave legal implications in cases such as child abuse or custody battles (Bala et al., 2010; Talwar et al., 2004; Warren et al., 2012).

Coaching is an important form of manipulation because it has been found to

influence children to engage in lie-telling (Lyon et al., 2008; Quas et al., 2007; Talwar et al., 2004; Warren et al., 2012).

Coaching differs from the spontaneous lies that children generate in that they are being deliberately influenced by another person to be dishonest (Talwar & Lee, 2008).

There are different degrees to which children can be coached to lie, with possible differential effects on lying behavior. Quas et al. (2007) found that most children who were coached to conceal that an adult touched them had in fact lied, and they were more consistent in their responses than non-coached truthtelling children in answering repeated follow-up questions about the touching, thereby rendering their lies more convincing to others. More extensive coaching may include increased rehearsal of a false statement leading to a better memory of the coached lie, which via consistent responses during questioning about the transgression, leads to more successful lies (Quas et al., 2007; Talwar & Lee, 2008). The children in the Quas et al.

(2007) study were coached to conceal an adult’s touching of them using two practice questions, and thus rehearsed their lie and may have relied on their verbatim memory to answer lie-related questions, resulting in increased consistency in their responses compared to those children who were not coached to lie.

In a study of coaching by Talwar et al. (2004), children’s parents broke a puppet, but were not given directions on how to coach their child to conceal the event. Parents did not provide their child with a cover story, but only coached them until they had received confirmation from the child that they would conceal their wrongdoing. This study found that two thirds of the participants eventually

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semantically leaked the truth, and many did not lie to conceal their parent’s breaking of a puppet as they had been coached to do. However, a study by Lyon et al. (2008) found that coaching the use of a cover story to conceal that children had played with a toy had an effect on children’s successful lie-telling, with most children maintaining their lie over a series of open and close-ended questions about the event.

Several studies have investigated the different factors that influence whether children will comply with adults’ demands, such as the degree or extent to which the child is coached to lie or the child’s developmental stage (Lyon et al., 2008; Quas et al., 2007; Talwar et al., 2004; Warren et al., 2012); however, these studies each used different measures of coaching and interviewing to investigate children’s lie-telling behavior. The present study aims to standardize both these measures of coaching by including two conditions that differ in degrees of coaching (light and heavy), and methods of interviewing, by including both openended and close-ended questions about the transgression.

In the present study, children aged four to seven and a research assistant played with toys from a chest, and children witnessed the research assistant “accidentally” break a ball, similar to the paradigm used by Talwar et al. (2004).

Children were assigned to one of two coaching conditions, light or heavy coaching, and they were asked by the research assistant to keep two secrets when interviewed by another experimenter: 1) that they played with a toy chest, and 2) that the research assistant broke a yellow ball. The interview consisted of one openended free recall question, followed by four close-ended yes/no questions about the research assistant’s transgression, in

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order to examine if children maintained response consistency across conditions.

The two independent variables measured in this study were how the degree of coaching, operationalized here as heavy and light coaching, and the child’s age may influence the dependent variable of their lie-telling behavior. We expected that the effect of the coaching on children’s deceptive behavior would depend on specific aspects of the coaching, such as the number and type of practice questions used to instigate lies and the presence of a cover story. Based on existing evidence (Lyon et al., 2008; Quas et al., 2007), we hypothesized that, as the degree of coaching increased, children would be more likely to initially lie to conceal the research assistant’s transgression, as well as maintain their initial false statement in follow-up questioning without semantic leakage of the truth. We also hypothesized that there would be an effect of age on children’s ability to successfully lie about the transgression, with age divided between younger (aged four to five) and older (aged six to seven) children, based on existing research about children’s lie-telling abilities as a function of age (Carlson et al., 1998; Gombos, 2006; Talwar & Lee, 2008).

Method Participants Participants consisted of 137 children (M=67, F=70), aged four through seven, who were recruited from public elementary school visits, an advertisement in a free local parent newspaper, and from the McGill Infant Research Group participant database. Parents were given a detailed description of the experiment by telephone or email before agreeing to have their child participate in the study. Both parental consent and child assent were obtained prior to participation. Children

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were given a small toy gift worth approximately two dollars for their time and effort, regardless of whether they actually completed the study.

Children were randomly assigned to one of two coaching conditions, each of which lasted approximately thirty minutes.

There were 73 children in the heavy coaching condition, and 64 in the light coaching condition. Testing took place at either McGill University or the child’s school, and children participated individually.

Materials A Band-Aid brand box filled with wax crayons and a Crayola crayon box filled with a stuffed toy duck were used to assess children’s theory of mind. A toy chest containing toys, including a yellow ping-pong ball, was used for the transgression that the research assistant coached children to conceal. A coin was used in a guessing game. One of three books, George Washington and the Cherry Tree, The Boy Who Cried Wolf, or The Tortoise and the Hare, was read to the children for investigative purposes independent of the current research. A hidden video camera was placed on the wall to record children’s responses to follow-up questions about the confederate’s transgression.

Procedure One child and two research assistants participated in each session.

Each child was given a False Belief task assessing theory of mind, for purposes other than the current research. Upon completion of this task, the first research assistant (RA1) informed the other one, a confederate, that she had to go get an item she had forgotten upstairs, and suggested that the confederate and child play a guessing game with a coin while waiting for her to return. The confederate and the child were left alone in the room to play

the guessing game, in which the research assistant quickly passed a coin from one hand to the other and then asked the child to guess which hand the coin was in. After three rounds of the guessing game, the confederate directed the child’s attention to the toy chest nearby, opened it, and encouraged the child to play with the various stuffed animals and figures. The confederate then took out a bag of green balls from the toy chest and drew attention to the one yellow ball in the bag. She removed it from the bag and bounced it up and down on the table behind the toy chest, and then allowed it to fall to the floor while the child was playing with the other toys. As she moved to pick it up, the confederate “accidentally” crushed the ball with her foot. She then brought the broken ball to the child’s attention and asked the child to help her put the toys back in the box. The confederate then asked the child to keep secret both that they had played with the toy chest and that the confederate had broken the ball.

The confederate stated that she would get into trouble and potentially lose her job if the child mentioned (during the interview) either secret about what they had done while RA1 was absent.

In the light coaching condition, children were encouraged by the confederate to keep the same two secrets during RA1’s follow-up questioning about the event. To ensure the children understood what the confederate was asking of them, they were given two practice questions: first, the open-ended “If RA1 comes in and asks ‘What did you do while I was gone?’ what are you going to say?” and, second, the close-ended “If RA1 comes in and asks ‘Did you play with the toy box while I was gone?’ what are you going to say?” Children were given positive feedback if they lied in response to

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the practice questions, and were reminded to keep the secrets.

In the heavy coaching condition, children were also encouraged to withhold the same information about playing with the toy chest and the broken ball.

Children were also given a cover story to use in the interview, which told how they played a guessing game with the coin for the entire amount of time they were with the confederate. They were given the same two practice questions as in the light coaching condition, as well as an additional close-ended practice question, “If RA1 asks ‘Did you play with the toys?’ what are you going to say?” They were also given positive feedback if they successfully lied in response to the practice questions, and were reminded to keep the secrets.

RA1 then returned and the confederate left the room. RA1 read one of three stories to the child: George Washington and the Cherry Tree, The Boy who Cried Wolf, or The Tortoise and the Hare, for purposes other than the current research.

RA1 then interviewed the child with one open-ended question (“What happened in here while I was gone?”), followed by four close-ended questions (including “Did you and [the confederate] play with the toy box on the floor?”, “When you took the toys out, did you and [the confederate] play with them?” and “Did you find the broken toy when you opened the toy box?”)

Immediately after the interview, the confederate returned and “confessed” to the transgression in front of the child, adding that the breaking of the ball was not the child’s fault. RA1 thanked the confederate for telling the truth and said they could now work on fixing the broken ball, in order to demonstrate that no harm resulted from the incident. The child was

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then instructed to pick out a prize for participating in the study. Parents were given a debriefing document to bring home, which contained the laboratory’s contact information in the event that they had any questions or concerns pertaining to the study.

Results Effects of age and coaching on lie-telling Chi-square tests were conducted to examine differences in children’s lie-telling behavior during the interview about the transgression depending on age and coaching condition. Although a trend suggested that 86% of older children (n=61) were more deceitful in their responses than 79% of younger children (n=52), age did not show a significant effect on the type of lie (no lie, unsuccessful lie attempt, or successful lie) children executed during the interview; χ² (2, N=137) = 2.66, ns. Coaching condition also did not have a significant effect on the type of lie executed by children during the interview; χ² (2, N=137) = 5.59, ns.

Lie-telling in response to open-ended questioning A chi-square test revealed a significant effect of coaching condition on children’s lie-telling behavior in response to open-ended questioning; χ² (1, N=137) = 4.65, p = .031 (see Figure 1). Coaching condition affected whether children attempted to lie, with 89% of children in the heavy coaching condition (n=65) and 75% of children in the light coaching condition (n=48) attempting to lie in response to open-ended questioning. The effect of age on whether children attempted to lie was not found to be significant; χ² (1, N=137) = 1.2, ns.

Use of the guessing game cover story The coaching condition had a significant effect on the use of a cover story in response to open-ended questioning; χ² (1, N=131) = 4.49, p = .034 (see Figure 2).

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Figure 1. Lie-telling in response to openended questioning.

Figure 2. Use of the guessing game cover story.

Figure 3. Successful maintenance of the lie.

Eighty-eight percent of heavily coached children were more likely to mention the guessing game cover story, which was explicitly provided to them, during free recall of the event compared to 73% of children in the light coaching condition.

Age was not found to have an effect on children’s tendency to mention the guessing game cover story; χ² (1, N=131) = 1.78, ns.

Successful maintenance of the lie The effect of coaching condition on successful maintenance of the lie was not significant, although there was a trend in the predicted direction, with 62% of children in the heavy coaching condition (n=45) and 45% of children in the light coaching condition (n=29) telling successful lies; χ² (1, N=137) = 3.66, ns. (see Figure 3). Although the effect of age was not significant on children’s abilities to successfully lie, a trend suggested that 60% of older children (n=43) were more likely to do so compared to 47% of younger children (n=31); χ² (1, N=137) = 2.56, ns.

Lie-telling in response to close-ended questioning Neither coaching condition, χ² (1, N=137) = .088, ns., nor age, χ² (1, N=137) = .702, ns., showed significant effects on children’s lie-telling in response to closeended questioning.

Responses to specific close-ended questions Children’s responses to one of the four close-ended follow-up questions was significantly influenced by age, χ² (2, N=134) = 7.18, p = .028. Almost half (41%) of the older children (n=28) were more likely to maintain their lie in response to the question “Did you find the broken toy when you opened up the chest?” compared to 23% of younger children (n=15). Age did not significantly predict children’s responses to any of the other three open-ended questions.

Coaching condition did not affect

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children’s responses to any of the four specific close-ended questions.

Discussion The results reported in this study did not fully support the hypotheses that there would be a significant effect of both age and coaching condition on children’s lie-telling behavior to conceal another’s transgression. Instead, the results revealed a significant effect of coaching condition on children’s lie-telling behavior during the interview when the types of lies told (no lie, unsuccessful lie attempt, and successful lie) were examined. Significantly more children in the heavy coaching condition lied in response to the openended question, “What happened in here while I was gone?” This finding suggests that elements of the heavy coaching condition, such as the additional practice question and cover story, influenced children’s lie-telling in response to the open-ended free recall question, regardless of their success in maintaining the lie during the following close-ended questions.

Further examination of the differences in children’s lie-telling in response to the open-ended question, “What did you do in here while I was gone?” demonstrated that coaching condition significantly affected the use of the guessing game cover story. While children in both conditions played the guessing game prior to playing with the toy chest and witnessing the confederate break the ball, the guessing game cover story was only provided to children in the heavy coaching condition. All but two (88%) of the children in the heavy coaching condition made use of the guessing game cover story during free recall of the event, compared with 73% of children in the light coaching condition (see Figure 2). This finding suggests that children in the heavy coaching condition

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were very receptive to being provided with a cover story to use during the interview, which is consistent with the finding presented by Lyon et al. (2008) that coaching children in the use of a cover story influenced them to initially lie when questioned about a research assistant’s transgression.

Contrary to our hypothesis, children’s maintenance of the attempted lie in follow-up close-ended questioning (and thus their success at lying) when dependent on coaching condition, did not reach significance, although there was a trend as 62% of children in the heavy coaching condition were more successful liars than 45% of those in the light coaching condition (see Figure 3). Thus, despite the additional practice question and cover story given to children in the heavy coaching condition, more extensive coaching did not influence children to ultimately be more successful at lying, for children in both conditions semantically leaked the truth during the follow-up questioning. This is consistent with the Talwar et al. (2004) results, where two thirds of children coached to lie eventually semantically leaked the truth about their parent’s transgression. This finding, however, is inconsistent with results from Lyon et al. (2008) and Quas et al. (2007), where coached children were able to maintain their lies over a series of openended and close-ended questions.

Results of this study revealed that while there was an overall trend for the older group to be more successful at lying than the younger group, age did not have a significant effect on any of the measures of lying. This finding eliminates the possibility that some of the children did not lie simply because they were incapable of lying, despite the research assistant’s efforts to coach them.

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In sum, coaching condition influenced children’s use of a cover story and attempt to lie, but not their successful maintenance of the lie, as shown by semantic leakage of the truth in response to the follow-up close-ended questions across coaching conditions. The finding that more extensive coaching was not found to ultimately influence children’s successful lying may be seen as encouraging and gives hope when extended to legal situations in which children may be asked to conceal another’s transgression and fail to do so.

However, that the heavy coaching condition significantly influenced children’s attempted lies in response to open-ended questions raises serious concerns about the effects of coaching children to use a cover story.

Children in the light coaching condition made fewer attempts to lie and made less use of the guessing game cover story, which was the only activity done in the absence of RA1 that was not to be kept secret and was expected to be salient in children’s minds at the time of the interview. Inability to independently think of a proper cover story when asked an open-ended question about the transgression may account for why a smaller proportion of children in the light coaching condition made use of the guessing game cover story or attempted to lie at all. This finding suggests that if children are not provided with and/or cannot think of a cover story concerning another’s transgression during a forensic interview, they may be more likely to be truthful. It would be of interest to examine, in future studies, the ways in which one can determine whether or not a child has been provided with a cover story, as well as how to diminish a cover story’s effect on children’s lie-telling.

An important limitation to the experiment was the lack of a control group with which to compare the effects of coaching reported here. A no-coaching condition, whereby the research assistant would ask the children to keep their transgression a secret but provide no guidance for lying, could allow for a greater understanding of the ways in which adults may influence lie-telling in children. This third condition could reveal lie-telling behavior when children are left to their own devices, as well as provide insight into the possible mechanisms of how children’s age limits their lie-telling ability when they have not at all been coached.

Another potential limitation of the research is that the age range of participants (four to seven years old) may have been too limited to assess the effect of age on lie-telling behavior. It would be of interest in future studies to study children’s ability to lie for others in a sample of participants with a larger age range.

The results pertaining to children’s responses to open-ended and close-ended questions highlight the need to ask children multiple questions about a transgression in legal settings, as semantic leakage of the truth may emerge following the initial lie. This is especially pertinent for children who have received the coaching equivalent to the light coaching condition prior to giving their testimony.

Interviews about the transgression should include open-ended questioning followed by close-ended questioning, in order to allow for potential semantic leakage of the truth.

While these results suggest that children are susceptible to lie when coached with a cover story, neither of the coaching conditions significantly prepared children to falsely deny the transgression during the close-ended questioning. This

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suggests that the difference in number of practice questions of the lie was not large enough to have a significant effect on their responses to the close-ended questions and, therefore, the success of their lies. It would be of interest in future studies of liecoaching to include a larger difference in the number of practice questions between conditions in order to examine the effect of practice questions, and thus the rehearsal of lies, on children’s lie-telling.

References Bala, N., Lee, K., Lindsay, R.C.L., & Talwar, V. (2010). The competency of children to testify: Psychological research informing Canadian law reform. International Journal of Children’s Rights, 18, 53-77.

Carlson, S., Moses, L., & Hix, H. (1998).

The role of inhibitory processes in young children’s difficulties with deception and false belief. Child Development, 69(3), 672-691.

Gombos, V. (2006). The cognition of deception: The role of executive processes in producing lies. Genetic, Social, and General Psychology Monographs, 132(3), 197-214.

Lyon, T., Malloy, L., Quas, J., & Talwar, V. (2008).

Coaching, truth induction, and young maltreated children’s false allegations and false denials. Child Development, 79(4), 914929.

Talwar, V. & Lee, K. (2008). Social and cognitive correlates of children’s lying behavior. Child Development, 79(4), 866-881.

Talwar, V., Lee, K., Bala, N., & Lindsay, R. (2004). Children’s lie-telling to conceal a parent’s transgression: Legal implications.

Law and Human Behavior, 28(4), 411435.

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Quas, K., Davis, E., Goodman, G., & Myers, J. (2007).

Repeated questions, deception, and children’s true and false reports of body touch. Child Maltreatment, 12, 60-67.

Warren, K., Dodd, E., Raynor, G., & Peterson, C. (2012). Detecting children’s lies: Comparing true accounts about highly stressful injuries with unprepared, prepared, and coached lies.

Behavioral Sciences and the Law, 30, 329-341.

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The Influence of Contingency and Cue Configuration on Human Causal Learning Alexandra Tighe Abstract This study investigated the effect of contingency and cue configuration manipulations on human causal learning about a cue-outcome relationship. Subjects participated in a computer task in which they were asked to take on the role of an astronaut visiting different planets and judge the relationship between environmental symbols (cues) and life forms (outcome). Based on previous research on associative models of causal learning, it was expected that participants’ judgements of target cue X would support the Contrast Hypothesis (Darredeau, 2009) and Pearce’s Configural Theory (Pearce, 1987/1994).

Consistent with these hypotheses, blocking of cue X occurred in the 0.5/1.0 (ΔPX/ΔPA) contingency condition and enhancement to cue X occurred in the 0.5/-1.0 (ΔPX/ΔPA) contingency condition. The discussion addresses the implication of these findings in supporting associative models as an explanation of contingency and cue configuration manipulations in human causal learning.

Keywords: contingency, cue-competition, blocking, enhancement, cue configuration

As they explore their environment, people naturally learn about relationships between cause and effect. In the field of learning psychology, this important process is studied as causal (i.e. contingency) judgements made between a cue and an outcome. Parallels may be drawn between human causal learning and classic Pavlovian conditioning studied in animals. In contingency judgements, the relationship drawn between a cause and effect may be understood in the Pavlovian terms of an association between a conditioned stimulus and an unconditioned stimulus. Similar to how a conditioned stimulus may be present or absent (i.e. CS+, CS-), causes may vary in

the degree to which they predict the outcome. A cause may be generative (i.e. positive contingency, increases probability of outcome), preventive (i.e. negative contingency, decreases probability of outcome), or have zero contingency (i.e. no relation to the outcome). There are four possible events that may occur on each trial: cue paired with outcome, cue without outcome, outcome without cue, or neither cue nor outcome present.

Analyzing the frequency of these events and calculating conditional probabilities may determine the contingency between a cause/cue and a single outcome. The normative statistic, ΔP, represents the difference between the probability of the

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cue given the presence of the outcome and the probability of the cue given the absence of the outcome (Darredeau, 2009). An understanding of the predictive nature of a cause (generative, preventive, or zero) and the contingency between the cause and the outcome (ΔP) forms the foundation of basic human causal judgements and the experimental investigation of these processes.

For a given outcome, there is often more than one causal factor present. The study of human learning has revealed that humans tend to base causal judgements on the relative validity of cues, rather than the target cue’s actual degree of prediction of the outcome (Baker, 1993).

Thus, multiple cues are placed in direct competition with each other for association with the outcome. There are two apparently contradictory consequences of cue competition that have been consistently observed in human causal learning: blocking and enhancement. The first of these effects, blocking (Kamin, 1969), describes how strong cues reduce judgements of weaker cues. There will be poor learning of a target cue if one has previously learned that a strong alternative cue predicts the outcome. For example, the pairing of cue X (ΔPX = 0.5) and cue A (ΔPA = 1.0) will result in a reduced judgement of cue X (e.g. reported ΔP of 0). Blocking can be explained by the Rescorla-Wagner model (1972), an arithmetic formula that measures the change in associative strength (i.e. predictive value of a target cue) based on the difference between the outcome and expectations formed in prior learning. The equation ΔV = αβ (λ – ΣV) may be conceptualized as a formalization of the notion of surprise. With this model, blocking may be understood as the result of less associative strength to blocked target cue due to previous training with a

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strong alternative cue. The model predicts that ratings of a moderate target will be rendered a redundant predictor of the outcome, and thus blocked, when paired with a stronger alternative cue, compared to when the moderate target is presented alone.

The second consequence of cuecompetition that has been observed in human causal judgements and described by the Contrast Hypothesis (Darredeau, 2009) is enhancement. This effect occurs when a strong cue of opposite polarity enhances, rather than reduces, judgements of a moderate cue. For example, if a moderate target cue (ΔPX = 0.5) is paired with strong negative alternative cue (ΔPA = -1.0), judgement of cue X will be perceived as greater than 0.5, approaching 1.0. This phenomenon is observed for cues of opposite polarity (i.e. positive and negative).

The cues appear to be “contrasted” as the causal estimates are shifted in the opposite direction of the alternative cue. Moreover, the Contrast Hypothesis provides an explanation of blocking which differs from the standard Rescorla-Wagner model.

Darredeau’s model states that if a strong cue is paired with a moderate target cue of the same polarity (i.e. both positive or negative), the target cue will be judged as a weaker predictor (e.g. reported ΔP of 0 or -0.2).

Thus, blocking occurs due to a contrast in cue strength and polarity, rather than a reduction in associative strength. The Contrast Hypothesis is a unique model as it states that enhancement, rather than blocking, will occur when a moderate target cue is paired with a strong preventive alternative cue (ΔPA= -1.0).

Additionally, whereas the RescorlaWagner Model states that blocking occurs to zero, the Contrast Hypothesis allows for blocking to occur past zero (e.g. ΔP of a blocked cue may be -0.5). Evidently, there

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Figure 1. Two graph representations of predictions of hypothetical mean ratings of cue X based on the Rescorla-Wagner model (left) and Contrast Hypothesis model (right). The former predicts blocking to cue X on both 0.5/1.0 and 0.5/-1.0 (ΔPX/ΔPA) conditions, and the latter predicts blocking to cue X on 0.5/1.0 and enhancement to cue X on 0.5/-1.0.

Note. Contrast Hypothesis prediction displayed above shows blocking to zero; however, the model predicts that blocking may occur past zero. (Lober, 2012)

are two theoretical models, which differ in the predictions and explanations of the effects of cue competition. For a graphical comparison of these models refer to Figure 1.

There are a number of other factors, aside from contingency (i.e. ΔP), which play a role in human causal learning. An important factor is cue configuration, the similarity or dissimilarity of cue elements.

With multiple cues competing for association with the outcome, it is important that one is able to appropriately discriminate and generalize between multiple cues.

Pearce’s Configural theory (1987, 1994) posits that the presence of common elements (i.e. similar cues) facilitates generalization and a lack of common elements between cues (i.e. no similar cues) facilitates discrimination. Therefore, the theory predicts that the amount of generalization or discrimination between cues will vary to the extent to which cues share common elements (Bouton, 2007).

Moreover, this notion may be extended to predict that when blocking occurs, there

would be a greater reduction in the judgement of the target cue as a result of increased discrimination between cue elements.

Research on blocking and contrast has been largely descriptive and much is known about its empirical foundation. It is now crucial to develop a theoretical understanding of this phenomenon. Why does blocking occur in some situations, and enhancement occur in others, and how can this be explained by associative models? There is a need to go beyond mere description of the effects of cue competition (blocking and enhancement) in order to understand why and how these effects occur. The current study aims to investigate the effect of manipulation of contingency and cue configuration on causal learning. Examining how people draw causal judgements between cues and a single outcome in a computer task assessed this.

The cue-outcome contingencies and degree of shared elements between cues were manipulated.

Effects of blocking and enhancement were measured by reported ratings of cue X

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relative to alternative cue A based on the contingency manipulations (ΔPX/ΔPA).

Overall, the goal of the study was to better understand the effect of contingency and cue configuration on the judgement of a target cue in human causal learning.

Method Participants Sixty-two undergraduate students at McGill University (from the PSYC 301 course) participated in exchange for course credit.

A written consent form was provided to all participants upon arrival.

A debrief was provided after the experiment in the form of class tutorials.

Apparatus The study used a computerized causal learning task, “Alien Life,” programmed in RealBasic. The task was presented to participants on Macintosh desktop computers with colour monitors in a quiet laboratory setting.

Procedure Participants were seated at a computer and asked to read a set of instructions (see Appendix).

The experimenter left the room before the participants began the task. The scenario involved participants taking on the role of an astronaut investigating six planets to learn about the relationship between environmental symbols (cues) and life forms (outcome). The task was a within subjects design, which consisted of three counterbalanced blocks of 16 trials per planet.

The independent variables manipulated included contingency of the target cue X and alternative cue A, ΔPX/ΔPA (0.5/0, 0.5/1.0, 0.5/-1.0) and cue configuration (common elements shared between cues X and A, no common elements between cues X and A).

The dependent variable measured was participant reports of the ratings of cue X based on a scale that ranged from -100

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(fully preventive cue) to +100 (fully generative cue). The mean ratings of cue X indicate the judgment of the relationship between the target cue and outcome.

The computer task consisted of two phases: a training phase and a test phase. The training phase presented 6 different contingency and cue configuration manipulation conditions, represented by 6 individual planets. In these trails, participants learned about the cue-outcome relationships.

Each environment presented a distinct set of 8 symbols with the question, “Do you think a life form will be detected?”

The question was answered by choosing either yes or no. Feedback (correct/incorrect) was provided after each trial. Test phases followed each of the training phases for the 6 planets. In the test phases, groups of environmental symbols (cues) were presented and participants were asked to indicate the likelihood that the cues predicted the presence of a life form. This was reported based on a rating scale ranging from fully preventive cause (-100) to fully generative cause (+100). The use of a scale that ranges from negative to positive contingencies has been shown to provide more accurate representation of cue competition effects (Hanley, 2005).

The task took approximately 30 minutes to complete. The experimental design and mean ratings of cue X are presented in Table 1.

Results A two factor repeated measures analysis of variance [Contingency ΔPX /ΔPA (0.5/0, 0.5/1.0, 0.5/-1.0) x Elements (no common elements, common elements); ANOVA] was performed to assess differences in the mean ratings of cue X between contingency and cue configuration.

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Contingency (ΔPX/ΔPA) 0.5/0

0.5/1.0

0.5/-1.0

Common Elements

39.16

-61.69

83.88

No Common Elements

41.66

-88.16

95.82

Table 1. Experimental design and mean ratings of cue X. Note. Two factor [Contingency ΔPX/ΔPA (0.5/0, 0.5/1.0, 0.5/-1.0) x Elements (Common Elements, No common Elements)] design displaying mean ratings of cue X. Ratings are rounded to two decimals.

Blocking occurred for the 0.5/1.0 (ΔPX /ΔPA) contingency condition, in which the mean ratings of cue X were more negative in no common elements compared to common elements.

Enhancement occurred for the 0.5/-1.0 (ΔPX /ΔPA) contingency condition, in which the mean ratings of cue X were greater in no common elements compared to common elements. The test revealed a significant main effect for contingency; F(2, 122) = 352.62, p < 0.05. This indicates a difference in the mean ratings of cue X across different contingencies (0.5/0, 0.5/1.0, 0.5/-1.0).

The test revealed no significant main effect for elements; F(1, 61) = 1496.011, p = 0.331, indicating that the mean ratings of cue X did not differ between the no common elements and common elements conditions. Additionally, the test revealed a significant interaction effect between contingency and elements; F(2, 122) = 7.922, p < 0.01.

A second two factor repeated measures ANOVA [contingency (blocking

Figure 2. Average ratings of cue X comparing No Common Elements and Common Elements in 0.5/.0, 0.5/1.0 and 0.5/-1.0 (ΔPX /ΔPA) contingency conditions.

White bars represent the contingency manipulation effects on ratings of cue X in the Common Elements condition, and grey bars represent the manipulation effects on ratings of cue X in the No Common Elements condition. Error bars indicate the standard errors of the mean. The graph indicates more blocking of cue X in the No Common Elements condition compared to the Common Elements condition when the contingency ΔPX /ΔPA is 0.5/1.0, and greater enhancement effects in the No Common Elements condition when the contingency ΔPX /ΔPA is 0.5/-1.0.

0.5/1.0, enhancement 0.5/-1.0) x elements (no common elements, common elements)] was conducted to examine differences in mean ratings of cue X between blocking/enhancement effects and cue configurations. When elements were manipulated, the mean ratings of cue X differed across contingency conditions 0.5/1.0 (blocking) and 0.5/-1.0 (enhancement).

The test revealed a significant main effect for contingency; F(1, 61) = 706.200, p < 0.01. This indicates that the mean ratings of cue X differed across ΔPX /ΔPA contingencies (0.5/1.0, 0.5/-1.0). No significant main effect was found for elements; F(1, 61) = 1.911, p = 0.172, indicating that when the control contingency condition (0.5/0) was removed, mean ratings of cue X did not significantly differ across cue configurations (no common elements, common elements) . The test revealed a

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significant interaction effect between contingency and elements; F(1, 61) = 12.227, p < 0.01.

enhancement between no common elements and common elements condition.

The test revealed a significant difference in means; T(61) = 2.236, p < 0.05. This illustrates that greater enhancement of cue X by cue A occurred with no common elements compared to common elements.

Figure 3. Average ratings of cue X comparing No Common Elements and Common Elements in 0.5/.0 and 0.5/1.0 (ΔPX /ΔPA) contingency conditions.

White bars represent the contingency manipulation effects on ratings of cue X in the Common Elements condition, and grey bars represent the manipulation effects on rats of cue X in the No Common Elements condition. Error bars indicate the standard errors of the means. The graph indicates blocking to cue X in 0.5/1.0 ΔPX /ΔPA condition. More blocking of cue X in the No Common Elements condition compared to the Common Elements condition. Blocking occurs past zero.

A post hoc paired samples T-test was performed to compare differences in mean ratings of cue X in blocking between no common elements and common element conditions. The test revealed a significant difference in means; T(61) = -2.837, p = 0.006. This illustrates that greater blocking to cue X by cue A occurred in the no common elements condition compared to the common elements condition.

Similarly, a second post hoc paired samples T-test was performed to compare differences in mean ratings of cue X in

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Figure 4. Average ratings of cue X comparing No Common Elements and Common Elements in 0.5/.0 and 0.5/1.0 (ΔPX /ΔPA) contingency conditions.

White bars represent the contingency manipulation effects on ratings of cue X in the Common Elements condition, and grey bars represent the manipulation effects on rats of cue X in the No Common Elements condition. Error bars indicate the standard errors of the means. The graph indicates enhancement to cue X in 0.5/-1.0 ΔPX /ΔPA condition.

Greater enhancement of cue X in the No Common Elements condition compared to the Common Elements condition.

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Figure 5. Mean ratings of cue X and A comparing contingency (ΔPX /ΔPA) conditions (0.5/0, 0.5/1.0, 0.5/-1.0) and elements (no common elements, common elements).

Black bars represent mean ratings of cue X and white bars represent mean ratings of cue A. Error bars indicate the standard errors of the means. The graph indicates greater blocking and greater enhancement to cue X in the no common elements condition compared to the common elements condition. Judgements of cue X shift in the opposite direction of cue A (i.e. positive versus negative mean ratings).

In addition to the analyses describes above, analyses of variance were conducted for cue A and context. A 3 x 2 [Contingency ΔPX /ΔPA (0.5/0, 0.5/1.0, 0.5/-1.0) x Elements (common elements, no common elements)] ANOVA was performed to assess differences in mean ratings of cue A across conditions. The test revealed a main effect for contingency, F = 457.718, p < 0.001, indicating that ratings of cue A differed across contingency conditions. All other effects were not significant.

A 2 x 2 [Contingency ΔPX /ΔPA (0.5/1.0, 0.5/1.0) x Elements (common elements, no common elements)] ANOVA for cue A revealed a significant main effect for contingency; F = 1228.946, p < 0.001. All other effects were not significant. Post hoc paired samples T-tests comparing differences in mean ratings of cue A and blocking or enhancement revealed no significant results.

A 3 x 2 [Contingency ΔPX /ΔPA (0.5/0, 0.5/1.0, 0.5/-1.0) x Elements (common elements, no common elements)] ANOVA was conducted to assess mean differences in ratings of context. The test revealed a significant main effect for contingency, F = 642.334, p < 0.001, indicating that ratings of context differed across contingency conditions. All other effects were not significant. A 2 x 2 [Contingency ΔPX /ΔPA (0.5/1.0, 0.5/-1.0) x Elements (common elements, no common elements)] ANOVA revealed a main effect for contingency; F = 1586.755, p < 0.001.

All other effects were not significant. Post hoc paired samples Ttests conducted to assess the difference in mean ratings of context in blocking or enhancement yielded nonsignificant results. Overall, the statistical analyses revealed that mean ratings of cue X were significantly effected by manipulations in contingency and an interaction between contingency and elements, whereas the mean ratings of cue A and context were only significantly effected by manipulation in contingency alone.

Discussion The current study examines the effects of manipulating contingency and cue configuration on human causal learning. A computerized causal learning task was used to assess causal judgements made about the relationship between cues (symbols) and outcome (life form). The findings are consistent with the Contrast Hypothesis model and support the predictions.

Blocking (i.e. reduced judgement) to cue X occurred in the 0.5/1.0 (ΔPX/ΔPA) contingency condition and enhancement (i.e. increased judgement) to cue X occurred in the 0.5/1.0 (ΔPX/ΔPA) contingency condition.

This illustrates that the presence of a stronger alternative cue (ΔPA) shifts

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participants’ perceived judgements of the target cue (ΔPX). The judgement of target cue relative to the alternative cue is influenced by polarity. Paired cues of the same polarity (i.e. both positive or both negative) resulted in blocking of the target cue, whereas cues of opposite polarity (i.e. negative and positive) resulted in enhancement of the target cue. Thus, it appears that the reported contingency judgements were based on the strength of cues and polarity, rather than their accumulated associative strength.

The findings also support the hypothesis that cue configuration manipulations would support Pearce’s Configural Theory (1987).

The interaction effect indicates that the ratings of cue X in contingency conditions differed across levels of cue configuration manipulations. In the 0.5/1.0 (ΔPX/ΔPA) contingency condition, more blocking (i.e. stronger negative ratings) was found with no common elements compared to common elements.

In the 0.5/-1.0 (ΔPX/ΔPA) contingency condition, greater enhancement (i.e. stronger positive ratings) was found with no common elements compared to common elements. The increase in blocking and enhancement, which occurred when there were no common elements compared to common elements, provides evidence for increased discrimination between cues (i.e. between alternative cue A and target cue X) when elements were not similar. Alternatively, this may be interpreted as increased generalization between cues when elements were similar. Evidence that the degree of similarity between cues influences causal judgements.

The findings are inconsistent with the Rescorla-Wagner model.

First, enhancement occurred in contingency conditions in which the alternative cue and target cues are of opposite polarity

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(0.5/-1.0). The stronger alternative cue shifted the judgement of the target cue, resulting in greater, rather than reduced, judgement of cue X. Secondly, blocking occurred past zero.

In the 0.5/1.0 contingency condition, the target cue was blocked by the strong alternative cue. The mean rating of cue X was judged below zero, as predicted by the Contrast Hypothesis. Overall, the findings provide evidence in support of the Contrast Hypothesis model (blocking and enhancement) and Pearce’s Configural Theory.

Limitations Although the study revealed significant and meaningful effects, one must consider potential limiting factors.

One limitation exists within the experimental procedure. The feedback provided in the program was most salient when a life form was detected compared to when no life form was detected. A life form detection was signalled with a bright shape appearing in the bottom trial box (each planet presented a unique life form symbol). This occurred irrespective of correct or incorrect judgements.

Consequently, the feedback indicating the presence or absence of a life form is more salient than the feedback indicating a correct or incorrect judgement. Further investigation of the specific impact of the feedback discrepancy must be considered.

This limitation may be controlled for by a modification to the computer program in which the feedback for the detection of a life form and a correct/incorrect judgement are matched on salience.

Additionally, the findings based on a computerized task may be limited in the generalizability to real-world human causal learning. Although a wealth of information on human learning has been based on computer tasks (for review see Barberia et al.), one must consider that

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real-world contingency judgements may not follow the exact patterns as seen in learning about symbolic cue elements and a single outcome. It is important when extrapolating these findings, one appreciates the complexity of the conditions of real-world human learning in which a great number of factors may influence our judgements.

Implications These findings provide clear evidence supporting the Contrast Hypothesis (Darredeau, 2009) and Configural Theory (Pearce, 1987) as a result of contingency and configuration manipulation on human causal learning.

These findings also encourage further investigation into the underlying mechanisms of human causal learning, specifically to the role of context. The current study presents insignificant interactions between learning about context and cues; however, it has been noted that the context present in the task is quite low in salience. Examination of context in cue-competition would shed greater light on our analysis of human causal judgements and the development of accurate explanations with associative models.

You must figure out whether the chemicals detected by the indicators signal that a life form will be present or absent.

To do so, you will observe several trials in which some or all indicators have detected a chemical. On every trial you will guess whether a life form will be detected. After your guess, you will receive feedback. First you will have to guess, but your predictions might get more accurate later on.

After you have observed all data from a planet, you will evaluate different chemical combinations detected by the indicators using a scale ranging from -100 to _100. Positive numbers mean that the combination signals a positive environment in which the life form is likely to be detected, while negative numbers indicate that the combination signals a negative environment in which a life form is NOT likely to be detected. Zero means that a life form is no more or less likely to be detected given that combination of chemicals, so these chemicals are not useful for making predictions. After you rate the chemical combinations, you will move on to another planet on which a different form of life was discovered.

Appendix Task Instructions

Baetu, I., & Baker, A. G. (2009). Human judgements of positive and negative causal chains.

Journal of Experimental Psychology: Animal Behaviour Processes, 35(2), 153168. doi: 10.1037/a0013764 Baker, A. G., Mercier, P., ValleeTournageau, F., Frank, R., & Pan, M. (1993). Selective associations and causality judgements: presence of a strong causal factor may reduce judgements of a weaker one. Journal

Imagine you are on a spaceship travelling in a nearby galaxy. There are many planets on which new forms of life have been recently discovered. Your task is to determine the environment in which these life forms survive.

You will land on six planets. Your spaceship is equipped with a set of indicators that detect various chemicals in the environment. When the indicators detect a chemical they display a symbol as in the example below. Different symbols indicate different chemicals.

References

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of Experimental Psychology: Learning, Memory and Cognition, 19 (2), 414-431.

Barberia, I., Baetu, I., Murphy, R. A., & Baker, A. G. (2011). Do associations explain mental models of cause? International Journal of Comparative Psychology, 24, 1 â&#x20AC;&#x201C; 24.

Bouton, M. E. (2007). Learning and Behaviour: A Contemporary Synthesis.

Massachusetts, USA: Sinauer Associates, Inc. Darredeau, C., Baetu, I., Baker, A. G., & Murphy, R. A. (2009). Competition between multiple causes of a single outcome in causal reasoning.

Journal of Experimental Psychology: Animal Behaviour Processes, 35 (1), 1-14.

Lober, J. (2012). Animal Learning and Theory, [PowerPoint slides].

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Mentoring Programs for Lower-Income Youth with Externalizing Symptoms: Moderators and Mediators of Effectiveness Carly Surchin

Mentoring is a popular intervention for at-risk youth (Dubois, Holloway, Valentine, & Cooper, 2002; Rhodes & Dubois, 2008; Roberts, Liabo, Lucas, DuBois, & Sheldon, 2004). This particular intervention involves an organized one-on-one relationship between a volunteer, who provides support and guidance, and someone who is younger or less experienced (Dubois et al., 2002; Roberts et al., 2004). Big Brothers/Big Sisters of America (BB/BSA), comprised of hundreds of local agencies worldwide, is the most prominent and largest of these programs (Dubois et al., 2002; Rhodes & Dubois, 2008).

BB/BSA focuses on providing these types of relationships for youth through its widespread local chapters. As of 2008, an estimated three million youth in the United States were engaged in a formal mentoring relationship (Rhodes & Dubois, 2008).

Mentoring programs have been thought to have the potential to help youth in a variety of realms, “including emotional and behavioral functioning, academic achievement, and employment or career development” (Dubois et al., 2002, pgs. 160-161). This paper will focus specifically on the effects of mentoring programs as they pertain to youth with

externalizing symptoms. Liu (2004) describes childhood externalizing symptoms as including the concepts of aggression, delinquency and hyperactivity.

Campbell, Shaw, and Gillom (2000) and Eisenberg et al. (2001) posit that externalizing behavior problems are reflected through outward behavior, through which the child acts negatively on the external environment (as cited in Liu, 2004).

The research literature has demonstrated an association between lower socioeconomic status and the development of psychopathology (Johnson, Cohen, Dohrenwend, & Link, 1999; Offord, Boyle, & Racine, 1989). For example, Roberts (1997) suggests that British birth cohort data from 1970 shows increasing hyperactivity and conduct disorder with decreasing social class. As suggested by Rhodes (1994), an interest in mentoring programs has been promoted by the idea that positive relationships with extra-familial adults can foster resilience in vulnerable youth. In this sense, mentoring may have the potential to help those youth with lower socioeconomic status who demonstrate externalizing and antisocial behaviors.

Externalizing and antisocial behavior in childhood poses serious risks

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to the affected individuals, their families, and society at large. Economic resources 1998), and externalizing behavior can pose great challenges for politicians, health and welfare professionals, police, and communities (Roberts et al., 2004). Given the maladaptive consequences that occur as a result of psychopathology, the need for effective interventions is vital.

This need for successful treatment programs begs the question of whether evidence for mentoring programs shows benefits for vulnerable youth with externalizing symptoms. Given the widespread enthusiasm for these programs (Rhodes & Dubois, 2008), there are various reasons that explain why one would want or expect mentoring programs to work. Firstly, our intuitions seem to lend support for this intervention.

Mentoring programs have strong face validity, intuitively telling us that they will work (Roberts et. al, 2004). Secondly, authors such as Werner (1993) have discussed the idea that the presence of supportive adults can act as a protective factor which enables high-risk youth to experience positive outcomes. Thirdly, mentoring does not require drug treatment (Roberts et al., 2004), which is likely attractive to both the individuals themselves and to their parents, especially in the case of younger children who are still developing. Furthermore, mentoring programs for at-risk populations have received hundreds of millions of dollars in support in various regions, including in the United States and England (Roberts et al., 2004). As funding has been pumped into these programs on a large scale, one would hope that society’s dollars and resources have been allocated towards constructive ends. Youth mentoring is often painted as a cost-effective, preemptive intervention that will help to prevent problems before they can intensify (St James-Roberts &

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spent on youth with conduct disorder are considerable (Scott, Singh, 2001). Intuitively, mentoring programs seem to be an ideal solution to a difficult problem.

Given the widespread use of mentoring programs for youth, including those with externalizing symptoms, it is important to evaluate the effectiveness of this intervention. Organized mentoring has been utilized as an intervention for youth with behavioral difficulties in the United States for over a century, dating back to reform-oriented efforts in the juvenile courts (Rhodes & Dubois, 2008).

Roberts et al. (2004) have suggested that, with regards to mentoring, there remains a vast array of unanswered questions regarding the efficacy of different types of programs. Due to the attractiveness of mentoring programs for policymakers and communities, there may be a danger of “running ahead of the evidence” (Roberts et al., 2004, p. 512). As there is large-scale funding for and dissemination of this intervention, there is also a risk that what becomes published research may be biased toward support for these interventions (Roberts et al., 2004). Considering that mentoring programs are offered on a mass scale and can affect policies regarding disorders that can have maladaptive outcomes (Cavell, Elledge, Malcolm, Faith, & Hughes, 2009), it is imperative that the findings be evaluated critically.

Research by Roberts et al. (2004) regarding this intervention for youth with externalizing symptoms suggests that there are benefits vary contextually between type of program, mentor-style, and mentee experience. Due to an apparent heterogeneity in estimates of effect size for mentoring programs, authors such as Dubois et al. (2002) suggest a need for a greater consideration of the specific factors which influence the success of these

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interventions. In order to better implement interventions for those struggling with externalizing symptoms and to ensure that social and economic resources are used effectively, it is essential to hone in on the specific mechanisms through which mentoring programs can benefit antisocial youth. This raises the question, what are the moderators and mediators of the effectiveness of mentoring programs for reducing externalizing symptoms among lower-income youth?

Evaluating the Effectiveness of Mentoring Programs In general, there are varying findings regarding the effectiveness of mentoring programs for youth. Dubois et al. (2002) conducted a meta-analytic review of 55 evaluations of these interventions in order to examine their success. Overall, the authors found a benefit of mentoring programs for the mentees and reported support for their effectiveness, but they stated that this benefit was modest or small (Dubois et al., 2002). The authors reported that, at the time of publishing in 2002, reviews regarding the effectiveness of this intervention had only recently begun to appear in the literature, despite long-term use of these programs (Dubois et al., 2002). This further supports the idea that funding and widespread dissemination of programs such as BB/BSA may have come ahead of the data needed to confirm the success of the intervention. Overall, Dubois et al. (2002) report that a typical youth participating in a mentoring program showed an approximate increase of one-eighth of a standard deviation in a favorable direction in terms of outcome measures, compared to the average youth before or without participation in one of the programs included in the review.

Given their investigation, the authors suggest that mentorship programs are

capable of reproducing beneficial relationships between youth and adults through the formal mechanism of organized mentoring programs. The authors also suggest positive effects of mentoring programs for youth with externalizing symptoms, based on positive effects (i.e. improvements) found for measures of outcome such as problem/high-risk behavior. The authors make the cautionary statement, however, that the average mentee will likely accrue only modest benefits. As a result of their meta-analytic review, the authors suggest that the small benefits found seem to fall short of the expected benefits, considering the widespread support mentoring has received over the years (Dubois et al., 2002).

Other authors have examined the effectiveness of mentoring programs, specifically in relation to youth exhibiting antisocial and externalizing symptoms, and found less promising results. Roberts et al. (2004) argue that mentoring programs for youth with externalizing behaviors show the danger of being implemented before accurate evidence for their effectiveness has been established.

Other authors, however, explain that mentees with behavior problems, the parents of these mentees, and the mentors report improvements as a result of the experience (St James-Roberts & Singh, 2001). Despite this, Roberts et al. (2004) conclude that given the current literature, mentoring programs do not appear to provide measureable effectiveness for the reduction of externalizing symptoms, such as truancy. Overall, these authors seem to suggest the need for further research in terms of the mechanisms by which mentoring can be effective. Roberts et al.

(2004) do not state that mentoring will never result in positive outcomes, but instead suggest the need for further

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understanding and improvement of the intervention.

Cavell et al. (2009) have also examined the impact of mentoring on aggressive, high-risk children. As with other research in the literature, these authors propose that, in general, the proliferation of this intervention has moved faster than the evidence found in support of it (Cavell et al., 2009). Cavell et al. (2009) also posit the idea that research is lacking specifically with regards to mentoring relationships for highly aggressive children.

With regards to the effectiveness of mentoring programs on externalizing symptoms, St James-Roberts and Singh (2001) report that positive increases in confidence, self-control and relationships for mentees were equivalent to those felt by non-mentored children. These authors suggest that this also appears to be true for standardized measures of behavior such as school attendance, social exclusion, and academic performance (St James-Roberts & Singh, 2001). While mentored children improved in these areas, a comparison group without mentors also improved (St James-Roberts & Singh, 2001), perhaps simply due to the perceived increase in structure in the youthsâ&#x20AC;&#x2122; lives. Overall, these findings do not appear to be in support of mentoring programs for lowerincome antisocial youth.

Many reports in the literature, including those previously discussed in this paper, acknowledge research conducted by advocates for widespread BB/BSA programs. The authors of these reports provide data in support of benefits for atrisk youth with externalizing symptoms.

These ideas are reflected in a report written by Tierney, Grossman, and Resch (2000). Tierney et al. (2000) claim to provide strong support for mentoring programs, specifically referring to

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statistically significant results regarding antisocial behaviors. The authors report that mentees were less likely to start using drugs and alcohol or to hit someone, showed improved school attendance and performance, and improved relationships with peers and family members (Tierney et al., 2000).

Despite these positive findings by Tierney et al., the overwhelming majority of the literature points toward the need for more research in order to increase the benefits of mentoring programs. In order to better understand how positive effects of mentoring programs can be achieved more consistently, it is imperative to investigate the mechanisms through which these interventions operate. Roberts et al.

(2004) are not suggesting that mentoring does not work; instead, the authors point to the idea that some kinds of mentoring may be more effective than others. Given this, it is important to examine the moderators and mediators of the effectiveness of mentoring programs for youth with behavioral difficulties.

Moderators and mediators of effectiveness Moderators of the effectiveness of mentoring programs A moderator variable is a factor that influences the direction or strength of a relationship between variables. Potential moderator variables discussed by Dubois et al. (2002) with regard to mentoring are demographic and background characteristics of mentees, such as age, gender, ethnicity, family structure, and socioeconomic status. Dubois et al. (2002) report similar effects of mentoring across all of these characteristics, with the exception of environmental disadvantage, including socioeconomic status. In this sense, the majority of the aforementioned factors do not appear to be significant moderators of the effectiveness of

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mentoring programs. The authors also suggest that characteristics of mentors such as gender and race were not significant moderators of effect size. Chan and Ho (2008) provide support for this idea and report that all investigated personal attributes of the mentee and the majority of personal attributes of the mentor are not related to the effectiveness of mentoring programs. These authors also note that it is important to remember that personal attributes cannot be manipulated or changed easily with regard to the planning and dissemination of mentoring programs.

As previously mentioned, there is evidence throughout the literature of an association between socioeconomic status and the presence of psychopathology. In their meta-analytic review, Dubois et al.

(2002) find support for the view that mentoring programs offer the greatest potential benefits to youth considered most at-risk. Offering further support for this notion, Zand et al. (2009) suggest that mentoring programs have shown promise as a preventive mechanism in promoting competence in high-risk youth. The authors include school-based functioning in this concept of competence, suggesting improvements in externalizing symptoms for mentees. In an examination of mentoring relationship failures, Spencer (2007) suggests that environmental factors such as fraught family relationships and lower socioeconomic status may make it difficult for adults potentially without these experiences to form relationships with vulnerable youths. Dubois et al. (2002), however, suggests that youth from backgrounds of environmental risk may be able to reap the greatest benefits from mentoring programs.

While Dubois et al. (2002) assert the benefits of mentoring programs for youth with environmental disadvantages,

including those with lower socioeconomic status, the authors also report that these benefits may be lacking for youth who are at risk on the basis of individual-level characteristics (e.g., academic failure).

Given the association between poverty and mental illness, these findings may pose risks for youth who are of lower socioeconomic status and also show externalizing symptoms. This raises the question of whether the benefits accrued to youth of lower socioeconomic status from mentoring can outweigh the lack of evidence for benefits for those with individual-level difficulties. Dubois et al.

(2002) address this concern, suggesting, “substantial positive effects of mentoring reported for programs in which youth targeted for participation could be regarded as at-risk from both an individual and environmental perspective” (p. 190).

The authors suggest two possible explanations for the positive effect of mentoring programs when both environmental and individual risk factors are present. For one, environmental risk may have a greater role in determining how responsive a mentee is to his or her mentor. Secondly, the authors suggest that given the presence of environmental factors, seemingly outside of the mentees’ control, mentors will be less likely to prematurely judge mentees based on personal shortcomings. This offers hope for lower-income youth with externalizing symptoms, as they exhibit both environmental and individual risk difficulties.

Mediators of the effectiveness of mentoring programs A mediator variable is the mechanism or means through which a variable produces an outcome. A variety of mediator variables that could explain the relationship between the use of mentoring programs and the outcome for

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youth are discussed throughout the literature. In their meta-analytic review, Dubois et al. (2002) suggest that effects are improved through the use of 11 theorybased and 7 empirically-based â&#x20AC;&#x2DC;best practices,â&#x20AC;&#x2122; and with the formation of a strong relationship between mentors and their mentees. The authors explain a cumulative effect of the use of these best practices. In other words, their data shows no single feature of either set of best practices to be responsible for the effects of mentoring; it appears that the combination of sets is necessary to give rise to successful mentorships. These best practices included ongoing training for mentors, structured activities for mentors and youth, expectations of frequency of contact, mechanisms for support and involvement of parents, and monitoring of overall program implementation. Along with the use of these best practices, which together seem to suggest increased support and structure for mentoring programs, Dubois et al. (2002) indicate a strong link between outcome and the intensity and quality of the mentoring relationship.

Dubois et al. (2002) also illuminate some of the features of mentoring programs that do not appear to explain the relationship between mentoring and outcome for youth. According to their review, effect size was not dependent on whether mentoring takes place alone or in tandem with other interventions, whether it is provided in accordance with BB/BSA models, or whether programs reflect more general goals (i.e. promoting positive psychosocial development) as opposed to more focused goals (i.e. promoting employment and education).

Rhodes and Dubois (2008) also provide evidence for the importance of the quality of the relationship between the mentee and the mentor for program effectiveness. These authors report that

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positive effects can only be established to the extent that the mentor and mentee have developed a mutually trusting relationship. The authors explain that in order to foster these high-quality relationships, mentors and mentees need to interact for a long period of time.

Furthermore, the authors suggest that relationship quality explains the association between mentoring programs and outcomes for youth by promoting positive developmental change. These authors also provide further support for best practices such as those reported by Dubois et al. (2002), suggesting their use in order to facilitate these mutually beneficial relationships (Rhodes & Dubois, 2008).

Other authors have examined these suggested mediators of mentoring programs as they pertain specifically to high-risk youth with externalizing problems. Cavell et al. (2009) examined the degree to which relationship quality predicted outcomes for aggressive children in two different types of mentoring programs. One of the mentoring programs, referred to as PrimeTime, was community-based and provided skills training and consultation for parents and teachers, along with extensive training and supervision for mentors. The other program, referred to as Lunch Buddy, was less structured, involving lunchtime visits twice a week by mentors to mentees, with a different mentor used each semester.

The mentors in the Lunch Buddy program did not receive formal training or supervision, apart from a 90-minute orientation session where they received a handout describing how and when to end the mentoring relationship and a description of responsibilities and tips.

Despite children in the PrimeTime condition rating their mentors as more supportive than children in the Lunch Buddy condition, there was little evidence

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to suggest that support variables were related to teacher-rated externalizing problems. Following further investigation, the authors found that it was relationship conflict that predicted teacher-rating externalizing symptoms. More specifically, “children in less conflicted mentoring relationships were viewed by teachers as having less problem behavior” (p. 194).

The authors also found that treatment condition did not moderate the relationship between teacher ratings and relationship conflict. In both conditions, increased relationship conflict suggested increased ratings of externalizing symptoms by teachers. The authors suggest that this may have been due to the idea that both conditions involved schoolbased activities that potentially influenced children’s behavior within a school setting, where teachers could perceive it. In terms of parent-rated outcomes of externalizing behavior, relationship quality was predictive of outcomes only in the PrimeTime condition. Overall, these findings seem to suggest that aspects of the mentoring bond —its quality, and whether it is positive or negative—seem to explain the relationship between treatment and outcomes with regard to externalizing symptoms.

Other studies have also found support for the idea that relationship conflict can mediate program effectiveness. Similar to Cavell et al.

(2009), Chan and Ho (2008) report that what they refer to as “perceived relationship asymmetry” can impact the benefits of mentoring programs felt by mentees. Chan and Ho (2008) suggest that asymmetrical relationships are characterized by themes that may include: the mentee feeling that their mentor is too busy or passive, poor communication between the mentor and mentee, the mentor breaking promises and the mentee

using the relationship only for instrumental gains, or the mentee lacking engagement. It is of note that Chan and Ho (2008) did not specifically examine effects for high-risk, aggressive youth.

There has been evidence to suggest, however, that mentors may encounter challenges and conflict in dealing with youth with individual-level difficulties (Dubois et al., 2002). This implies that issues regarding relationship conflict may be of particular relevance to the formation of mentor and mentee relationships for youth exhibiting behavioral difficulties.

The data demonstrated by Cavell et al. (2009) is consistent with findings suggested by Dubois et al. (2002) with regard to the role of relationship quality.

Cavell et al. (2009) offer a more specific examination of this mediating variable as it pertains to high-risk aggressive children.

According to the authors, the organizers of mentoring programs may need to keep in mind that relationship conflict can play a role in externalizing symptoms, especially in programs where there is increased contact between mentors and mentees.

Such increased contact may provide ample opportunities for conflict to arise.

The training and supervision required for mentors to manage these negative aspects of relationships should be provided in these circumstances. These ideas may be linked to those suggested by Dubois et al.’s (2002) best practices, which include themes of increased support and structure for mentors. These factors may be pertinent in more intensive program conditions in which relationship conflict may be more likely to arise, such as BB/BSA and the PrimeTime program suggested by Cavell et al. (2009).

Proponents of BB/BSA explain that findings regarding externalizing symptoms, such as mentees being less likely to start using drugs or alcohol, hit

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someone, or skip school, “reflect the workings of a carefully structured approach to mentoring” (Tierney et al., 2000). Many of the BB/BSA participants in the sample used by Tierney et al. (2000) were from low-income households, with a significant number from households showing externalizing symptoms such as a history of family violence or substance abuse. While Dubois et al. (2002) did not find adherence to BB/BSA programs to be a significant mechanism with regard to effect size, the positive findings of Tierney et al. (2000) might reflect a general use of best practices such as support and structure. In their earlier report of the impact of BB/BSA, Grossman and Tierney (1998) also reiterate this idea. The authors explain that the positive impacts were unlikely to have occurred without the relationship with the mentor (involving a high level of contact with the mentee) and the support that the program provided the pair (Grossman & Tierney, 1998).

Discussion In light of the review of the literature, themes emerge which suggest that that it should be considered that “mentoring is an inherently interpersonal endeavor” (Dubois et al., 2002, p. 189).

Given the aforementioned evidence pointing to specific aspects of the mentoring relationship (such as its quality and the presence of conflict) mediating the beneficial effects of the intervention, factors that could affect this relationship must be acknowledged. Working with youth with externalizing symptoms may pose additional challenges for the mentor, given the mentee’s tendencies toward disruptive, hyperactive or aggressive behavior. In line with these ideas, Werner (1993) suggests that individuals with easy temperaments may have more sociable and pleasant habits as babies, and are also able to rely on a wider network of caring

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adults in middle childhood. He goes on to posit that social support can be protective in allowing high-risk individuals to experience positive outcomes. This idea raises interesting questions with regards to high-risk children, who do not have easy temperaments but instead show externalizing symptoms. If there is a link between temperament and access to social support, as suggested by Werner (1993), research must be conducted to examine if children with difficult temperaments receive less social support and therefore less facilitated resilience. Additionally, it would be of importance to address whether or not these children with difficult temperaments would be less likely to successfully participate in an interpersonal program such as PrimeTime or BB/BSA. One would hope that youth with externalizing symptoms would be able to participate in sustained mentoring matches, given the negative impacts that have been shown for mentees when mentoring relationships breakdown and terminate prematurely (Grossman & Rhodes, 2002). Grossman and Rhodes (2002) suggest that relationships that terminated after short periods of time are associated with decreased levels of functioning in several areas for mentees. It appears important that the breakdown of relationships be avoided, and that the mentor and mentee maintain frequent contact for an extended duration of time.

It must be noted, however, that this link between temperament and social support as discussed by Werner (1993), does not discuss social support when it is provided to youth by willing volunteers. In order to avoid the premature breakdowns in mentoring matches, Dubois et al. (2002) suggest that youth with individual-level difficulties may require specialized assistance, mentioning a requirement for mentor training to ensure beneficial

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mentorships. Fortunately, the authors also state that adherence to best practices, such as mentor training, may help youth with externalizing symptoms to reap the benefits of mentoring. As mentioned previously, individual-level difficulties may also be easier to approach in the context of environmental disadvantages that appear out of the menteeâ&#x20AC;&#x2122;s control and therefore are less likely to be blamed on lack of motivation (Dubois et al., 2002).

Nevertheless, it seems crucial to consider the idea that these youth may pose unique challenges to the mentoring relationship, and that training and overall program design should be structured accordingly.

As such, Dubois et al. (2002) have noted the beneficial effects of the utilization of mentors who have a background in a helping role or profession (i.e. teachers). It is possible that these mentors would be more adept in developing a meaningful relationship with at-risk youth due to their background knowledge and expertise. It is interesting to note, however, that the literature largely includes mentors who are volunteers. It is possible that an average volunteer would not be employed in a helping profession, and so the implementation of such an idea might substantially affect the recruitment of mentors. This limitation might also reduce the number of eligible volunteers available to potential mentees in need.

Implications While conflicting evidence may exist in the literature regarding the benefits of mentoring programs for antisocial, lower-income youth, the understanding of mediators that promote effectiveness may help policymakers to deliver better results. It is possible that effect size may be small or modest as a result of a lack of effort put toward strengthening the effect of these mediators.

For antisocial youth, ameliorating

relationship conflict appears particularly relevant. It is important to consider that youth from a higher socioeconomic status with externalizing symptoms may not benefit from the same interventions as lower-income youth with similar symptoms.

At the conclusion of their metaanalytic review, Dubois et al. (2002) discuss how their finding of a small or modest beneficial effect of mentoring programs seems to suggest room for innovation and improvement. Many authors throughout the literature have noted the gaps between evidence and the use of mentoring programs (Cavell et al., 2009; Rhodes & Dubois, 2008; Roberts et al., 2004). This suggests an overall need for increased research into the factors that make mentoring effective, especially those for highly vulnerable populations such as youth with psychopathology and/or lower socioeconomic status. In order to do so, it seems pressing to investigate the mechanisms that would allow for the relationship quality between mentees and mentors to be strengthened. The mechanisms through which long lasting, positive relationships can be fostered need to be further investigated. It is important that policymakers and researchers collaborate to hone in on the ways through which relationship conflict and breakdown can be avoided. This is especially crucial for the youth who need it most, those who show externalizing symptoms and come from lower-income families. It appears to be especially important that mentors working with these environmentally disadvantaged youth, who also show individual-level difficulties, be trained in practices specifically relevant to these kinds of youth. Mentoring shows promise, but dissemination of the intervention does not seem to have been perfected. An established understanding of

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the underlying mechanisms through which mentoring can benefit at-risk youth offers promise for increased success of these programs in the future. As suggested by Roberts et al. (2004), â&#x20AC;&#x153;our current state of knowledge on the effectiveness of mentoring is similar to that of a new drug that shows promise but remains in need of further research and developmentâ&#x20AC;? (p.

513).

Limitations This review of the literature illustrates the idea that there appears to be a lack of available findings regarding mentoring specifically as it pertains to lower income youth with externalizing symptoms. Accordingly, the scope of this investigation and the ability to draw conclusive findings is limited. It seems that authors who examined youth specifically with behavioral problems reported a lack of evidence for mentoring programs (Roberts et al., 2004). Dubois et al. (2002) suggest, however, that mentees showing both environmental and individual-level risk factors may be able to experience benefits. Given the association between lower socioeconomic status and psychopathology (Johnson et al., 1999; Offord et al., 1989), one might expect for many youth of low socioeconomic status to also show externalizing symptoms, and vice versa. It is important to determine if the suggestions made by Dubois et al.

(2002) would be evident in samples that looked specifically at youth with both of these specific characteristics, as the same interventions will likely not work for everyone, thus limiting the generalizability of these results. Instead of considering what works, we must instead consider what works for whom.

Conclusion Mentoring is one of the most popular interventions for at-risk youth and has received enthusiastic support (Dubois

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et al., 2002; Rhodes & Dubois, 2008).

Despite its large-scale funding (Roberts et al., 2004) and widespread propagation, a meta-analytic review suggests only small or modest benefits of mentoring programs (Dubois et al., 2002).

Given the findings throughout the literature, various moderators and mediators appear for mentoring programs, which are often implemented for disadvantaged youth with behavioral difficulties. Evidence exists for possible moderator variables of effectiveness such as socioeconomic status (Dubois et al., 2002; Zand et al., 2009), potentially when in combination with individual-level risk factors (Dubois et al., 2002). It also appears that the association between the use of mentoring programs and the benefits for youth would not exist without a high quality relationship between the match (Cavell et al., 2009; Chan & Ho, 2008; Dubois et al., 2002; Rhodes & Dubois, 2008; Tierney & Grossman, 1998), along with best practices that promote support and structure (Dubois et al., 2002; Rhodes & Dubois, 2008, Tierney et al., 2000). With regards to youth with externalizing symptoms, Cavell et al. (2009) provide support for the idea that the presence of relationship conflict can mediate the association between the administration of mentoring programs and their outcomes. These authors suggest that increased relationship conflict predicts increased teacher-rated externalizing behaviors (Cavell et al., 2009). Chan and Ho (2008) provide further support for this in their suggestion that relationship asymmetry may affect mentoring outcomes for youth. Thus, it appears that the formation of a high quality, low conflict relationship with an adult who provides support and structure is key to producing benefits to youth. It is imperative that researchers recognize the

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suggested success of certain mentorship programs, and continue to endeavor to improve such initiatives.

References Cavell, T. A., Elledge, L. C., Malcolm, K. T., Faith, M. A., & Hughes, J. N. (2009). Relationship quality and the mentoring of aggressive, highrisk children. Journal of Clinical Child & Adolescent Psychology, 38(2), 185198. doi: 10.1080/15374410802698420 Chan, C. C., & Ho, W. C. (2008). An ecological framework for evaluating relationship-functional aspects of youth mentoring. Journal of Applied Social Psychology, 38(4), 837-867. doi: 10.1111/j.15591816.2008.00329.x DuBois, D. L., Holloway, B. E., Valentine, J. C., & Cooper, H. (2002).

Effectiveness of mentoring programs for youth: A metaanalytic review. American Journal of Community Psychology, 30(2), 157197. doi: 10.1023/A:1014628810714 Grossman, J. B., & Rhodes, J. E. (2002).

The test of time: Predictors and effects of duration in youth mentoring relationships. American Journal of Community Psychology, (2), 199-219.

Grossman, J. B., & Tierney, J. P. (1998).

Does mentoring work? An impact study of the Big Brothers Big Sisters program. Evaluation Review, 22(3), 403-426. doi: 10.1177/0193841X9802200304 Johnson, J. G., Cohen, P., Dohrenwend, B. P., Link, B. G., Brook, J. S. (1999). A longitudinal investigation of social causation and social selection processes involved in the association between socioeconomic

status and psychiatric disorder.

Journal of Abnormal Psychology, 108.

490-499.

Liu, J. (2004). Childhood externalizing behavior: Theory and implications.

Journal of Child and Adolescent Psychiatric Nursing, 17(3), 93-103. doi: 10.1111/j.17446171.2004.tb00003.x Offord, D. R., Boyle, M.H., & Racine, Y. (1989). Ontario child heath study: Correlates of disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 28, 856- 860.

Rhodes, J. E. (1994). Older and wiser: Mentoring relationships in childhood and adolescence. Journal of Primary Prevention, 14, 187–196.

Rhodes, J. E., & DuBois, D. L. (2008).

Mentoring relationships and programs for youth. Current Directions in Psychological Science, 17(4), 254-258. doi: 10.1111/j.14678721.2008.00585.x Roberts, H. (1997). Socioeconomic determinants of health: Children, inequalities, and health. BMJ, 314(7087), 1122. doi: 10.1136/bmj.314.7087.1122 Roberts, H., Liabo, K., Lucas, P., DuBois, D., & Sheldon, T. A. (2004).

Mentoring to reduce antisocial behaviour in childhood. BMJ, 328(7438), 512-514. doi: 10.1136/bmj.328.7438.512 Scott, S. (1998). Fortnightly review: Aggressive behaviour in childhood.

BMJ, 316(7126), 202-206. doi: 10.1136/bmj.316.7126.202 Spencer, R. (2007). “It’s not what I expected”: A qualitative study of youth mentoring relationship failures. Journal of Adolescent

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Research, 22(4), 331-354. doi: 10.1177/0743558407301915 St James-Roberts, I., & Singh, C. S. (2001). Can mentors help primary school children with behaviour problems? Final report of the three-year evaluation of Project Chance carried out by the Thomas Coram Research Unit between March 1997 and 2000.

Tierney, J. P., Grossman, J. B., & Resch, N. L. (2000). Making a difference: An impact study of big brothers/big sisters.

Public/Private Ventures.

Werner, E. E. (1993). Risk, resilience, and recovery: Perspectives from the Kauai Longitudinal Study.

Development and Psychopathology, 5, 503-515.

Zand, D. H., Thomson, N., Cervantes, R., Espiritu, R., Klagholz, D., LaBlanc, L., & Taylor, A. (2009).

The mentor–youth alliance: The role of mentoring relationships in promoting youth competence.

Journal of adolescence, 32(1), 1-17.

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Mental Health Courts: Do They Benefit Mentally Ill People in the Criminal Justice System?

Hubie Yu

Abstract The deinstitutionalization of mental health facilities in the 1960s and 70s gave rise to augmented arrest rates for people with mental illnesses, as a result of an increased presence of such people in the community. Research shows that this group of people is overrepresented in jails and is less likely to be released on bail, thereby serving longer prison sentences. Because of criminal courts’ inadequacy in working with those with mental illnesses, many states and provinces across North America started building Mental Health Courts (MHCs) as a potential solution. This paper outlines in the beginning the historical factors that contributed to the rise in popularity of these courts and their general mandates, then explores the relationship between them and people with mental illnesses. Additionally, this paper attempts to show the flaws of these courts: the lack of standardization across MHCs, the contribution to the stigma associated with mental illnesses, and the exacerbation of the problem of insufficient resources.

The law exists to protect the public from harm, and as a “guardian of its citizens,” the government is empowered to enact and implement laws for the care and protection of those who are unable to care for themselves or to make their own decisions (Shah, 2008). However, the legal system has often failed to interpret laws in ways that are cognizant of mental health and sympathetic to people with mental illnesses, especially in the criminal justice setting ("Developments in The Law of Mental Illness," 2008). Research shows that people with mental illnesses are more likely to be arrested, are over-represented in jails, are less likely to be released on bail, and typically serve longer prison sentences (Wolff, 2002). In response to this, mental health courts (MHCs) were created as one potential solution.

Precedent for the development of MHCs was the implementation of America’s first drug court in 1989 in Dade County, Florida. MHCs originated in 1997 in Broward County, Florida, a neighbouring county of the first drug court, and were created to be less punitive than drugs courts. Instead, their goal is to maximize treatment options and to provide an appropriate context for mentally ill people within the criminal justice system. Falling under the category of problem-solving or specialty courts MHCs are usually considered part of a local – and sometimes state – court system.

They were in part created to divert this population from jail and prison into community treatment, and to stop the revolving door of repeated cycling of this population through the criminal justice

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system (Redlich et al., 2006). The intent of MHCs is to reduce criminal behaviour and recidivism by directly treating the illness that is causing illegal behaviours they are limited to Toronto, Saint John, Halifax, and St. John’s, though there are plans to construct more throughout the country (Makin, 2011). The discussion of MHCs in this paper will be limited in several ways. First, this paper will be primarily referencing articles on American MHCs, due to the dearth of literature on the Canadian MHC system. Second, literature in this area is quite limited.

Despite the growth of MHCs, empirical data remain sparse. There are many informative articles written on individual courts, but general analyzes are not readily available. While these individual courts will be mentioned throughout this paper, they are by no means representative of the entire nation’s MHCs. Third, there exists no uniform definition of “mental health courts” in the literature reviewed. While MHCs are described throughout the literature, each comes with a variety of definitions; for example, the inclusion of the words “criminal court” in the definition itself. This paper will not attempt to provide a definition; instead, it will describe the goals and mandates of these courts. When “criminal court” is mentioned, it refers to the traditional criminal justice system; people with mental illnesses who participate in these courts will be referred to as “participants” and not “defendants,” a term many authors have chosen to use. The following will provide a history of the creation of these courts and their general mandates, then explore the relationship between these courts and people with mental illnesses.

Many authors have outlined the inherent and practical flaws of mental health courts, but none have written about

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(Wolff, 2002).

There are currently around 100 of these courts in the United States. In Canada, its impact on the mentally ill community.

While MHCs were first created to benefit these people, they do not address the root of the problem that causes overrepresentation of people with mental illnesses in the criminal justice system.

Three flaws are apparent: first, mental health courts are not standardized, and as a system, they do not consistently benefit the mentally ill. Second, they contribute to the stigma associated with mental illnesses.

Lastly, they exacerbate the existing problem of lack of resources in the American mental health system.

Rise and Popularity of Mental Health Courts in America On November 13th, 2000, American President Bill Clinton signed Senate Bill 865 into law (Public Law 106515), which appropriated $7 million to the development of new Mental Health Courts. This historic turning point in MHC history can be attributed to predating social factors, namely, deinstitutionalization of those suffering from mental illnesses in the 1960s and 70s.

This shift had the indirect consequence of the increased recycling of mentally ill people through the criminal justice system.

During the 1960s and ’70s, the transition of people with mental illnesses from state and county facilities to community outpatient clinics created a more visible presence of mental illness in society, causing increased reports to police by scared citizens. As a result, in order to avoid bureaucratic and legal impediments to mental health referrals, the police often opted to arrest these individuals. Thus, the criminal justice process became the ‘default’ for people unable to be treated

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within the mental health system, especially in situations where these individuals were not sufficiently disturbed to be hospitalized, yet were too public in their deviance to be ignored (Teplin, 1984).

Several regional studies concluded that arrest rates of people with mental illnesses increased before and after deinstitutionalization. Multiple authors have also shown a direct correlation between the decrease of psychiatric beds and increase of mentally ill patients (Pogrebin & Poole, 1987). This presents several problems, as deinstitutionalization is a continuous process, and to this day the repercussions of these patients’ departure from institutions are still felt. With the lack of criteria to determine when the level of community-based services are high enough and should not be expanded further, came the creation of a cyclic process with no end in sight (Sealy & Whitehead, 2004). Thus the increased criminalization of people with mental illnesses caused the criminal justice system to become a point of entry into the mental health system (Teplin, 1984), with jails slowly assuming the role of delivering psychiatric services to poor, mentally disturbed offenders.

In many jurisdictions, jails have come under court order to develop and administer mental health programs for special prisoners. An oft-quoted statistic is that the largest mental institution in the United States is actually the Los Angeles County Jail (Chaimowitz, 2012). This is troublesome, as psychiatric resources within jails range in both quality and scope (Pogrebin & Poole, 1987). Another problematic aspect of deinstitutionalization is that communitybased mental health centers are traditionally geared to provide services to less severely disturbed people who could be treated in a non-institutional setting.

They are not equipped to handle the seriously and chronically ill patients who were previously institutionalized (Pogrebin & Poole, 1987).

Mental Health Courts were the response to this societal problem. These courts were a product of cooperation between criminal justice and mental health systems: they identify the most effective and the least restrictive treatment interventions while providing effective legal advocacy for the mentally ill. MHCs have an ambitious mandate: their aim is to both protect society, while applying the tenets of therapeutic jurisprudence. One of the basic tenets of MHC philosophy is to “respond (...) to crime by seeking to rehabilitate the offender and repair the harm suffered by the victim and community rather than by punishing the offender according to retributive or deterrent principles” (Slate & Johnson, 2008).

A problem with the study of these courts is that they are not standardized across the U.S., and as such there is no single model of a Mental Health Court that is suitable to all communities (Slate & Johnson, 2008). MHCs generally vary in terms of intervention point, use of criminal charges and sanctions, eligibility, plea agreements, supervision, incentives, and completion standards (Slate & Johnson, 2008). However, a few commonalities exist among these courts, such as the goal of diversion of mentally ill people from the criminal justice into treatment instead of jail. MHCs also typically have dedicated personnel who are thoroughly trained in mental illness and its treatment, as well as a psychological framework through which criminal behaviour of the mentally ill is assessed. The panel includes a judge, a prosecutor, a public defender, and caseworkers.

Many describe the courtrooms as a place where impersonal

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justice is given, and therefore operates more like a group therapy room ("Developments in The Law of Mental Illness," 2008). A team approach is the common practice for legal decisionmaking, with MHCs often creating incentives for the individual to adhere to treatment. Occasionally MHCs provide continued supervision via judicial status review hearings, which can range from every week to every other month, until the participant graduates from the treatment programs. These types of judicial and community supervision can depend on both the court and the participant.

Participation in these courts is voluntary and operates on a referral basis. The majority of the courts deal with misdemeanants who committed “quality of life” crimes ("Developments in The Law of Mental Illness," 2008). Redlich et al.

(2005) identified a second generation of mental health courts, where more felony cases were accepted; in these courts, the use of supervision affiliated with the criminal justice, such as the presence of probation officers and use of jail as a sanction for noncompliance, increased.

Nearly all MHCs use the promise of a cleared criminal record or reduced sentence as an incentive for treatment compliance, though the consequence of non-compliance varies.

Lack of Standardization Selection Bias

and

One of the primary flaws of Mental Health Courts is that they lack standardization; therefore, it is very difficult to measure their success or efficacy. These inconsistencies, when combined, show that people with mental illnesses do not necessarily benefit from participating in MHCs. Three issues common within the MHC literature will be discussed: whether a plea is required to participate in the court, selection bias of

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participants, and sanctions for noncompliance.

Some MHCs require a plea in exchange for participation in the court. In a report published by the Bazelon Centre for Mental Health Law, approximately half of them required guilty or no-contest pleas. Courts requiring this do so to ensure that the participants are motivated to accept court treatment, as some courts utilize a pre-adjudication model whereby charges are suspended while participants are in treatment. However, expungements or dismissals of criminal charges are not automatic. Instead an individual has to request it, which is a long, difficult, and cumbersome process. Furthermore, some courts still retain the participants’ records of conviction. As a result, plea requirement has proven to be an effective form of coercion for increasing treatment compliance (Seltzer, 2005). However, this puts these participants at a large disadvantage as they attempt to integrate back to society. Having a criminal record could hamper obtainment of housing and meaningful employment opportunities – stability that is crucial for effective mental health management.

In addition, pressuring a person with a mental illness to plead guilty continues (and sometimes exacerbates) the existing disparities between arrest rates and subsequent jail time for individuals with mental illnesses compared to other defendants. The most unfortunate situation is when a defendant without a mental illness would typically have charges dismissed, whereas people with mental illnesses would have to plead guilty in order to access services and supports that could be provided by the MHC (Seltzer, 2005).

Across the board, Mental Health Courts are inconsistent in the selection of their participants. While a second generation of MHCs is observed to be

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more proactive in hearing felony cases, the majority of courts only hear misdemeanor cases such as the low-level offences of panhandling, public nuisance, or loitering.

In addition to this, courts favour individuals with no prior criminal histories, as they are therefore more likely and more willing to accept treatment and abide by the court’s ruling. This is an important measure of a court’s success, with the rate of successful treatments likely to be higher if the courts show a trend of preferred selection. The success of therapeutic intervention mandated by these metrics is largely predetermined by each individual court’s selection criteria.

Mental health programs are also more likely to accept these participants because there is ample research showing that if these participants want to be treated, then these community-based treatments will work well for them (Wolff, 2002). In summary, the MHCs benefit by taking ‘good risk’ patients on instead of individuals who are more resistant to treatment. This recruitment system is not beneficial for people with mental illnesses who find themselves trapped within the system. Misdemeanors do not usually result in long sentences, and in order to completely fulfill their mandates of helping people who are mentally ill, Mental Health Courts need to accept riskier cases; this acceptance of riskier cases has the potential to truly change a participant’s sentence and mental well being significantly through a shortened sentence or customized treatment plan to help them get better. (At the same time, however, it is important to note that courts that do accept felony cases tend to have a higher rate of transferring participants back to the criminal justice.)

Finally, sanctions for noncompliance stand out as a complication within the Mental Health

Court system. Because each mental health court is so different, there are various schools of thoughts on the use of criminal charges and sanctions in MHCs. For the courts that do use jail as a sanction, deviant participants are typically dismissed from MHCs and returned to regular criminal court processing or to their jail sentence. In fact, MHCs that required their clients to see a judge more frequently were more likely to report using jail as a sanction (Redlich et al., 2006). This is a controversial decision, as returning people to jail for treatment noncompliance is counter to the therapeutic philosophy of MHCs and seemingly punishes people for their mental illness (Redlich et al., 2006).

Many argue that using incarceration as a punishment is a distortion of the underlying maxims of MHCs. Noncompliance itself is a complex issue. Mental health treatment is much more difficult to quantify than drug abuse treatment, which has easily defined measures of compliance and where noncompliance itself is a crime. On the other hand, the success of mental health services is gauged by outcomes and not necessarily by adherence to a specific plan of care (Seltzer, 2005). When a participant is noncompliant, it is very important to consider the causes. In order to ensure that the participants are not being thrown back to the criminal justice system, noncompliance should be assessed to determine “whether [it] was willful, was a symptom of mental health illness, or was an indication for the need to change the treatment plan” (Seltzer, 2005).

Inconsistencies in mental health courts across the nation only serve to further burden mentally ill people who are already part of the criminal justice system.

More often than not, these individuals find themselves being processed through the criminal justice system once again.

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Perpetuating Stigma In 1963, the Community Mental Health Centers and Construction Act was passed in the United States. Based on the philosophy of normalization during the community integration movement of the 1960s, it was believed that equal treatment stands as an integral part of full citizenship: be it insurance coverage, education, housing, employment, or, most significantly for the context of MHCs, criminal processing. Such a stance is congruent with the behavioural philosophy underpinning the Assertive Community Treatment model in which “patients [be] held responsible for their behaviour” (Wolff, 2002). Thus, if people with mental illnesses engage in goaldirected (i.e. voluntary) criminal behavior for which any other citizen would be held responsible, then criminal processing should be applied equally (Wolff, 2002).

Allowing differences among arrestees on the basis of mental illness is a direct violation of the philosophy of normalization. Whether it was intended or not, providing different treatment for offenders who have mental illnesses implies that they are deviant (different from “normal”) arrestees (Wolff, 2002).

In 1999, the U.S. Supreme Court held that unnecessary segregation of people with psychiatric disabilities is illegal regardless of whether it is well intentioned (Olmstead v. L.C.; Stefan & Winick, 2005).

This segregation suggests that it is not normal for people with mental illnesses to make mistakes that have criminal implications. This perpetuates the stigma that is already experienced by the mentally ill community. Instead, this type of differential treatments suggests that it is mental illnesses that should be blamed for some behavior that is deemed deviant, even though many non-mentally ill people engage in similar behavior under similar

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conditions (Wolff, 2002). Labeling these specialty courts as “mental health” courts is in itself contributing to this stigma and focuses public attention to psychiatric issues (Wolff, 2002).

This problem is something that a mental health court in Anchorage, Alaska, is well aware of. While the aims of the Anchorage District Court’s Court Coordinated Research Project initiative to link mentally ill participants with community-based mental health services are similar to those of other MHCs, the court chose not to call itself a “mental health court” to avoid the stigma that might be associated with such a label (Goldkamp & Irons-Guynn, 2000). Susan Stefan, a public interest lawyer and expert in mental disability law, compared this differential treatment to problems facing African American defendants in the criminal justice system today. As she has stated (Stefan & Winick, 2005): If you are treating people unfairly in any system, the solution is not to exclude them from the system, but to change both the criminal justice system and the mental health system, however long and painful a process that may be.

These defendants are often subjected to racial profiling; however, the solution is not to create a separate court system for African Americans, but instead to bring justice to the existing system.

Mental health courts also disenfranchise the mentally ill community by contributing to increased arrest rates within this population, especially those committing minor types of offences (Slate & Johnson, 2008). Redlich et al. (2006) labels this problem as “net-widening,” speculating that the police will arrest more people in order to get them into treatment (it is important to note that as of yet, there is no empirical data to support this claim).

Finally, the stigmatization of the

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individual participants in these courts presents a point of concern.

Confidentiality of medical records of these people is a controversial issue. Competent individuals appearing before the court can sign waivers to allow release of mental health assessments and records to the court for consideration in determining the best way to proceed (Slate & Johnson, 2008). In addition, family members are not bound by doctor-patient privilege and therefore can also provide information to the court and its officers, motivated to do so in order to reduce the sentence or length of treatment. Prior to the hearing medical information is commonly shared between mental health providers used by the court and those seen regularly by the participants. Lastly, interagency computer networks allow crosschecks to be done in order to determine whether jailers have received mental health treatment in the past. This is done in Broward County, where the first MHC originated (Slate & Johnson, 2008). This issue has never really been addressed adequately by MHCs, which is problematic as the participants’ identities and private information are being released, contributing to the stigma they would receive from other members of the society. However, in the interest of fairness it does seem reasonable that the court is made aware of a participant’s mental illness, provided it is not used as a basis for discrimination.

Lack of Resources: A Problem of Supply and Demand With so many competing demands on the public health care budget, psychiatry and the treatment of people with mental illness was – and still is – valued less than other medical or surgical specialties. An example of this bias can be seen in America’s fiscal reduction in mental health programs after deinstitutionalization (Teplin, 1984). The

lack of funding proves to be a problem for the mandates of MHCs, specifically their mandate to help their participants to access community-based treatment and services, giving them an opportunity to utilize the court to integrate and help ensure continued access to care (Slate & Johnson, 2008). This presents many problems on an already inadequate mental health system, resulting in both the decrease of quality and quantity of support for mental health patients inside and outside of the criminal justice system.

In the United States, public mental health systems are facing increasing demands from both the criminal justice system and social services agencies to provide services and support for people with mental illnesses. In 2003, the President’s Commission on Mental Health called for improved access to quality care and services for all Americans with mental illnesses; however, this recommendation did not come with an increase in public expenditures (Sinaiko & McGuire, 2006).

In order to keep up with the pressures of various demands while trying to be costeffective, the public mental health system found itself having to answer to governmental agencies that imposed priorities on which patients should be getting treatment first. Before MHCs, the public health system already received requests from criminal courts; with the arrival of MHCs, the demand was even higher with treatment agreements frequently used as an alternative to incarceration.

MHCs also feature a collaborative team, including a clinical specialist, which handles each case and works to gain access to appropriate clinical services for the participant. This leads to a growing set of policies that use legal and administrative authority to influence a patient’s or a provider’s decision, forcing a certain

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course of treatment (Sinaiko & McGuire, 2006). The policy of prioritizing MHC participants is complex, as it changes the type and intensity of services the public health system could deliver and which patients could receive. First, patients partaking in treatment as part of a mandated program by the MHC were merely added to the caseloads of clinicians who already have patients. Clinicians would have to make room for more patients and attempt to spread the same amount of resources across a larger caseload. This resulted in a reduction in the quality of treatments, creating a dilution effect. The demand from MHCs led to a qualitative decrease in mental health support: with more patients to follow it became more difficult to expect the providers to be as committed to each patient, therefore causing voluntary patients to receive less or a different form of care than would have been otherwise provided (such as changing from private to group therapy sessions). This creates a dilemma: if treatment is administered under the assumption that it protects against criminality, then the decrease in quality of treatment would be expected to increase the population of people with untreated symptoms in the community, creating an increase in the amount of criminality. Ironically, the MHC may be indirectly creating its own demand for participants through its effect on the general treatment system (Wolff, 2002).

Prioritization also creates what is called the waiting list – or queue-jumping – effect. Petrila and colleagues (2001) contend that mental health courts have “intrinsic advantages” in gaining access for patients to treatment, and mental health courts have been found to provide both formal and informal mechanisms to gain priority access to services (Sinaiko & McGuire, 2006). These policies induce

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providers to allocate more of their time and resources to certain patients than they otherwise would, regarding those patients as a priority. Coupled with a lack of designated funding that accompanies these priority patients from the MHC, providers must reallocate their existing resources, and as a result, take away from their nonpriority patients. From this, a priority spectrum is created, the lowest level being patients without any special status, followed by those who are determined by policy makers to be a priority, and lastly, those from the MHCs, whose special status has been clearly defined before entering the system, and for whom a specific set of treatment is already prescribed (Sinaiko & McGuire, 2006).

This is unfair to prior patients within the system, and more so for patients already on the waiting list. Though it would be fair if the offender was already in line to see a clinician, the effect of queue-jumping is that someone who was not previously on the waiting list for services suddenly becomes among the first to receive them (Pierce, 2008). Therefore as a result of court-mandated treatment, the offenders consume resources ahead of nonoffenders. The increased burden on the mental health system as a result of increased popularity of MHCs will cause a decrease in quality and quantity of mental health care, ultimately disenfranchising all mental health patients both in and out of the criminal justice system.

Conclusion There are many flaws of MHC that this paper did not cover, such as whether MHC participants were in a state to make “voluntary, knowing, and intelligent” decisions about entry into these courts (Redlich, 2005). Another concern is having a criminal court-like system administering a social service, begging the question of whether that is a

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violation of the separation of powers or whether these powers should be kept separate at all. There also exists within the literature a discussion of the coercive nature of MHCs, as many courts use a carrot-and-stick approach, where they may use dismissal of charges after successful completion of the MHC program as an incentive to participate in community treatment and avoid recidivism. A major flaw of this is whether MHCs resemble the idea of civil commitment in the 1960s to 1990s, and are themselves coercive in nature. Without proper resources – beyond medication – in place to focus on housing and employment, MHCs may function as a coercive force similar to outpatient commitment. In some instances, MHCs have been formed to provide leverage to coerce medication compliance that outpatient commitment proponents find lacking in civil court (Seltzer, 2005).

However, MHCs are not without successes. It appears that successful completion of such programs is associated with maintenance of reductions in recidivism and violence after graduates were no longer under supervision of the MHC (McNiel, 2007).

This paper has several suggestions for the creation of future MHCs: • Funding needs to be extended to mental health facilities that deal with participants after a treatment plan is devised, guaranteeing the success of treatment plans.

• More funding is required to provide necessary support for individual re-entry into society, such as a post-treatment program clearly established among correctional and mental health staff detailing their respective duties in monitoring the participant in the community after

re-entry (Haimowitz, 2004).

• A more active and better-funded mental health system is necessary for treatments mandated by courts to be carried out in a satisfactory manner. There are also “external benefits” of mental health treatment, such as crime reduction.

(Sinaiko &McGuire, 2006) • Participants agreeing to treatment should be able to participate in the negotiation of the terms of the treatment agreement, instead of only having the court team involved.

• Investment in pre-booking diversion programs or crisis intervention teams is necessary to ensure that more contentious cases are moved to the MHCs. The evolution of Mental Health Courts will continue to be a heated issue in the legal and psychiatric communities.

As more courts are being constructed every day, outcome data must be collected vigilantly to fully evaluate the effectiveness of this system. It is imperative that Mental Health Courts exercise caution when they operate, and some causation may be advisable before systematic implementation. Mental Health Courts must also continue to work at improving upon their flaws to ensure that people with mental illnesses are treated fairly within the criminal justice system. This will ultimately involve investigating the root of the problem: the lack of adequate mental health services in communities across America.

References Chaimowitz, G. (2012).

The Criminalization of People With Mental Illness. Canadian Journal of Psychiatry, (57, 2), Insert, 1-6.

Developments in The Law of Mental Illness (2008). Harvard Law Review,

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1117-1179.

Goldkamp, J.S., & Irons-Guynn, C. (2000).

Emerging Judicial Strategies for the Mentally Ill in the Criminal Caseload: Mental Heath Courts in Fort Lauderdale, Seattle, San Bernardino, and Anchorage. Washington, D.C.: U.S. Department of Justice, Bureau of Justice Assistance, Office of Justice Programs.

Haimowitz, S. (2004). Slowing the Revolving Door: Community Reentry of Offenders With Mental Illness. Psychiatric Services (55,4), 373-5.

McNiel, D., & Binder, R. (2007).

Effectiveness of a Mental Health Court in Reducing Criminal Recidivism and Violence. American Journal of Psychiatry (164,9), 13951403.

Pierce, R. (2008). Queue-Jumping?: Do Mental Health Courts Privilege Criminal Behavior?. Journal of Ethics in Mental Health (3,2), 1-5.

Pogrebin, M. R., & Poole, E.D. (1987).

Deinstitutionalization and increased arrest rates among the mentally disordered. The Journal of Psychiatry & Law (Spring), 117-127.

Redlich, A. D. (2005). Voluntary, but Knowing and Intelligent?

Comprehension in Mental Health Courts. Psychology, Public Policy, and Law (11,4), 605-619.

Redlich, A. D., Steadman, H. J., Monahan, J., Robbins, P.C., & Petrila, J. (2006). Patterns of Practice in Mental Health Courts: A National Survey. Law and Human

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Behavior 30, 347-362.

Sealy, P., & Whitehead, P.C. (2009). Forty Years of Deinstitutionalization of Psychiatric Services in Canada: An Empirical Assessment. Canadian Journal of Psychiatry (49,4), 249-257.

Seltzer, T. (2005). Mental Health Courts: A Misguided Attempt to Address the Criminal Justice System's Unfair Treatment of People With Mental Illnesses. Psychology, Public Policy, and Law (11,4), 570-586.

Shah, Saleem A. (1989). Mental Disorder and the Criminal Justice System: Some Overarching Issues.

International Journal of Law and Pscyhiatry (12), 231-244.

Sinaiko, A. D., & McGuire, T.G. (2006) Patient Inducement, Provider Priorities, and Resource Allocation in Public Mental Health Systems.

Journal of Health Politics, Policy and Law (31,6), 1075-1106.

Slate, R. N., & Johnson, W. W. (2008).

The Criminalization of Mental Illness.

Durham, N.C.: Carolina Academic Press.

Stefan, S., & Winick, B. J. (2005).

Foreword: A Dialogue on Mental Health Courts. Psychology, Public Policy, and Law (11,4), 507-526.

Teplin, L. A. (1984). Criminalizing mental disorder: The comparative arrest rate of the mentally ill. American Psychologist (39,7), 794-803.

Wolff, N. (2002). Courts as Therapeutic Agents: Thinking Past the Novelty of Mental Health Courts. Journal of the American Academy of Psychiatry and the Law (30,3),431-7.

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How Perceived Collective Autonomy and Distinctiveness of Cultural Customs Influence Motivation to Follow These Customs Midori Nishioka

Abstract The present study investigated how perceived autonomy and distinctiveness of a cultural custom influenced group members’ motivation to follow that custom. Motivation to eat a customary Japanese food (“sticky rice”) was assessed in a sample of 22 Japanese undergraduate students. Participants were randomly assigned to four experimental conditions: Autonomy/Distinct, Autonomy/Not Distinct, Controlled/Distinct, and Controlled/Not Distinct. Perceived collective autonomy and distinctiveness were manipulated through articles on Japanese rice in a historical context. The articles depicted Japanese people as either autonomous or controlled in eating rice, and Japanese rice as distinct or not distinct to the culture. Motivation to eat Japanese rice was measured with a questionnaire assessing individual motivation, a free-choice paradigm, and the proportion of rice balls eaten when participants were offered snacks. It was hypothesized that participants in Autonomy conditions will be more motivated than those in Controlled conditions, and participants in Distinct conditions will be more motivated than those in Not Distinct conditions. It was also hypothesized that participants in Autonomy conditions will be more motivated if the custom is perceived to be distinctive rather than not distinctive, and participants in Controlled conditions will be more motivated if the custom is perceived to be not distinctive rather than distinctive. The hypotheses were not supported. However, in the free-choice paradigm, those in Autonomy/Distinct condition were more motivated compared to the other three groups. The study suggests that both perceived collective autonomy and distinctiveness are implicated in motivation to follow cultural customs.

Collective Autonomy, Distinctiveness, and Motivation Our customs, values, and beliefs come from the social group to which we belong (Oyserman, 2007). For instance, our cultural groups serve as a guide for how we should behave and what we should value in life. A cultural group may have shared customs and the members’ motivation to follow that custom can be

affected by several factors. We explored two possible factors: perceived collective autonomy of the group and perceived distinctiveness of a custom.

Individuals may struggle due to the historical events that their group endured.

Disadvantaged groups, such as Aboriginals in Canada, face many social challenges.

High unemployment rates, underachievement in education, and high

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incidences of crime are a few examples of the social challenges they encounter (Taylor, 2002). These social challenges may stem from difficulties in maintaining motivation to follow valued cultural customs.

One important factor that can influence motivation is perceived autonomy. Self-determination theory proposes that autonomy, “a desire to selforganize experience and behaviour,” is a basic need (Deci & Ryan, 2000).

Motivation to follow a cultural custom may be compromised when individuals do not feel autonomous. Individuals will be less likely to follow a custom if they feel coerced to do so. On the other hand, they will be more likely to follow a custom if they feel autonomous in doing so. Studies have shown that autonomy is predictive of motivation in various activities. Iyengar and Lepper (1999) showed that AngloAmerican children performed better in self-selected games than in games that the experimenter chose for them. The authors attribute the value of personal choice among the Anglo-American children to be responsible for the phenomenon; being able to make personal choices supported the children’s feeling of autonomy.

Similarly, in another study, participants who were allowed to allocate the time spent on several puzzles were more motivated to solve more puzzles when left alone without instructions than those who did not have such choices (Zuckerman et al., 1978). Other studies show that trivial or illusory sense of choice also enhances people’s motivation (Langer, 1975; Langer & Rodin, 1976).

Members of disadvantaged groups underachieve as a whole, and more powerful social groups have often undermined their group autonomy. For example, Aboriginal children were forced to attend residential schools, where they

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were taught English and were forced to embrace European culture (Leeuw, 2009).

Aboriginal languages were forbidden.

Now, only three of the 53 aboriginal languages are expected to survive (Fleras & Elliott, 1992). One of the reasons for the underachievement in education among disadvantaged communities may stem from the perception that their group has been coerced into life situations against their will.

Although self-determination theory focuses primarily on the individual, Aboriginals suffered a lack of autonomy as a group and continue to suffer social challenges as a group to this day. Deci and Ryan (2000) outline the role of culture as part of an individual’s environment in affecting motivation.

Furthermore, cultural differences and universality of the need for autonomy have been widely disputed and studied extensively (Chirkov, Ryan, Kim, & Kaplan, 2003). Yet, the theory and related studies do not consider how perceiving their cultural group as autonomous may affect the group members’ motivation to follow the cultural customs.

According to social identity theory (Tajfel, 1978) and Taylor’s theory of selfconcept (Taylor, 1997/2002), individuals derive their identity from the group to which they belong. The social component of the self-concept is built from the information that the social group provides.

Given that the self-concept derives from the group, we can infer that if individuals perceive their group to be coerced by other groups, it is likely that they will feel controlled as individuals. Conversely, if individuals perceive their group to be autonomous, it is likely that they will also feel autonomous as individuals. Their motivation may be subsequently affected, as predicted by self-determination theory.

To our knowledge, the effect of the

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group’s perception of autonomy on the motivation of its members has not been studied. One objective of the present study was to determine whether perceiving a cultural custom as either autonomously chosen by their group or coerced by another group will affect an individual’s motivation to follow that custom. We aimed to determine if perceived collective autonomy of individuals extends to their perceived individual autonomy and affects their individual motivation to follow a cultural custom.

The present study extended the concepts of self-determination theory, an individualistically focused theory, to the collective level. Introducing the role of cultural groups in motivation necessitates that the issue be considered in terms of groups. According to Tajfel (1978), each social group is strongly motivated to be distinct from other groups. For example, some Aboriginal groups demanded to gain a status different from Canadian citizenship. They wished to possess a unique status as the original inhabitants of Canada (Felas & Elliott, 1992). Perceiving that the group is distinctive may enhance members’ motivation to follow their group’s cultural customs. We may be more motivated to follow a custom that is distinct to our culture than to follow a custom that is similar or adopted from another cultural group. The second objective of the present study was to determine how perceiving a cultural custom to be distinctive or not distinctive to the group would affect an individual’s motivation to follow that custom.

Although autonomy seems to play a significant role in individuals’ motivation, when the role of the group is considered, being distinctive as a group may also be important. Collective autonomy and distinctiveness were studied in our experiment to determine the degree to

which each of them contributes to the group members’ motivation to follow a cultural custom. Four combinations of perceived collective autonomy and distinctiveness are possible: 1) autonomous and distinctive, 2) autonomous and not distinctive, 3) controlled and not distinctive, and 4) controlled and distinctive.

Intuitively, if the members of a group feel collectively autonomous in a cultural custom that is distinctive to the group (1), one may predict that their motivation to follow that custom should be high. Cowan’s (2005) study showed that including Inuit art into an adult educational program had beneficial effects on Inuit women’s learning. The women initiated the re-learning of the basketmaking that was fading from the culture; autonomously choosing to learn the distinctive cultural custom seemed to support their motivation in learning.

Collective autonomy in a nondistinctive cultural custom (2) may support individuals’ motivation to follow the custom as well. For example, bannock is a popular food among Aboriginal peoples, yet it originated in Scotland and was introduced to the Aboriginal population by fur traders. The cooking methods of bannock were adapted and modified among Aboriginal people over time (Government of British Columbia, 2011; Minister of Indian Affairs and Northern Development, 1998). Collective autonomy in integrating bannock into the culture may have supported their motivation, despite the origin of bannock.

A group may feel less autonomous when dictated by another group (3). This phenomenon is apparent in colonialism in which the indigenous people were under the control of the colonising group and are told to behave in a way that is different from their original culture. A historical

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example is the way European settlers assimilated indigenous people by forcing them to learn a new language (Fleras & Elliott, 1992). In cases such as this, both the autonomy and the distinctiveness of the group are threatened, undermined, and sometimes ultimately obliterated.

However, when individuals perceive their cultural custom to be non-distinctive, autonomy may not play a large role in motivation, since the custom did not originate from their group. They may not value the custom as much as if it originated within their culture.

What would happen to the members’ motivation if the custom is distinctive to the group, but another group has forced them to follow that custom (4)?

For example, the Department of Indian Affairs and Northern Development, although aiming to respond to the culturally distinctive needs of the Aboriginal communities (Fleras & Elliott, 1992), may have been undermining the autonomy of the communities due to the strong influence of the state. In the view of social identity theory, behaving in a way that is distinctive to the culture should be reinforcing for the group members, regardless of their level of autonomy.

The motivation of group members to follow a distinctive cultural custom may be severely affected when the autonomy of the group is undermined. A distinctive custom may be of greater importance to the self-concept of group members than an adopted custom, thus undermining motivation to a greater degree when another group controls the distinctive custom. This interaction of collective autonomy and distinctiveness on individual motivation may be counterintuitive, since it implies that policies encouraging restoration of a disadvantaged group’s culture may be

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potentially more damaging than assimilation of the group.

To summarize, we hypothesize that individuals who perceive their cultural custom to be autonomously chosen by their group will be more motivated to follow that custom than those who perceive the custom as being forced onto their group by another group. Similarly, individuals who perceive their cultural custom to be distinctive to their group will be more motivated to follow that custom than those who perceive the custom to be non-distinctive to their group.

Furthermore, individuals who perceive their cultural custom to be autonomously chosen by their group will be more motivated to follow that custom, if they also perceive it as distinctive to their group. Conversely, individuals who perceive their cultural custom to be forced onto their group by another group will be more motivated to follow that custom, if they also perceive it as non-distinctive to their group.

Research Context In order to study the effects of collective autonomy and distinctiveness on motivation, Japanese students were selected as the population of interest. We studied the consumption of Japanese “sticky rice” as the cultural custom.

Instead of creating an artificial group in a lab and providing an arbitrary activity, we believed that using a real cultural group with an existing cultural custom would have greater ecological validity.

Rice consumption is portrayed as a culturally important custom in Japan. In 2005, the Japanese government implemented the Basic Law on Food and Nutrition Education. Children are now taught about food safety and nutrition (Government of Japan, 2008). In particular, they are encouraged to eat and learn about Japanese rice. Although rice is

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eaten in many cultures, Japanese sticky rice is symbolic of Japanese culture. Rice cultivation reflects cooperation, which is a culturally valued trait. Concepts related to rice are integrated into the Japanese language. Rice is regarded as a divine symbol associated with the emperor (Kharakwal, Yano, Yasuda, Shine, & Osada, 2004; Wojtan, 1993). Consuming Japanese rice might reflect the greater goal of preserving tradition and culture.

We predicted that the Japanese students who were told that Japanese people autonomously chose rice as their staple food will be more motivated to actually consume Japanese sticky rice, made available in the lab, than those who were told that Japanese people were coerced by Chinese people into eating rice. Similarly, those who were told that eating Japanese rice is distinctive to the Japanese culture will be more motivated to eat sticky rice than those who were told that it derived from Chinese culture.

Furthermore, we predicted an interaction between distinctiveness and autonomy.

Those who were told that the Japanese people autonomously chose rice will be more motivated to eat sticky rice if also told that the custom is distinctive, rather than not distinctive. Conversely, those who were told that Japanese people were coerced will be less motivated if also told that the custom is not distinctive, rather than distinctive.

Method Participants Twenty-two Japanese McGill undergraduate students, 8 males (mean age 20, SD = 1.75) and 14 females (mean age 20, SD = 1.30), were recruited from the Social Psychology Paid Participant Pool, McGill Psychology Human Participant Pool, posters around McGill University campus, and through emails sent to members of the Japanese Students’

Association at McGill University. The study was described as a “food and memory study”, to avoid transparency of actual objectives of the study. All participants were compensated $10 per hour for their participation, or awarded course credit. Through all forms of recruitment it was made explicit that we were only recruiting Japanese participants.

Thirteen participants (59.1 %) reported holding Japanese citizenship and 9 participants (40.9 %) indicated citizenship in other countries, including Canada. Ten participants (45.5 %) reported Japanese, 9 participants (40.9 %) reported half Japanese, and 3 participants (13.6 %) reported other as their ethnic background. When asked, “What is your mother tongue?” 11 participants (50 %) reported Japanese and 11 participants (50 %) reported other, including English, as their mother tongue.

Participants were randomly assigned to four conditions: Autonomy/Distinct condition (n = 6, 2 males and 4 females), Autonomy/Not Distinct condition (n = 6, 1 male and 5 females), Controlled/Distinct condition (n = 4, 2 males and 2 females), and Controlled/Not Distinct condition (n = 6, 3 males and 3 females).

Materials and Procedure The independent variables of the study, perceived collective autonomy and distinctiveness, were manipulated through the content of the articles read by the participants (described below). The dependent variables were 1) self-report scale of individual motivation to eat Japanese rice (adapted from Intrinsic Motivation Inventory (IMI); McAuley, Duncan, & Tammen, 1989; Ryan, 1982), 2) time spent viewing materials on Japanese rice, and 3) proportion of rice balls eaten in the lab.

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Consent from the participants was obtained first. Participants were told that this study was about food and memory and that we were interested in how much information they could remember from reading articles about food. We also included articles about bread and donuts to avoid revealing the study’s actual objective.

Participants were first asked to complete a set of preliminary questionnaires on the computer. The questionnaires included: 1) General Demographics (such as age, gender, ethnic background, country of citizenship, and mother-tongue language), 2) Group Identification Scale (adapted from Cameron, 2004), and 3) a scale to measure current level of hunger (developed by the researchers).

Each participant was given three articles to read. The experimenter instructed the participants to read each article only once, equally paying attention to all the details. The participants were told that they would be given a memory test on the content of the articles. One article was about donuts, another about bread, and another about Japanese rice.

Filler articles on donuts and bread were included to minimize transparency of the study.

The participants read one of four different articles on Japanese rice, depending on the experimental condition to which they were assigned. There were four experimental conditions: Autonomy/Distinct condition, Autonomy/Not Distinct condition, Controlled/Distinct condition, and Controlled/Not Distinct condition. All the articles described historical events (250 BCE – 250 CE) when rice cultivation began in Japan. In the Autonomy/Distinct condition, participants read an article that depicted Japanese people autonomously

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choosing to eat rice and discovering the unique Japanese rice species. In the Autonomy/Not Distinct condition, participants read an article that depicted Japanese people autonomously adopting the rice species that originated in China.

In the Controlled/Distinct condition, participants read an article that depicted a Chinese emperor forcing Japanese people to expand the rice species that originated in Japan. In the Controlled/Not Distinct condition, participants read an article that depicted a Chinese emperor forcing Japanese people to expand the rice species that originated in China.

A memory test on the content of the articles was given. The memory test consisted of six multiple-choice questions.

The memory test was not a dependent variable, but part of the cover story that the experiment is about food and memory.

After the memory test, participants were given a final set of questionnaires.

The final questionnaires contained measures of 1) individual motivation for eating the three foods, 2) perceived collective autonomy of Japanese people for eating the three foods (both scales adapted from IMI; McAuley, Duncan, & Tammen, 1989; Ryan, 1982), and 3) perceived distinctiveness of the three foods in Japanese culture (two items adapted from Wohl and Branscombe (2009) and two items developed by the researchers).

The items on donuts and bread were included in the questionnaires in order to have consistency for the cover story. Only the items on Japanese rice were of interest in this study and were analyzed.

Individual motivation for eating rice was one of our dependent variables in measuring each participant’s motivation.

The 7 items from the “interest and enjoyment” subscale of the IMI was used.

Perceived collective autonomy of Japanese people for eating rice was a measure to

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check whether the manipulation on autonomy was effective. The 7 items from the “perceived choice” subscale of the IMI were reworded to measure perceived collective autonomy. For example, the item that would measure individual autonomy, “I eat rice because I want to,” was adapted to “Japanese people eat rice because they want to.” The 4 items on perceived distinctiveness of Japanese rice in Japanese culture were a measure to check whether the manipulation on distinctiveness was effective. In all of the questionnaires, participants rated their agreement to statements on a scale of (1) “Strongly disagree” to (7) “Strongly agree.”

Participants were then tested on the free-choice paradigm (Zuckerman et al., 1978). The experimenter said, “I will be gone for about 5 to 10 minutes to prepare for the next part of the study. During that time, you can look at pictures, videos, and recipes of the different foods”. The experimenter opened a slide show on the computer that contained the materials on the three foods. The experimenter left the room for 8 minutes, during which the participants were free to view whatever they wished in the computer file. The slide show was made to be interactive.

Participants were able to choose flexibly among the materials on three foods. Their activity on the computer was recorded as a video using HyperCam software. The amount of time the participants spent viewing the rice material during the 8 minutes was one of the dependent variables to measure motivation to eat rice.

After 8 minutes, the experimenter returned with a tray of equally-sized snacks of bread, donuts, and rice. The snacks consisted of three pieces of donuts (“Tim bits” obtained from Tim Hortons), three pieces of bread (micro croissants),

and three pieces of rice balls (a Japanese finger food prepared by the experimenters) per participant. The participants were told to eat as much as they like. The proportion of the number of rice balls eaten relative to total number of food piece eaten was the final dependent variable for motivation to eat Japanese rice.

Participants were then fully debriefed about the nature of the study.

The experimenter carefully explained the deception used in the study and informed participants of the experimenter’s contact information should they have any questions about the study. Participants were compensated for their time and then dismissed.

Analysis Data obtained from the dependent variables (individual motivation, freechoice paradigm, and proportion of rice balls eaten) were analyzed by two-way independent groups analysis of variance (ANOVA), using IBM SPSS Statistics version 20.

Results Conditions and Participants The participants’ self-reports of the country they had lived in during childhood, their typical response to the question “Where are you from?”, their ethnic background, and their mothertongue were coded for correlational analyses (Japan or Japanese = 1.0, Half Japanese = 0.5, Not Japan or Japanese = 0.0). The four conditions in the experiment did not correlate with participants’ self-reports of childhood country, identification, ethnicity, and mother-tongue. A one-way independent groups ANOVA was performed to verify that groups did not differ in group identification. Participants’ scores on group identification scale did not

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significantly differ between the autonomy conditions, F(1, 14) = .55, p = .47, and did not significantly differ between the distinctiveness conditions; F(1, 14) = .93, p = .35. There was no significant interaction effect; F(1, 14) = .03, p = .87 (see Table 1 for mean scores).

Manipulation Check Autonomy: The 7 items measuring the perceived collective autonomy of Japanese people for eating rice was used to check whether the manipulation for autonomy using the articles was effective. This scale was highly reliable (Cronbach’s α = 0.89). A two-way independent groups ANOVA was performed to examine whether the manipulation for autonomy affected, in the aimed direction, each participant’s perceived collective autonomy of Japanese people for eating rice. The extent to which autonomy manipulation (autonomy, controlled) and distinctiveness manipulation (distinct, not distinct) affected participants’ perceived collective autonomy was analyzed. There was no

Figure 1. Autonomy and Distinctiveness Conditions in Free-choice Paradigm.

Mean time spent viewing rice materials during 8-minute period (seconds). Error bars represent one standard error of the mean.

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significant main effect of autonomy conditions; F(1, 17) = .12, p = .73.

However, the groups significantly differed on distinctiveness conditions; F(1, 17) = 6.27, p < .05. The participants in the Distinct conditions (M = 5.64, SD = 1.21) perceived Japanese people as more autonomous in eating Japanese rice than the participants in the Not Distinct conditions perceived them to be (M = 4.42, SD = .99). There was no significant interaction of autonomy and distinctiveness conditions; F(1, 17) = 1.76, p = .20 (see Table 1).

Distinctiveness: The 4 items to measure the participants’ perceived distinctiveness had reasonable reliability (α = .70). A two-way independent groups ANOVA was performed to examine whether the manipulation for distinctiveness affected, in the aimed direction, the participants’ perceived distinctiveness of Japanese rice in Japanese culture. The extent to which autonomy manipulation (autonomy, controlled) and distinctiveness manipulation (distinct, not distinct) affected participants’ perceived distinctiveness was analyzed. There was no significant difference between the distinctiveness conditions; F(1, 18) = .18, p = .68. There was no significant difference between the autonomy conditions; F(1, 18) = .71, p = .41. There was no interaction of autonomy and distinctiveness conditions; F(1, 18) = .59, p = .41 (see Table 1).

Individual Motivation for Eating Rice The group of 7 items measuring individual motivation for eating rice was a dependent variable measuring motivation to eat rice. This scale was reliable (α = .79). Scores were obtained from 20 participants. A two-way independent groups ANOVA for autonomy (autonomy, controlled) and distinctiveness (distinct, not distinct) conditions on individual motivation for eating rice did not yield a

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significant effect of autonomy; F(1, 16) = .12, p = .73. There was no difference between distinctiveness conditions, F(1, 16) = 1.78, p = .20, and no interaction of autonomy and distinctiveness; F(1, 16) = .07, p = .79 (see Table 1).

Autonomy Key Outcomes Group Identity

Collective Autonomy Check

Distinctiveness Check

Individual Motivation

Free-choice Paradigm

Proportion Rice Balls Eaten

Controlled

Distinct

Not Distinct

Distinct

Not Distinct

M

5.51

5.11

5.97

5.40

SD

1.18

1.19

.73

.83

M

5.83

3.97

5.36

4.79

SD

1.01

.85

1.56

1.00

M

6.25

6.42

6.25

5.75

SD

.65

.66

.54

1.41

M

5.86

6.23

5.64

6.20

SD

.85

.54

1.05

.59

M

294.38

169.48

188.25

190.17

SD

45.23

41.29

50.57

41.29

M

.64

.41

.35

.56

SD

.43

.10

.03

.34

Table 1. Mean Values and Standard Deviations of Key Outcomes from Each Experimental Condition. Group Identity, Collective Autonomy Check, Distinctiveness Check, and Individual Motivation: means on 7-point Likert scale. Free-choice Paradigm: mean seconds during 8-minutes. Proportion Rice Balls Eaten: mean proportion of rice balls eaten relative to total food eaten.

Free-choice Paradigm A two-way independent groups ANOVA was performed to examine the effects of autonomy (autonomy, controlled) and distinctiveness (distinct, not distinct) manipulations on the mean time spent viewing materials related to rice in the free-choice paradigm.

Twenty-one

participants completed the free-choice paradigm. There was no significant effect of the autonomy manipulation on the mean time spent viewing rice materials; F(1, 17) = .91, p = .35. There was no significant effect of the distinctiveness manipulation on the mean time spent viewing rice materials; F(1, 17) = 1.89, p = .19. There was no significant interaction effect of autonomy and distinctiveness on the mean time spent viewing rice materials; F(1, 17) = 2.01, p = .18 (see Table 1).

When the means of the four groups on time spent viewing rice materials were plotted graphically (see Figure 1), it seemed that the participants in the Autonomy/Distinct condition spent longer time viewing rice materials, compared to the other three groups (Autonomy/Not Distinct, Controlled/Distinct, and Controlled/Not Distinct). Standard error of the means of these three groups seemed to overlap largely, but no overlap is seen with Autonomy/Distinct condition.

The researchers speculated that the groups in which at least one of the needs (need for autonomy or need for group distinctiveness) was threatened might have lower motivation than the group in which both needs were fulfilled.

Three conditions (Autonomy/Not Distinct, Controlled/Not Distinct, Controlled/Distinct) were aggregated to form one group entitled “threat condition.” The threat condition was compared to the “non-threat condition” (Autonomy/Distinct condition), in which both the need for autonomy and the need for distinctiveness were fulfilled (see Figure 2).

A one-way independent groups ANOVA was performed to examine the difference between the threat and nonthreat conditions on time spent viewing

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rice materials. The participants in the non-threat condition (M = 294.38, SD = 116.65) spent significantly longer time viewing rice materials than those in the threat condition (M = 181.93, SD = 89.79); F(1, 19) = 5.22, p < .05 (see Table 2).

same pattern as the results from freechoice paradigm (see Figure 3). However, ANOVA could not be performed to examine the difference between the conditions, since the error variance across the groups was not equal on Levene’s test; F = 7.46, p < .05. In addition, logarithm and inverse transformations on this data did not resolve the violation of homogeneity of variance. A MannWhitney U test was conducted to evaluate the differences between threat and nonthreat conditions. The result of the test was not significant; z = -1.05, p = .34.

Figure 2. Non-Threat and Threat Conditions in Free-choice Paradigm. Mean time spent viewing rice materials during 8-minute period (seconds).

Bars represent means and error bars represent one standard error of the mean.

Key Outcomes Free-choice Paradigm

Proportion of Rice Balls Eaten

Non-threat

Threat

M

294.38

181.93

SD

116.65

89.79

M

.64

.45

SD

.43

.22

Table 2. Mean Values and Standard Deviations of Free-choice Paradigm and Proportion of Rice Eaten from Non-threat and Threat Conditions.

Free-choice Paradigm: mean seconds during 8minutes. Proportion Rice Balls Eaten: mean proportion of rice balls eaten relative to total food eaten.

Proportion of Rice Balls Eaten Participants were offered three pieces each of donuts, bread, and rice balls. Proportion of rice balls eaten relative to total food eaten from all 22 participants was obtained for this measure.

Graphically, the means on threat and nonthreat conditions seemed to follow the

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Figure 3. Non-threat and Threat Conditions in Proportion of Rice Eaten. Mean proportion of number of rice balls eaten relative to total number of food piece eaten. Bars represent means and error bars represent one standard error of the mean.

Discussion Previous research has focused on how the perception of individual autonomy affects individual motivation.

The present study explored the effect of individuals’ perceived collective autonomy of their cultural group on their individual motivation.

Social identity theory postulates that each group is motivated to maintain a distinct collective identity.

Since the present study is placed in an intergroup context, perceived

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distinctiveness on individual motivation was also explored.

Autonomy and Distinctiveness Manipulations Before interpreting the results from the measures of motivation, the fact that manipulations in the study may not have been effective should be addressed.

Autonomy conditions did not significantly differ on the measure for perceived collective autonomy.

However, distinctiveness conditions significantly differed on this measure. Neither autonomy nor distinctiveness conditions differed on the distinctiveness scale. Since the questionnaires for collective autonomy and distinctiveness had moderate to high reliabilities (internal consistencies), it is possible that due to small sample size, the effect of manipulations were obscured by large individual differences. However, further interpretations are possible: either (a) the manipulations were not effective, or (b) since the manipulation checks were self-report measures, the effects of the manipulations were not detected.

The articles may not have been effective in manipulating perceived autonomy and distinctiveness. Firstly, the researchers believed that the scenarios of Japanese people being autonomous or controlled and distinct or not distinct in the historical context (Yayoi period, 250BCE – 250CE) would be credible.

However, the participants may not have identified themselves with the Japanese people in Yayoi period. Given that the sample was of students in an international university and that the participants live outside of Japan, the effect of the articles depicting Japanese history may have had little influence. Secondly, although Chinese influence on Japanese culture is relevant and plausible, the present study did not verify whether modern young Japanese adults would perceive China as a threat to Japan. The attempts to maintain

credibility in the articles may have negatively affected the strength of the manipulations.

A larger issue in the independent variables of the current study lies in the difficulty of separating autonomy and distinctiveness. Since each article had to make logical sense, features of autonomy and distinctiveness were interrelated.

Moreover, the levels within each factor may not have been equal. For example, while the Autonomy/Distinct article did not mention China extensively, the Autonomy/Not Distinct article depicted Japanese people autonomously learning from Chinese culture. In this case, “autonomy” is not depicted equally in the two conditions. However, it is also possible that the participants’ senses of autonomy and distinctiveness were not affected independently of each other. People might commonly assume that distinctiveness (uniqueness, authenticity, originality) implies autonomy, and vice versa. The significant effect of distinctiveness threat on the manipulation check for collective autonomy may be explained by this ambiguity.

If the manipulations were effective, the manipulation check may not have detected the effect. Social identity theory proposes that each social group is strongly motivated to be distinctive (Tajfel, 1978).

Participants in the Not Distinct conditions may not have been willing to report that their cultural custom is not distinctive.

Participants in all conditions perceived their group as distinct, as the mean scores of distinctiveness were generally high, ranging from M = 5.75, SD = 1.41 (Controlled/Not Distinct) to M = 6.42, SD = .66 (Autonomy/Not Distinct), out of a maximum possible mean score of 7.

In the following discussion of the results, the general limitations of the manipulations and sample size should be

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kept in mind. The analyses of the results are primarily exploratory, given these limitations and the new methodologies employed.

Individual Motivation We hypothesized that the groups that read about Japanese people being autonomous in choosing to eat and cultivate rice (Autonomy conditions) would score significantly higher on the scale of individual motivation, compared to the groups that read about Japanese people being forced to eat and cultivate rice (Controlled conditions). Contrary to the hypothesis, there was no significant difference between Autonomy and Controlled conditions.

We also hypothesized that the groups that read about Japanese rice being distinctive (Distinct conditions) would score higher on the scale of individual motivation, compared to the groups that read about Japanese rice not being distinctive (Not Distinct conditions). Again, there was no significant difference between conditions.

Furthermore, the hypothesis that there would be an interaction between autonomy and distinctiveness did not appear significant.

Free-choice Paradigm The researchers hypothesized that the participants in Autonomy conditions would spend longer time viewing rice materials than the participants in Controlled conditions. Similarly, it was hypothesized that the participants in Distinct conditions will spend longer time than the participants in Not Distinct conditions.

In addition, it was hypothesized that there would be an interaction of autonomy and distinctiveness.

Contrary to the hypotheses, there were neither significant main effects nor an interaction effect.

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Interestingly, despite the lack of significance in the initial analysis, the participants in the Autonomy/Distinct condition seemed to spend longer time viewing rice materials than the other three groups. As expected, when this condition, the “non-threat” condition, was compared to the aggregated group, the “threat” condition, the two groups were significantly different. Both autonomy and distinctiveness may play a role in influencing motivation. A threat to either one or both of autonomy and distinctiveness seemed to undermine motivation. Perceived distinctiveness of cultural customs has not been implicated in individual motivation in previous studies; our results suggest that in the context of pursuing cultural customs, perceived distinctiveness of the group may be equally important as perceived autonomy of the group.

However, these analyses were exploratory. Aggregating the four conditions to two groups increased statistical power, resulting in significant difference between the two; this may also be the explanation for the lack of significance when the four conditions were compared.

Proportion of Rice Balls Eaten Proportion of rice balls eaten by the participants could not be substantially analyzed due to violation of the assumption of ANOVA. However, a similar pattern of results from the freechoice paradigm may be seen when conditions are aggregated to threat and non-threat conditions. This method was exploratory; the researchers believed that measuring the behaviour of eating Japanese rice reflects motivation in an ecologically valid manner. Limitations may include inconsistencies in the quality of food and individual differences in preference for each food (donuts, bread,

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and rice). If taste and quality of the foods are controlled and participants’ preferences for the foods are measured, this method may be useful in future research.

General Implications and Future Direction Although the current study is not conclusive, some implications can be drawn. The most interesting result was that there was a suggestion that the perceived distinctiveness of the cultural custom has an impact on individual motivation. Furthermore, the effect of perceived collective autonomy on individual motivation is suggested.

Perceived collective autonomy and distinctiveness are likely to be especially important factors affecting motivation among disadvantaged communities. Many studies employing self-determination theory have explored the role of autonomy between individuals with unequal power.

For example, controlling or autonomysupporting behaviours of managers towards their subordinates (Deci, Connell, & Ryan, 1989) and teachers towards their students (Deci, Schwartz, Sheinman, & Ryan, 1981) have been studied. In the intergroup context, the study of power differences between groups has been a major issue for disadvantaged communities, whose autonomy and distinctiveness of culture have been undermined. Despite this reality, research into how an individual’s perception of their group's autonomy and distinctiveness affects their individual motivation has not been studied extensively. Future research into this area is clearly necessary.

Conclusion The present study investigated the roles of perceived collective autonomy and distinctiveness of a cultural custom in affecting the group members’ motivation to follow that custom. Japanese people were studied as the population of interest;

the cultural custom of eating Japanese rice was studied. Perceived autonomy and distinctiveness of eating rice were manipulated through articles on the Japanese history of rice. Motivation to eat Japanese rice was measured through the individual motivation scale and time spent viewing materials related to Japanese rice during the free-choice paradigm. The proportion of rice balls eaten by the participants was the final measure of motivation to eat rice. The researchers’ hypotheses, a main effect of autonomy, main effect of distinctiveness, and an interaction effect of autonomy by distinctiveness, were not supported.

However, the results from free-choice paradigm showed significant difference between the condition in which both needs of autonomy and distinctiveness were fulfilled and the conditions in which only one of the needs were threatened.

Limitations were discussed with primary focus on the sample size and the effectiveness of the manipulations. The present study gives insight into both perceived collective autonomy and distinctiveness as factors influencing motivation in cultural customs. Further research into the area of collective autonomy, distinctiveness, and motivation is suggested due to its significance in social challenges faced by disadvantaged communities.

References Cameron, J. E. (2004). A three-factor model of social identity. Self and Identity, 3, 239-262.

Chirkov, V., Ryan, R., Kim, Y., & Kaplan, U. (2003). Differentiating autonomy from individualism and independence: A self-determination theory perspective on internalization of cultural orientations and wellbeing. Journal of Personality and Social Psychology, 84(1), 97-110.

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Cowan, C. (2005). Re-learning the traditional art of Inuit grass basketmaking.

Convergence, 38(4), 51-67.

Deci, E. L., Connell, J. P., & Ryan, R. M. (1989). Self-determination in a work organization. Journal of Applied Psychology,74(4), 580-590.

Deci, E. L., & Ryan, R. (2000). The “what” and “why” of custom pursuits: Human needs and the self-determination of behaviour.

Psychological Inquiry, 11(4), 227-268.

Deci. E. L., Schwartz, A. J., Sheinman, L., & Ryan, R. M. (1981). An instrument to assess adults’ orientations toward control versus autonomy with children: Reflections on intrinsic motivation and perceived competence. Journal of Educational Psychology, 73, 642-650.

Fleras, A., & Elliott, J. (1992). The nations within. Toronto: Oxford University Press.

Government of British Columbia. (2011).

Bannock awareness.

Government of Japan. (2008). Basic plan for the promotion of education (provisional translation).

Iyengar, S., & Lepper, M. (1999).

Rethinking the value of choice: A cultural perspective on intrinsic motivation. Journal of Personality and Social Psychology, 76(3), 349-366.

Kharakwal, J., Yano, A., Yasuda, Y., Shine, V., & Osada, T. (2004). Cord impressed ware and rice cultivation in South Asia, China and Japan: Possibilities of inter-links.

Quaternary International, 123125, 105-115.

Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32, 311-328.

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Langer, E. J., & Rodin, J. (1976). The effects of choice and enhanced personal responsibility for the aged: A field experiment in an institutional setting. Journal of Personality and Social Psychology, 34, 191-198.

Leeuw, S. (2009). ‘If anything is to be done with the Indian, we must catch him very young’: Colonial constructions of Aboriginal children and the geographies of Indian residential schooling in British Columbia, Canada.

Children’s Geographies, 7(2), 123-140.

McAuley, E., Duncan, T., & Tammen, V. V. (1989). Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: A confirmatory factor analysis. Research Quarterly for Exercise and Sport, 60, 48-58.

Minister of Indian Affairs and Northern Development (1998). Chances are, it’s Aboriginal! A conversation about Aboriginal foods.

Oyserman, D. (2007). Social identity and self-regulation. In A. Kruglanski & T. Higgins (Eds.), Handbook of social psychology, (2nd ed., pp. 432453). New York: Guilford Press.

Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43(3), 450- 461.

Tajfel, H. (1978). Differentiation between social groups: Studies in the social psychology of intergroup relations. London, UK: Academic Press.

Taylor, D. M. (1997). The quest for collective identity: the plight of disadvantaged ethnic

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minorities. Canadian Psychology, 38, 174-189.

Taylor, D. M. (2002). The quest for identity: from minority groups to generation Xers.

Westport, CT: Praeger.

Wohl, M. J. A., & Branscombe, N. R. (2009). Group threat, collective angst, and ingroup forgiveness for the war in Iraq. Political Psychology, 30(2), 193-217.

Wojtan, L. (1993). Rice: It's more than just a food. Japan Digest.

Zuckerman, M., Porac, J., Lathin, D., Smith, R., & Deci, E. (1978). On the importance of selfdetermination for intrinsicallymotivated behavior. Personality and Social Psychology Bulletin, 4(3), 443446. Â

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The Effect of Alcohol on Behavioural Inhibition of HighImpulsive and Low-Impulsive Individuals Stephanie Scala Supervisors: Dr. R.O. Pihl & Dr. A. Dagher

Abstract Background: Previous research on alcohol and impulsivity has resulted in conflicting results. This is in part due to a lack of a unified definition of impulsivity among the scientific community.

Objective: The study had two main objectives: 1) further validate and test a gambling paradigm under alcohol intoxication, and 2) examine which behavioural measures of impulsivity show differences between high- and low-impulsive subjects in a placebo and an alcohol condition.

Methods: Ten healthy male participants were divided into high- and low-impulsive groups based on their scores on the Barratt Impulsiveness Scale. Participants completed the Balloon Analogue Risk Task, a gambling task, and the Go/No-Go task in a placebo and an alcohol condition in which they received a dose of 0.8g of alcohol/kg of body weight.

Results: A trend towards significance for a main effect of alcohol on all three experimental tasks was found. Scores on the Barratt Impulsivity Scale were significantly correlated with the Substance Use Risk Profile scales of Impulsivity and Sensation Seeking.

Conclusion: Our findings contribute to the existing literature that alcohol increases behavioural disinhibition, especially motor impulsivity. More research is needed to uncover the underlying mechanisms of impulsivity and better understand how alcohol exerts its effects on these mechanisms, resulting in more impulsive behaviour in humans.

Alcohol has been a part of human history for the past nine thousand years (Kan, 2010). Numerous risky behaviours, such as unprotected sex, crime, and motorized vehicle accidents, are associated with alcohol consumption (Calhoun, Pekar & Pearlson, 2004; Cooper, 2002; Murdoch, Pihl & Ross, 1990). These types of behaviours are thought to result from alcohol’s causation of impulsive behaviour; alcohol has been shown to reduce inhibition, which in turn leads to more impulsive behaviour in both humans

(Critchlow, 1986; Steele & Southwick, 1985) and rats (Poulos, Parker & Lê, 1998).

Previous studies have reported an association between elevated levels of impulsivity and alcohol use disorders in both adolescents and adults (Benjamin & Wulfert, 2005; Nagoshi, Wilson & Rodriguez, 1991; Soloff, Lynch & Moss, 2000). Moreover, the severity of patients’ dependency on alcohol is positively correlated with their level of impulsivity (Irwin, Schuckit & Smith, 1990).

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Impulsivity has also been identified as a central component in the diagnosis of disorders like pathological gambling, ADHD, kleptomania, and antisocial personality disorder (American Psychiatric Association Committee, 2000; Cloninger, Sigvardsson & Bohman, 1988; Schachar, Tannock & Logan, 1993). These findings supplement a recent study by Ersche et al.

(2010), which suggests that impulsivity is an endophenotype for drug addiction.

Since impulsivity is a multidimensional construct, a unified definition of impulsivity is difficult to establish within the scientific community. Thus, the scientific community lacks conceptual clarity in regard to impulsivity. This fact causes confusion in the literature on impulsivity. Many studies have found conflicting results regarding impulsive behaviour. This is in part due to the fact that little is known about the underlying mechanisms of impulsivity and to the wide variety of theoretical approaches used to study the construct of impulsivity (i.e. behavioural, personality, and cognitive theories).

The different theoretical approaches used to study the construct of impulsivity have also led to low intercorrelations among both self-reported measures and behavioural measures of impulsivity (Parker & Bagby, 1997; White et al., 1994). These low intercorrelations can be attributed to methodological oversights of the studies (e.g. small samples, using measures that were not standardized for the populations under study), which assess the relationships between measures of impulsivity (Parker & Bagby, 1997).

Despite a diversity in measurements and definitions of impulsivity, empirical studies of impulsivity have focused on motor and cognitive impulsivity which have been

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found to be two major components of impulsivity (Ainslie, 1975; Brunner & Hen, 1997; Patton, Stanford & Barratt, 1995; White et al., 1994). Motor impulsivity involves the inability to inhibit a response, whereas cognitive impulsivity involves inaccurate judgment of possible outcomes, which results in loss of reward over time (for instance, the preference of small, immediate rewards over larger, delayed rewards) (Brunner & Hen, 1997). The majority of studies conducted in this field examine only one aspect of impulsivity, namely motor impulsivity (de Wit, Crean & Richards, 2000; Finn, Justus & Mazas, 1999).

Previous laboratory studies on alcohol and impulsivity have provided evidence that alcohol reduces inhibition in human behaviour (Finn et al., 1999; Horn, Dolan, Elliott, Deakin & Woodruff, 2003), but the literature on cognitive impulsivity is more complex. There have been mixed results concerning what doses of alcohol cause cognitive impulsivity in humans, and in which tasks these behavioural effects can be observed (Ortner, MacDonald & Olmstead, 2003; Reynolds, Richards & de Wit, 2006; Richards, Zhang, Mitchell & de Wit, 1999). For instance, Ortner et al.

(2003) reported that intoxicated subjects, with a blood alcohol level (BAL) of 0.7g of alcohol/kg of body weight, discounted delayed rewards at lower rates than sober participants. A study by Reynolds et al.

(2006) reported that after receiving a dose of 0.8g of alcohol/kg of body weight, participants performed more impulsively on the Experiential Discounting Task (EDT) (i.e. contradicting Ortnerâ&#x20AC;&#x2122;s findings). However, the tasks used by Ortner and Reynolds to assess delayed discounting were different. The Delay Discounting Task used by Ortner is a commonly used measure of cognitive impulsivity, but it has been criticized for

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being too hypothetical and not exposing participants to choice outcomes during testing. Reynolds, on the other hand, used the EDT task, a real-time task, in which participants experience the consequences or benefits of their decisions. However, the EDT is not solely a delay-discounting measure; it contains other aspects, such as a probability component, and does not have intervals between trials. The differences between these two behavioural tasks may account for Ortner and Reynold’s contradictory findings.

One of the goals of the present pilot study was to contribute to the ecological validity of a computerized gambling task created by Dr. Alain Dagher. The gambling task, called Acey Deucey, assesses loss aversion by fitting the prospect theory model to subjects’ responses (Tversky & Kahneman, 1992).

Loss aversion is defined as the tendency to prefer avoiding losses to acquiring equivalent gains. Risk seeking and impulsivity are related to loss aversion even at the neuronal level. A previous study found that individuals who are less loss averse (i.e. those who take more risks) show decreased neural sensitivity to losses (as well as gains) compared to those who are more loss averse (i.e. less risky) (Tom, Fox, Trepel & Poldrack, 2007). Loss aversion has been observed in children as young as five years of age (Harbaugh, Krause & Vesterlund, 2001), and has been consistently observed in non-laboratory settings (Genesove & Mayer, 2001; Hardie, Johnson & Fader, 1993; Kube, Maréchal & Puppe, 2011).

To date, only a small number of studies have used the Acey Deucey task.

Previous unpublished studies conducted by the Dagher lab have found that pathological gamblers and Parkinson’s patients with problem gambling display decreased loss aversion (i.e. increased risk

seeking) compared to healthy controls.

Another unpublished study by the Dagher lab demonstrated that dopamine depletion resulted in a more conservative betting strategy among participants (i.e. it led to an increase in loss aversion scores).

Although a previous study by the Dagher lab used the Acey Deucey task in a dopamine depletion paradigm, the current pilot study is the first study to examine participants’ responses on the Acey Deucey task in an alcohol/placebo paradigm. Since impulsivity is closely connected to problem gambling (VerdejoGarcia, Lawrence & Clark, 2008), highimpulsive subjects were expected to have lower loss aversion scores than lowimpulsive subjects. We also hypothesized that both groups of subjects would show a decrease in loss aversion scores after consuming alcohol.

The second purpose of the current study was to examine which tasks produce differences between alcohol-intoxicated high- and low-impulsive subjects. The tasks, which show differences between high- and low-impulsive participants, were then used in an fMRI study that examined the neural correlates of impulsivity and how alcohol affects these neural correlates in high- and low-impulsive individuals. We hypothesized that high-impulsive subjects would significantly differ from lowimpulsive subjects even in the placebo conditions. Both motor and cognitive impulsivity were measured in counterbalanced placebo (in which the participants had a BAL of 0) and alcohol (in which participants were given a dose of alcohol to yield a BAL of 0.8) conditions.

Based on the conclusions reached by previous studies according to which alcohol increases error rates on the Go/No-Go task, we expect that highly impulsive subjects will have a greater error rate on the Go/No-Go task compared to

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low-impulsive subjects (Dougherty, MarshRichard, Hatzis, Nouvion & Mathias, 2008).

Materials and Methods Participants Ten healthy male social drinkers (aged 19 - 26, mean = 21.4, SD = 2.7) were recruited for the study by online advertisements and fliers placed around Montreal. Females were not tested due to the potential consequences of consuming alcohol when pregnant and because hormones affect alcohol metabolism at different points during the menstrual cycle (Thomasson, 2002). Before coming into the lab, subjects were screened through online questionnaires to make sure they met the criteria for participation.

Screening questionnaires included: the short form of the Michigan Alcohol Screening Test (SMAST), the South Oaks Gambling Screen (SOGS), the Anxiety Sensitivity Scale (ASI), the Substance Use Risk Profile Scale (SURPS), and the Barratt Impulsiveness Scale (BIS-11).

Scores of 7 or higher on the MAST, 5 or higher on the SOGS, 12 or higher on the Anxiety Sensitivity subscale of the SURPS, and 11 or higher on the ASI resulted in exclusion from the study. The BIS-11, which encompasses the areas of attentional, motor, and non-planning impulsivity, was used to divide subjects into high- and low-impulsive groups. Our cut-off scores for the BIS-11 were based on a review of the BIS by Stanford et al.

(2009). A maximum score of 51 was required for inclusion into the low impulsivity group and a minimum score of 72 was required for inclusion into the high impulsivity group.

On the first day of testing, subjects completed the short form of the Structured Clinical Interview for DSM Disorders (SCID) to rule out any clinical

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disorders. Intelligence was also assessed using the vocabulary and block components of the Weschler Adult Intelligence Scale (WAIS-IV). The SelfOrdered Pointing Task was used to assess participants’ working memory.

Information about subjects’ family histories of alcoholism was also collected in order to explain possible variations between data obtained from subjects having a positive family history of alcohol Low Impulsive (n = 5)

High Impulsive (n = 5)

Mean

SD

Mean

SD

Age

22.6

3.4

20.2

1.1

Average drinks per week

4.7

3.4

7.8

7.2

BIS total score

48.8

1.8

76.8

5.8

BIS nonplanning

19.2

3.7

29.2

3.1

BIS attention

11.8

2.2

18.8

1.6

BIS motor

17.8

3.6

28.8

2.9

SURPS IMP

7.8

2.6

12.4

3.1

SURPS SS

19.8

6.5

22.6

4.7

SURPS H

11.8

5.5

13.2

4.1

SURPS AS

7.8

1.1

9

1

Table 1. The mean and standard deviation (SD) for age, average number of weekly alcoholic drinks, and scores on the BIS and SURPS (IMP = impulsivity, SS = sensation seeking, H = hopelessness, and AS = anxiety sensitivity) of the high- and low-impulsive subject groups.

compared to those who do not. Previous studies have shown that those with a family history of alcoholism experience alcohol differently. Men at a higher risk of developing alcoholism (i.e. those having a positive family history of alcoholism) report statistically significant attenuated feelings of intoxication, which subsequently lead to less intense changes

Psi Ψ Issue III March 2013

in psychomotor test performance and hormonal reactions to ethanol (Schuckit, 1988).

Exclusion criteria included an unhealthy BMI (i.e. one that is not between 19-27), a history of medical or psychiatric illness, currently taking prescription medication, a gambling or illicit drug use problem, unfamiliarity with the alcohol dose administered, and unfamiliarity with how card games work (i.e. that a King is higher than a Jack). The McGill Medicine Institutional Review Board approved the study, and all participants gave written informed consent. Participants were told not to drink any alcohol and not to take any recreational drugs the day before testing, and not to eat anything two hours before testing. Participants received $90 for their participation and were fully debriefed after completing their second testing session.

Dependent Measures Acey Deucey: This is a computerized card gambling task created

by Dr. A. Dagher on MATLAB software.

Participants have to place a bet (either $1 or $3), using the numeric keys ‘1’ and ‘3,’ on whether the face value of a card drawn from a deck will fall between the values of two cards displayed on a screen. The actual odds that the middle card will fall between the turned up pair of cards are displayed at the top of the screen. After each bet is placed, the card in the middle is turned over after a delay period of five seconds and either the word “win” or “lose” is displayed. The participant’s total amount of money is updated on the right side of the screen, corresponding to either a win or a loss of the money wagered ($1 or $3) after each trial. Participants were told that the cards were dealt randomly and were reshuffled after each hand. The dependent variables examined were a participant’s loss aversion score and the

consistency of a participant’s betting strategy (i.e. does the participant consistently wager $1 for every trial in which there is a 20% chance of winning?).

Lower scores of loss aversion and consistency indicate more impulsive and risky behaviour.

Figure 1. Screenshot of the Acey Deucey task.

Go/No-Go: In this computerized task, a series of white, two-digit numerical stimuli are displayed on a black background. The goal of the task is for the participant to learn to discriminate stimuli that demand a mouse click (Go stimuli) from those that require the participant to withhold his or her response (No-Go stimuli) (Nosek & Banaji, 2001).

Participants are required to learn by trialand-error which stimuli are rewarded (i.e. responding to a Go stimulus) and which are punished (i.e. responding to a No-Go stimulus). Eight numerical stimuli (four rewarding and four punishing) are presented in random order. Participants have to complete eighty trials (each stimulus is presented ten times). Each stimulus remains on the screen until the participant responds, or for up to three seconds. After each response, the participant’s updated score is presented (a correct response results in an increase of 0.1 and an incorrect response results in a decrease of 0.1). Omission errors (i.e. when a person fails to respond to a correct stimulus) are believed to reflect inattention (Trommer, Hoeppner, Lorber, & Armstrong, 1988), but were included with commission errors (i.e. responding to an

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Psi Ψ Issue III March 2013

incorrect stimulus) as the dependent variables in our study.

Balloon Analogue Risk Task (BART): The BART task is a computerized task which assesses an individual's risk-taking propensity (Lejuez et al., 2002). We downloaded the task from the University of Maryland’s Psychology Department’s website (http://www.addiction.umd.edu/CAPER WebSite/downloads.html). The goal of the task is to win as much money as possible by pumping up balloons. Each pump earns the participant a certain amount of money (for our study, participants were awarded 5 cents per pump), but after every pump there is a chance the balloon may explode. If this occurs, the individual loses all the earnings associated with that specific balloon. The participant can decide to stop pumping a balloon at any time, and bank their earnings during each trial; earnings from each trial accumulate for a grand total.

The more trials completed, the greater the risk of popping a balloon. Participants in our study completed 30 trials of this task.

The dependent variables for this task were: the amount of times that the balloon popped during the 30 trials, and the average number of times that a participant pumped up the balloon in trials in which no explosion occurred.

Questionnaires: In addition to behavioural tasks, a number of questionnaires were administered to assess mood and personality traits, both of which have the potential to impact the results of the study. The Profile of Mood States (POMS), the Subjective High Assessment Scale (SHAS), and the Biphasic Alcohol Effects Scale (BAES) were completed multiple times by participants during testing. The Drinking Expectancy Questionnaire (DEQ) was also given at the beginning of the first testing session, before

88

the participant received alcohol or a placebo. Additionally, the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) was completed during both testing sessions after participants received the alcoholic and placebo drinks; this was done to examine if alcohol affected subjects’ responses on the questionnaire.

Alcohol Challenge Each participant received an alcohol dose of 1 ml/kg of 95% USP alcohol in order to yield a blood alcohol level of 0.8. The alcohol was mixed with orange juice and was administered prior to testing. The placebo consisted of orange juice, with drops of alcohol in the drink and around the rim. The volume of both the alcohol and placebo drinks were equal, and subjects were told that they would be receiving two different dosages of alcohol.

Subjects were told to consume the three drinks within fifteen minutes (five minutes per drink). Testing occurred after thirty minutes had elapsed from the start of alcohol consumption so that the participant’s blood alcohol level would be at or near the height of the blood alcohol curve (BAC) when testing began.

Participants’ blood alcohol levels were measured using an Alco-Senser® III Breathalyzer at the start of the session to confirm a BAC of 0.00% and at multiple times across the blood alcohol curve (i.e. every ten minutes). Breathalyzer readings were taken in both conditions in order to maintain an experimental blind.

Data Analyses Data were analyzed using the Statistical Package for Social Sciences, version 11 (SPSS, Inc., Chicago, Illinois).

Data which was not normally distributed was log transformed. Repeated measures ANOVAs with between-subjects effects (with an independent variable of condition and group as the between-subjects factor)

Psi Ψ Issue III March 2013

were conducted to assess if the independent variable of condition affected performance on the tasks, and if there was an interaction effect between group and condition. Two-tailed independent sample t-tests were conducted to assess the main effect of group. A Wilcoxon signed rank test was also conducted for scores on the SPSRQ. The dependent variables examined differed according to each task (e.g., number of errors of omission for the Go/No-Go task). For pairwise and posthoc comparisons, the Bonferroni correction was used. Pearson’s correlation coefficient was also used to assess relationships among the participants’ scores on the BIS-11 and on the SURPS subscales of Impulsivity and Sensation Seeking, and participants’ performance on the five tasks. All correlations were onetailed, and a significance level of .05 was used for all statistical analyses.

Results A) Go/No-Go Task Commission Errors Main effect of condition: A trend towards significance for a main effect of condition on commission errors was found; F(1,8) = 3.307, p = .106.

Main effect of group: An independent samples t-test revealed no main effect of group between subject groups for placebo commission errors (t(8) = -.925, p = .382) nor between subject groups for alcohol commission errors (t(8) = -1.069, p = .316).

Interaction effect (group by condition): There was a non-significant interaction effect; F(1,8) = .024, p = .881.

Correlations: Pearson’s correlation revealed a trend toward significant correlations between placebo commission errors and the Motor subscale of the BIS; r = .51, p = .066. A trend toward significance was also found between the Motor subscale of the BIS and the number

Low Impulsive (n = 5)

High Impulsive (n = 5)

Mean

SD

Mean

SD

Placebo commission errors

10

11.9

10

5.3

Alcohol commission errors

11.6

11.9

13.8

5.5

Table 2. The mean and standard deviation (SD) for Go/No-Go commission errors.

of commission errors in the alcohol condition; r = .51, p = .067.

Omission Errors Main effect of condition: No main effect of condition was discovered; F(1,8) = .760, p = .409.

Main effect of group: No group differences in the placebo (p = .37) nor in the alcohol condition (p = .616) were revealed.

Interaction effect (group by condition): There was no significant interaction effect; F(1, 8) = .103, p = .756.

Correlation: A significant positive correlation was found between the number of omission errors made in the placebo condition and the Attention subscale of the BIS; r = -.57, p = .042.

Low Impulsive

High Impulsive

(n = 5)

(n = 5)

Mean

SD

Mean

SD

Placebo omission errors

13.6

12.1

5.6

4.5

Alcohol omission errors

10.4

12.8

4.2

4.1

Table 3. The mean and standard deviation (SD) for Go/No-Go omission errors.

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Psi Ψ Issue III March 2013

B) Acey Deucey One participant in the low impulsivity group was excluded from this task due to an inconsistent betting strategy that did not fit in with the model of prospect theory (please refer to Appendix for the output graphs showing participants’ responses and the prospect theory model for this task).

Loss Aversion (Lambda) Main effect of condition: There was no main effect of condition on loss aversion; F(1,7) = .199, p = .671.

Main effect of group: No group differences were found between subjects in the placebo condition (t(7) = 1.324, p = .227), nor between high- and lowimpulsive subjects in the alcohol condition (t(7) = 1.291, p = .238).

Interaction effect (group by condition): No group by condition interaction effect was revealed; F(1, 7) = .528, p = .495).

Correlation: Placebo condition scores of loss aversion showed a high negative correlation with the Attention subscale of the BIS; r = -.75, p = .010. A trend toward significance for a correlation between loss aversion in the placebo condition and the SURPS Impulsivity subscale was also found; r = -.54, p = .065. Alcohol loss aversion scores had a significant negative correlation with the Attention subscale of the BIS (r = -.69, p = .021), as well as with the SURPS Impulsivity subscale (r = -.69, p = .019), and the SURPS Sensation Seeking subscale (r = -.65, p = .030).

Consistency (Mu) Main effect of condition: A significant trendwas found for alcohol on consistency scores; F(1,7) = 4.108, p = .082.

Main effect of group: No group differences were found in the placebo condition (t(7) = .975, p = .362), nor in the alcohol condition (t(7) = .786, p = .458).

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Low Impulsive

High Impulsive

(n = 4)

(n = 5)

Mean

SD

Mean

SD

Placebo loss aversion

1.4

.5

1.0

.3

Alcohol loss aversion

1.2

.2

1.0

.3

Table 4. The mean and standard deviation (SD) for loss aversion scores. A lower loss aversion score implies more risky behaviour.

Interaction effect (group by condition): No significant group by condition interaction effect was found; F(1, 7) = .245, p > .636.

Correlations: Significant correlations were discovered between subjects’ consistency scores in the placebo condition with the SURPS Impulsivity subscale (r = -.63, p = .034), as well as with the SURPS Sensation Seeking subscale (r = -.66, p = .026). No significant correlations were found for consistency scores in the alcohol condition.

Low Impulsive (n = 4)

High Impulsive (n = 5)

Mean

SD

Mean

SD

Placebo consistency

21.8

30.5

7.3

2.6

Alcohol consistency

6.6

2.4

5.3

3

Table 5. The mean and standard deviation (SD) for participants’ consistency scores. The lower the score, the lower a participant’s consistency.

Correlations of the Acey Deucey Task with Other Behavioural Measures of Impulsivity Significant correlations were found for loss aversion and consistency scores in both conditions. Placebo loss aversion scores correlated with: placebo commission errors (r = .6, p = .046) and alcohol commission errors (r = .69, p = .02). Alcohol loss aversion scores showed a trend towards significance for correlations

Psi Ψ Issue III March 2013

with: mean pumps in the placebo condition (r = .53, p = .073), placebo commission errors (r = .52, p = .078), and alcohol commission error (r = .53, p = .072). Scores for consistency in the placebo conditions were correlated with placebo commission errors (r = -.58, p = .049), and showed a trend toward significance for a correlation with alcohol commission errors (r = -.51, p = .083), and with alcohol omission errors (r = -.51, p = .079).

Placebo consistency scores also had a correlation of -.62 (p = .038) with placebo omission errors. Scores of consistency in the alcohol condition had a significant correlation with alcohol omission errors (r = -.76, p = .008), and showed a trend toward significance for correlations with placebo explosions (r = .57, p = .056), and placebo mean pumps (r = .54, p = .068).

C) BART Balloon Explosions Main effect of condition: A trend towards significance for a main effect of alcohol on balloon explosions was discovered; F(1,8) = 2.231, p = .174.

Main effect of group: No significant group differences were found between groups in the placebo condition (t(8) = .981, p = .355) nor in the alcohol condition (t(8) = .-1.01, p = .322).

Interaction effect (group by condition): No significant group by condition interaction effect was found; F(1, 8) = .11, p > .748.

Correlations: The number of explosions in the alcohol condition was significantly correlated with scores on the Motor subscale of the BIS; r = .61, p = .030. A trend towards significance for a correlation between placebo explosions and the Motor subscale of the BIS was found; r = .50, p = 0.069.

Low Impulsive (n = 5)

High Impulsive (n = 5)

Mean

SD

Mean

SD

Placebo balloon explosions

4.6

1.7

11

7.8

Alcohol balloon explosions

5.8

3.11

11

6.6

Table 6. The mean and standard deviation (SD) for balloon explosions on the BART.

Mean Number of Balloon Pumps Main effect of condition: A trend towards significance for the main effect of condition on average number of balloon pumps was observed; F(1,8) =.647, p = .165.

Main effect of group: No significant group differences in the placebo condition (t(8) = -7.17, p = .494) nor in the alcohol condition (t(8) = -.855, p = .417) were found.

Interaction effect (group by condition): No significant type by condition interaction effect was found; F(1,8) = .014, p = .907.

Correlation: A trend towards significance for a correlation between placebo balloon pumps and the Motor subscale of the BIS was found (r = .44, p = .1), and between the average number of pumps in the alcohol condition and the Motor subscale of the BIS (r = .53, p = .059).

D) Questionnaire Measures of Impulsivity One-tailed Pearson’s correlations were carried out on the different measures of impulsivity used. A high correlation was found between scores on the BIS and with scores on the SURPS Impulsivity subscale; r = .73, p = .009. Total BIS scores were also highly correlated with the SURPS Sensation Seeking scale; r = .72, p = .009.

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Psi Ψ Issue III March 2013 Low Impulsive

High Impulsive

(n = 5)

(n = 5)

Mean

SD

Mean

SD

Placebo average pumps

28

10.7

42

11.7

Alcohol average pumps

32

9.7

45

21.6

Low Impulsive

High

(n = 5)

Impulsive (n = 5)

Mean

SD

Mean

SD

Placebo SP

5.6

3.7

7.4

6.3

Alcohol SP

6.8

2.4

10

7.0

Placebo SR

10.4

1.8

12

5.8

Alcohol SR

9.6

3.0

14

3.9

Table 7. The mean and standard deviation (SD) for the average number of balloon pumps on the BART.

Table 8. The mean and standard deviation (SD) of scores for both groups of subjects on the Sensitivity to Punishment and Sensitivity to Reward Questionnaire. SP is Sensitivity to Punishment; SR is Sensitivity to Reward.

Moreover, significant correlations were discovered between the different subscales of the BIS with the SURPS Impulsivity and Sensation Seeking scales. The Motor subscale on the BIS had a correlation of .59 with the SURPS Impulsivity subscale (p = .037), and a correlation of .55 with the Sensation Seeking subscale (p = .048).

The Attention subscale of the BIS had a moderate correlation of .55 with the SURPS Impulsivity scale (p = .049), and a correlation of .55 with the SURPS Sensation Seeking scale (p = .048). The Non-planning subscale of the BIS had the highest correlation with the SURPS Impulsivity scale (r = .78, p = .004) and with the SURPS Sensation Seeking scale (r = .8, p = .003).

E) Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) A Wilcoxon signed rank test was used to assess differences between scores on the SPSRQ completed when the participants were sober and when they were intoxicated. Analyses revealed a statistically significant change in Sensitivity to Punishment scores between the placebo and alcohol condition; Z = -2.304, p =.028.

Discussion

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The goals of this pilot study were to examine differences between high- and low-impulsive healthy individuals in their ability to exert behavioural inhibition after consuming alcohol, and to examine the Acey Deucey paradigm as a measure of impulsive responding. Our hypotheses that differences between high- and lowimpulsive subjects would exist and that alcohol would increase behavioural disinhibition were partly supported.

Although differences between high- and low-impulsive participants were not found, there was a trend towards significance for a main effect of alcohol on the dependent measures.

Our main findings of the study are as follows: 1) alcohol consumption resulted in more impulsive behaviour on behavioural tasks, 2) data collected contributed to validation of the Acey Deucey as a measure of risky behaviour in an alcohol/placebo paradigm, 3) participantsâ&#x20AC;&#x2122; scores on the Sensitivity to Punishment scale of the SPSRQ were significantly elevated in the alcohol condition relative to the placebo condition, and 4) significant moderate correlations between the BIS and SURPS were found.

Psi Ψ Issue III March 2013

Though non-significant, a trend for an effect of alcohol on behavioural inhibition was found across all three behavioural impulsivity tasks. A decrease in behavioural inhibition following a dose of alcohol on the Go/No-Go and BART task is consistent with previous findings that alcohol increases motor impulsivity (Finn et al., 1999; Kindlon, Mezzacappa & Earls, 1995; Marsh, Dougherty, Mathias, Moeller & Hicks, 2002). The effect of alcohol on behaviour was predominately seen on measures of motor (rather than cognitive) impulsivity.

Although past studies have found support for increased cognitive impulsivity after drinking alcohol, other studies have not (Ortner et al., 2003; Steele & Josephs, 1990).

The strongest trend towards significance for the effect of alcohol on behaviour was on participants’ consistency in betting on the Acey Deucey task.

Although consistency scores were shown to decrease when subjects completed the task under the influence of alcohol, we have to be cautious in interpreting this finding as showing impulsivity. While a component of impulsivity is inattention, it is likely that this change in consistency scores reflects changes in attention levels rather than in cognitive impulsivity. This view is consistent with further findings that both omission errors on the Go/No-Go task and the Attention subscale of the BIS are negatively correlated with the dependent variable of consistency (i.e. the more consistent a person is, the less omission errors they make and the lower their score on the BIS subscale of Attention).

Highly impulsive subjects in our study were found to have lower scores on loss aversion, although not at a level of statistical significance. This finding ties in with previous studies conducted by the

Dagher lab that have found that pathological gamblers and Parkinson’s patients with problem gambling (i.e. populations with increased impulsivity levels) display decreased loss aversion compared to healthy controls.

An interesting finding, although non-significant, was that despite stability in loss aversion scores among highimpulsive participants across conditions, low-impulsive subjects’ scores of loss aversion were found to decrease after alcohol consumption. A recent fMRI study on loss aversion by Tom et al. (2007) found that those who had decreased levels of behavioural loss aversion showed decreased neural activation in response to losses as well as to gains, whereas those who were more behaviourally loss averse showed an increased neural response to both losses and gains. These findings are consistent with the theory which states that some forms of risk taking are related to a diminished physiological response to stimulation or a decreased amount of dopamine (Comings & Blum, 2000; Dawe & Loxton, 2004; Everitt et al., 2008; Zuckerman, 1994). The finding that only the loss aversion scores of low-impulsive subjects decreased after alcohol consumption could be a reflection of the decremental effects of alcohol on the brain areas involved in loss aversion for lowimpulsive subjects. Alcohol may not affect these brain areas as much in highly impulsive individuals due to their already decreased activity. However, this is the first study to examine responses on the Acey Deucey task during an alcohol condition so further studies are needed to examine if this finding can be repeated, and if it occurs in healthy controls with average impulsivity levels compared with highly impulsive populations, such as pathological gamblers. Support for the Acey Deucey task as an indicator of

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impulsive behaviour was strengthened by its significant correlations with the dependent variables from the other behavioural impulsivity tasks, as well as the SURPS scales of Impulsivity and Sensation Seeking.

The Sensitivity to Punishment scale of the SPSRQ was also affected by alcohol, especially in high-impulsive subjects. Although a study by Loxton, Nguyen, Casey & Dawe (2008) found that problem gamblers have both high impulsivity and high sensitivity to punishment, this finding contradicts previous studies according to which highly impulsive individuals will show greater sensitivity to reward than punishment (Dawe & Loxton, 2004; Kambouropolous & Staiger, 2002; Torrubia, Avila, Molto & Caseras, 2001). This finding may be mediated by the co-variables of hopelessness and anxiety sensitivity found on the SURPS (high-impulsive subjects had a higher mean average score on both of these measures than low-impulsive subjects). A previous study reported that individuals who score high in sensitivity to punishment might be more impulsive due to their increased vulnerability to negative emotions (Slessareva & Muraven, 2004).

Even though behavioural differences were seen between high- and low-impulsive subjects, we did not find a significant group difference. Our lack of a significant difference between subject groups may be due to our small sample size; each group was composed of only five participants.

There was, however, no difference between high- and low-impulsive subjects on the number of commission errors made in the placebo condition. This ties in with a study by Horn et al. (2003) in which a relationship between the number of errors of commission and impulsivity scores on the BIS or Eysenckâ&#x20AC;&#x2122;s Impulsivity Inventory

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was not found. Instead, Horn found that the covariable of IQ was strongly related to errors of commission on the Go/No-Go task. Similarly, a study by Finn et al.

(1999) reported that only subjects who had low levels of working memory when sober made more errors on the Go/No-Go task.

These studies illustrate the importance of examining the effects of possible neurocognitive covariables on behaviour.

A significant correlation was found between the two trait measures of impulsivity used (i.e. the BIS and SURPS Impulsivity scale), as well as between the BIS and the Sensation Seeking scale on the SURPS. This is consistent with previous research on the relationship between the trait of sensation seeking and impulsivity (Buss & Plomin, 1975; Ersche et al., 2010). However, most of these previous studies reporting a relationship between impulsivity and sensation seeking used different trait measurements than our study; the Sensation Seeking Scale is commonly used to assess sensation seeking rather than the SURPS, and the Eysenck Impulsivity Inventory is often used to assess impulsive behaviour.

There are several limitations of the current study to acknowledge. Firstly, our pilot study had a small sample which resulted in a diminished power for statistical analyses. Secondly, only healthy males who were currently attending university participated in the study; this limits the generalizability of our results to different populations. Third, impulsivity may have interfered with completion of the screening questionnaires. Highly impulsive individuals may not have bothered to finish all of the questionnaires or may have spent less time contemplating their responses than low-impulsive subjects. This is plausible since previous research has reported that both distractibility and un-reflectiveness are two

Psi Ψ Issue III March 2013

 components of impulsivity (Gerbing, Ahadi & Patton, 1987). Fourth, the majority of subjects recruited for the low impulsive group were very close to the cutoff for inclusion into the group. This may be due to the fact that low impulsive subjects may have been less willing to participate in a study that involved alcohol. Fifth, only one measure of impulsivity, the BIS, was used to divide subjects into high- and low-impulsive groups.

Sixth, each behavioural task was only examined at one point along the BAC so our results for the behavioural tasks may not apply to different blood alcohol levels.

Seventh, despite the BART having real-world validity (Lejeuez et al., 2002), the other behavioural tasks used might not be ecologically valid. Participants were also alone while completing the tasks so the effects of social interaction on cognitive and motor impulsivity were not examined.

Eighth, the gambling paradigms in our study relied on the use of hypothetical amounts of money. The use of hypothetical money (instead of actual money) for the Acey Deucey task and the BART may have influenced participantsâ&#x20AC;&#x2122; betting strategy; subjects may have bet more money given that they faced no realworld consequences, thus leading to an overestimation of risky behaviour on the gambling tasks.

Previous studies conducted on gambling have used both hypothetical (Claassen et al., 2011) as well as actual money and rewards (Frydman, Camerer, Bossaerts & Rangel, 2010; Tom et al., 2007). Findings from the studies using hypothetical and actual rewards are similar, but a larger rate of discounting has been found when actual rewards are used (Bickel & Marsch, 2010). This may be due to actual rewards being smaller than hypothetical rewards, since it is not

feasible to give forty subjects $100 to gamble with, nor for them to decide between a $1000 reward in a year or an immediate $200 reward.

Lastly, despite our efforts to maintain an experimental blind (e.g. by taking blood alcohol readings across both conditions), many participants realized that they were participating in a testing session in which they received little (or no) alcohol. A solution to this would have been to use wine or beer for the alcohol condition and then a non-alcoholic version of wine or beer on the placebo day. This strategy has been used before in other studies (Kyngdon & Dickerson, 1999).

Copious amounts of previous studies conducted in the area of alcohol, however, have employed the same placebo paradigm as our pilot study (Dougherty et al., 2008), and have found significant results between placebo and alcohol conditions. This method is also more cost effective because, since pure ethanol was used in the alcohol condition, a smaller amount of alcohol has to be consumed in order to reach the legal limit of alcohol intoxication (versus drinking two or more bottles of beer to reach the same BAL).

Future studies should use a doubleblind design and should employ different measures of impulsivity to classify participants into high- and low-impulsive groups. High- and low-impulsive subjects should also be matched on the criteria of drinking habits, if possible, to avoid responses reflecting the different reactions that light drinkers vs. heavy drinkers have to drinking alcohol (King, Houle, de Wit, Holdstock & Schuster, 2006).

Neurocognitive and demographic variables, such as working memory and stress level, should also be measured in order to account for possible covariables on increased (or decreased) impulsive responding.

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In conclusion, our study used a counterbalanced design in which ten healthy male subjects completed behavioural measures of impulsivity. A trend towards significance for alcohol resulting in an increase of impulsive behaviour was found, but no statistically significant group differences were discovered. Significant correlations were reported between the Acey Deucey gambling task, the Go/No-Go, and the BART, as well as with the Sensation Seeking and Impulsivity subscales of the SURPS. Sensitivity to punishment increased in all subjects after consumption of alcohol compared to their responses when sober. Moderate correlations between the BIS and the SURPS Impulsivity and Sensation-Seeking scales were also found. Further research is needed to uncover the way(s) in which alcohol affects the brain, resulting in a lack of inhibition.

Appendix

Alcohol Condition

b) Subject 005 (Low Impulsive) Placebo Condition

Alcohol Condition

Note: The odds of winning are displayed on the x-axis and the amount of money wagered is on the y-axis. The dark line represents a participant’s bets, and the light line is the model for prospect theory. a) Subject 003 (Low Impulsive) Placebo Condition

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c) Subject 006 (High Impulsive) Placebo Condition

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Alcohol Condition

Alcohol Condition

d) Subject 007 (High Impulsive) Placebo Condition

f) Subject 009 (High Impulsive) Placebo Condition

Alcohol Condition

Alcohol Condition

e) Subject 008 (Low Impulsive) Placebo Condition

g) Subject 010 (High Impulsive) Placebo Condition

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Alcohol Condition

i) Subject 012 (High Impulsive) Placebo Condition

Alcohol Condition h) Subject 011 (Low Impulsive) *This subject was excluded from the analyses on loss aversion and consistency for the Acey Deucey task.

Placebo Condition

j) Subject 013 (Low Impulsive) Placebo Condition

Alcohol Condition

Alcohol Condition

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Early Lies: A Study of Pre-School Aged Children’s Lie Telling and Executive Functioning Laura Penalosa

Abstract The present study examined the relation between children’s lie-telling and their executive functioning, specifically measuring inhibitory control. The majority of children (2-3 years) peeked at the hidden toy when completing the temptation resistance paradigm, even after being instructed not to do so. Of those who peeked, 14 (38%) lied to conceal their transgression. Children who peeked and lied about having done so scored higher on a measure of inhibitory control, as compared to children who peeked and told the truth, and those who did not peek. No differences in inhibitory control were seen between the latter groups. These findings suggest that inhibitory control may contribute to the emergence of successful lie-telling.

Keywords: inhibitory control, executive function, lie-telling

Lying is a pervasive social behaviour utilized by individuals of most cultures and historical backgrounds, in pursuit of meeting altruistic, self-protective or antisocial goals (DePaulo, Kashy, Kirkendol, Wyer, & Epstein, 1996). Its presence is of such commonality, that the average adult tells at least one lie for every five social interactions (DePaulo & Kashy, 1998).

While there has been no agreement on a universal definition, the act of lying consistently entails the production of a false statement with the intention to deceive the recipient (Bok, 1999; Chandler, Fritz, & Hala, 1989; Lee, 2000; Lindley, 1980).

Due to the high relevance and cognitive complexity of its social nature, researchers from a wide range of disciplines have directed their interests to

study the intricacies of children’s lying behaviour. Contributions began around the time of Piaget, who described children’s perception of lies as nothing more than forbidden utterances and unintentional errors (DePaulo, Jordan, Irvine, & Laser, 1982; Piaget, 1965; Xu, Bao, Fu, & Talwar, 2010). Furthermore, Piaget asserted that children do not begin to define lies in the same manner as adults until the age of eleven.

Whilst his contributions to the study of socialcognition have been greatly recognized, experimental research and anecdotal evidence conducted since then has allocated a more accurate time frame, dictating the emergence of lie-telling and understanding of false beliefs to the age of 3-4 (Bussey, 1999; Bussey & Grimbeek, 2000; Carlson, Moses, & Hix, 1998;

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Chandler et al., 1989; Wellman, Cross, & Watson, 2003).

Developmental psychologists have emphasized the importance of the study of childhood lie telling due to its implications for both theoretical research and practical applications (Talwar & Lee, 2008).

Theoretically, research on children’s lying is relevant to cognitive development in terms of the degree to which executive function and theory of mind contribute to successful lie-telling. Examining children’s lie-telling has practical relevance in home, school, legal and clinical settings. From an early age, parents often highlight the antisocial nature of lying to their children in both explicit and implicit terms (Xu et al., 2010). In conjunction, teachers discipline students regarding the value of honesty in the domains of academics and morality. Lastly, deception is often a contributing factor to the symptomology of patients with psychological impairments in both legal and clinical settings (Goodman et al., 2006; Lyon, 1999).

Despite the abundance of anecdotal evidence regarding the of children’s lietelling, and the existent research on such behaviours, the literature is lacking evidence-based support for the cognitive abilities which underlie the emergence of deception.

When measuring such capabilities, it is important to use ecologically valid measures of lie-telling within an experimental setting.

Emergence of Lie-Telling Current literature demonstrates that children’s lie-telling emerges around the age of 3, and continues to develop into early adolescence (Lee, 2000; Talwar & Lee, 2008).

Children’s first lies are typically poorly executed and antisocial in nature; the majority of which are related to the denial of wrong doing or committed transgressions (Lewis, Stanger, & Sullivan, 1989; Newton, Reddy, & Bull, 2000;

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Talwar & Lee, 2002). This finding may be a result of social learning, given that it pertains to a window of time in where children are consistently being disciplined by surrounding adults (Bandura & McClelland, 1977; Xu et al., 2010). Lewis et al. (1989) conducted an editorializing study in whereby researchers examined the verbal deceptive behaviour of 3-year olds children in an experimental setting through the use of a modified version of the temptation resistance paradigm (Lewis et al., 1989; Talwar & Lee, 2002). Their results demonstrated that among 3-year olds who peeked at a forbidden toy, 38% denied their transgression, while the majority of children over 3 did not tell the truth. These results were later replicated

by Talwar & Lee (2008), who found that 64% of children (between the ages of 3-8) who peeked, also lied to conceal their transgression. It was evident that the older children exerted greater efforts to feign innocence by providing plausible explanations as to why they had correctly guessed the identity of the hidden toy, as compared to the younger group. This, along with findings from comparable studies (Polak & Harris, 1999; Talwar & Lee, 2002; 2008;), lends evidence to the argument that from 3 years of age on, children’s capabilities to lie about their own transgressions improve with social and cognitive maturity. Despite their noteworthy findings regarding the developmental trend of children’s lying behaviour, Lewis et al. (2002) failed to outline the cognitive processes that differentiate the deceptive abilities of the older versus younger children, by not providing participants with an opportunity to answer elaborative questions that would require them to sustain their transgression for a period of time (i.e. semantic leakage control). In order to conceptualize the necessary cognitive capacities for the emergence of lie-telling, research must

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outline the specific mechanisms encompassed by theory of mind and executive function and how they influence children’s lying behaviour.

Theory of Mind and Lie-telling To hold a theory of mind entails some understanding of certain classes of behaviours to be predicated by the particular beliefs and desires subscribed to by those whose action is in question (Chandler et al., 1989). In other words, it is the ability to attribute mental states to oneself and others which may, in fact, be incongruent but not mutually exclusive (Rassmusen, Wyper, & Talwar, 2009).

The literature is predominantly consistent in pointing to the preschool years as the critical time when these cognitive competencies begin to emerge (Carlson & Moses, 2001; Carlson et al., 1998; Chandler et al., 1989; Gordon & Olson, 1998; Rasmussen et al., 2009). Young pre-schoolers tend to present a realist perspective, where they believe that only one perspective of any state of affairs can be correct at a given moment in time.

Some explain this perceptual congruency between reality and appearance to be a lack of conceptual belief and mental representation (Carlson & Moses, 2001; Gopnik, 1993; Moses & Flavell, 1990; Perner, 1991). These 3-year old children, as compared to their 5-year old counterparts, exhibit poor performance on measures of false belief and deception, the appearance-reality testing and perspective taking (Carlson et al., 1998). Proponents of such theory characterize children 3 years of age and younger as having an unspecified cognitive deficit that limits their ability to understand counterfactual beliefs, and therefore theory of mind in general (Chandler et al., 1989; Perner, Leekman, & Wimmer, 1987).

There is a well-documented relationship between children’s theory of

mind abilities and their capacity to tell a successful lie that is plausible and maintained throughout time. In order to be successful when lying, lie-tellers must apprehend the deviation between one’s perspectives as compared to that of others, ultimately demonstrating a mere development of theory of mind.

Subsequently, an individual must construct a false statement and furthermore, regulate their behaviour such that only congruent verbal and non-verbal behaviours are evident, and expressive verbal and non-verbal behaviours that are incongruent with the lie are inhibited (Talwar, Gordon, & Lee, 2007). This practice entails the practice of exercising self-regulation in terms of semantic leakage control, or maintaining consistent behaviour, in order to avoid suspicion from the recipient (Talwar & Lee, 2002).

Ahern, Lyon and Quas (2011) recently demonstrated that children as young as 2.5 can produce initial false statements when prompted to, but do not demonstrate understanding of subsequent congruent false behaviour as their 3.5 year old counterparts do.

The emergence of such required abilities mirrors the time in which development of theory of mind occurs.

When children deliberately create a false belief in the mind of another by, for example, denying having committed a forbidden act, they demonstrate first order false belief (Chandler et al., 1989; Polak & Harris, 1999; Talwar & Lee, 2008). In their studies, Polak & Harris (1999) demonstrated that the false belief understanding of children from ages 3-5 was correlated with their false denials of having played with a toy (Talwar & Lee, 2008). This understanding of first order beliefs was, in turn, found to be highly correlated with the emergence of lie-telling in a childhood population (Polak & Harris,

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1999).

More recent studies have confirmed that typically developing 3 year olds show comprehension in first order beliefs (Ahern, Lyon, & Quas, 2011; Chandler et al., 1989; Talwar & Lee, 2002).

However, when children demonstrate a capacity to construct a false statement that is directly based on a false belief by, for example, maintaining consistency in their verbal and nonverbal responses when being questioned regarding a previous transgression, they demonstrate a capacity to maintain second order beliefs (Polak & Harris, 1999; Talwar & Lee, 2002; 2008).

To comprehend the latter, children must imagine themselves part-taking in a behaviour that they falsely said they engaged in, and then further infer a belief that they would hypothetically have if they were in such position, therefore constructing a belief based on a false belief (Talwar & Lee, 2008). This second-order false-belief representation is more commonly evident in children of 6-7 years old who not only lie, but attribute efforts to feign ignorance (Perner & Wimmer, 1985; Sullivan, Winner, & Hopfield, 1995).

These advances in lie-telling have repeatedly been shown to temporally mirror the development of childrenâ&#x20AC;&#x2122;s cognitive growth during pre-school years (Hala, 1991). In a noteworthy study, Talwar, Gordon, & Lee (2007) explored the relationship between second-order beliefs and lie-telling behaviours of children 6-11 years old.

Results demonstrated that, although 93% of the children who peeked denied their transgression, those who had difficulties with the follow-up questions had the lowest scores on measures of second-order beliefs. This capacity appears to emerge later in childhood, given that three year olds were four times less likely to control

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for semantic leakage as compared to seven year olds (Talwar & Lee, 2002). While Talwar & Lee (2008) examined the relationship between executive functioning, theory of mind and lie-telling in regards to both first order and second order beliefs, they failed to differentiate between the various components of executive functioning (i.e. working memory, inhibitory control, etc.) and their influence on lie-telling. Despite early contributions regarding the relation between theory of mind and lie-telling, the research examining the role of executive functioning in the development of lying is very limited.

Executive Telling

Function

and

Lie-

An additional cognitive component that has been repeatedly found to contribute to the development of theory of mind (ToM) and explain such differences on lie-telling measures is executive functioning (Carlson, Moses, & Breton, 2002; Carlson et al., 1998; Sabbagh, Xu, Carlson, Moses, & Lee, 2006). This construct encompasses processes that monitor and control thought and action including self-regulation, planning, behavioural organization, cognitive flexibility, error detection and correction, response inhibition and resistance to interference (Eslinger, 1996; Zelazo, Carter, Reznick, & Frye, 1997). It is unclear whether executive functioning affects the emergence or the expression of theory of mind (or both); however, their strong association has been thoroughly documented.

Inhibitory control (IC), a component of executive functioning that entails the capacity to supress potentially interfering thoughts or actions while pursuing a cognitively represented goal, has been proposed to be a prime candidate for an executive ability that

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 might relate to the development theory of mind for several reasons (Carlson et al., 1998; Carlson & Moses, 2001; Dempster, 1991; McCall, 1994). Firstly, both IC and ToM show rapid progression within the preschool years (Carlson et al., 1998).

Secondly, both IC and ToM appear to be by-products of frontal lobe activity (Carlson et al., 1998; Dempster, 1993; Luria, 1973; Rothbart & Posner, 1985).

Evidence to support this association has been derived from children born with phenylketonuria (PKU), a metabolic disorder that depletes the frontal brain region of dopamine (Carlson & Moses, 2001; Diamond, Prevor, Callender, & Druin, 1997). Furthermore, the extended development of IC throughout the first 6 years of life parallels the protracted development of the frontal lobes (Diamond & Taylor, 1996; Stuss, 1992).

Thirdly, deficits in both theory of mind tasks and executive functioning tasks are paralleled in autistic individuals with average range IQ as well as in children with fetal alcohol syndrome, providing further proof for their relevance (Hughes & Russel 1993; Ozonoff, Pennington, & Rogers, 1991; Rasmussen et al., 2009).

Lastly, it appears a prerequisite to success on measures of ToM could be the development of IC skills. For example, in tasks where children are given the opportunity to deceive, such as the Temptation Peeking Paradigm (Sears, Rau, & Alpert, 1965), individuals must be able to conceal their transgression by lying about whether or not they peeked (first order belief), and furthermore, suppress their knowledge of the real identity of the hidden toy and feign ignorance by providing an alternative answer (second order belief). This latter process requires the child to inhibit their enticement to reveal the identity of the toy, and simultaneously produce a plausible

response.

Carlson & Moses (2001) confirmed any remaining doubts of such speculations by demonstrating, in one of the most comprehensive studies of this kind, that inhibitory control and theory of mind task batteries were strongly correlated with one another (r = .66), after controlling for age, receptive vocabulary, and sex (Carlson, Moses, & Claxton, 2004). Given the evidence presented depicting the association between ToM and the emergence of lie-telling, as well as the vast correlations between ToM and IC, it is plausible that IC would also play a contributing role in the development of lying.

Although many researchers have speculated about the functional dependence between executive control, specifically inhibitory control, and lietelling, only one study has looked at the relationship between inhibitory control and deceptive capabilities in an experimental format.

In their study, Carlson et al. (1998) demonstrated that three-year-old children who presented difficulties in measures of inhibitory control were also prone to deficiencies in measures of deception. Although this study marks the commencement of the study of the relationship between inhibitory control and deceptive behaviour, its focus was limited to deceptive acts, as opposed to statements, and therefore results cannot be generalized. It is likely that the cognitive capacities necessary to execute and conceal a behavioural transgression may differ from those utilized for verbal deception. As of now, there are few studies that examine the role of inhibitory control on lying behaviours using experimental techniques and lie-telling measures that foster spontaneity.

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Goals of the Present Study The present study aimed to further examine the relation between preschool children’s executive functioning, specifically inhibitory control, and their abilities to deceive.

While previous research has demonstrated a reliable correlation between theory of mind and inhibitory control, investigations confirming the direct link between inhibitory control and children’s lie-telling are limited. A large sample of 2-3 year olds participated in a series of tasks completed in one individualized session at one of McGill University’s laboratories in Montreal. Inhibitory control was assessed using the Whispers task, which was first developed by Kochanska, Murray, Jacques, Koenig, and Vandegeet (1996).

Children were asked to whisper the name of familiar and unfamiliar characters presented by the experimenter.

The assumption of the task is that children will need to exert more inhibitory efforts when presented with familiar characters in efforts to supress the temptation to shout their name, as compared to unfamiliar characters. Deception was assessed with the Temptation Resistance Paradigm (TRP), which was originally pioneered by Sears et al., (1965). This paradigm creates a highly tempting situation for young children to peek at a forbidden toy, while subsequently providing them an opportunity to lie in order to conceal their transgression for self-protection (Talwar & Lee 2002; 2008). This task is highly efficient as it elicits spontaneous lying, is highly motivating for the selected population, and mimics the natural conditions in which children of this age group tend to lie (Newton et al., 2000; Smith, Wilson, Ross, & Ross, 1997). For the purpose of this study, 2 variables from the temptation resistance paradigm were analyzed: did the child peek, and did the

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child lie about peeking. Given the strong relation between IC and ToM (Carlson & Moses, 2001; Frye, Zelazo, & Palfai, 1995), we hypothesized that inhibitory performance would predict lie-telling, where children with higher scores on the Whispers paradigm would be more likely to conceal their transgression by saying they did not peak. These results are presumed to contribute to the existing literature by providing further evidence for the prominent role of inhibitory control on the ability to execute successful lies.

Methods Participants Eighty-seven children between the ages of 2 and 3 years old were recruited from the local Montreal Metropolitan area (M: 32.6 months, range from 26-46 months, SD: 3.30). The sample consisted of 48 females, and 39 males, all of whom were predominantly Caucasian. Of the 87 children, nineteen did not successfully complete the Temptation Resistance Paradigm due to non- compliance with instructions, therefore the results only pertain to the 67 children who completed both tasks. For the purpose of data analysis, children were divided into three groups: those who did not peek, those who peeked but did not lie about having done so, and those who peeked and lied about it.

Procedure Children were tested in individual sessions which began with a warm-up game played with the experimenter in the testing room.

Two tasks were then administered: the Whispers paradigm (Kochanska et al., 1996) and the TRP (Sears et al., 1965). The order in which the tasks were administered was randomized. All sessions were video-

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recorded through the use of hidden cameras.

Parents were asked to complete a consent form as well as a demographics questionnaire, and later received a compensation of $10 once the session was finished, regardless of their child’s achievement.

Measures Whispers paradigm: This task requires children to voluntarily lower their tone of voice to a whisper when reporting the name of cartoon characters (i.e., familiar and unfamiliar characters) presented by the experimenter (Kochanska et al., 1996).

To begin, the experimenter asked the child to whisper their name to assess willingness and ability to comply with the task. Subsequently, children were shown 10 images of cartoon characters that appeared on construction paper; 6 of which were familiar (i.e. Nemo), and 4 of which were unfamiliar (i.e. teddy bear).

Children were instructed to whisper the name of the character if they recognized it, and in the case where the image appeared unfamiliar, they were to whisper, “I don’t know”. The task was scored as follows: 2 points for a whisper, 1 point for a normal voice, and 0 points for a shout. The videos were subsequently checked by an alternate experimenter to ensure inter-rater reliability.

Temptation resistance paradigm: Children were told that they would be participating in a guessing game. The child was instructed to either turn their back to the experimenter or place their chair facing the opposite direction from that of the experimenter. The child was told that the experimenter would place a toy on the table that stood between them (therefore behind the child); the toy would make a noise (i.e. a toy bird), and the child, without turning around, was to

guess what the toy was. The first and second trials consisted of such routine. On the third trial, the experimenter placed the “target toy” on the table and activated the noise, as they had before. However this time, they informed the child that they had to step out of the room for a moment, but reminded the child not to turn around and look at the toy while they were gone.

The child remained in the testing room alone for a period of 60 seconds. When the experimenter re-entered the room, they asked the child whether they had peeked, what they thought the toy was, and why they thought that. The videos were later checked by an alternate experimenter, who also recorded whether the child had actually peeked, and if so, how many seconds it took them to do so.

Results Results revealed that there were fourteen children who did not peek (21%), as instructed not to, and fifty-three children who did peek (79%). Out of the children who peeked, fourteen of them later lied about having done so (26%), while thirty-nine told the truth.

To examine our main hypothesis, we conducted a 2x3 Analysis of Variance (ANOVA) on the executive functioning scores with gender and group (liars, truthtellers, and non-peekers) as between subject variables. No effects approaching significance (P > 0.20) were obtained for gender, therefore we decided to conduct a one-way Analysis of Variance.

This ANOVA failed to reveal a significant group effect; F(2, 63) = 1.51, p = 0.23.

However, Figure 1 demonstrates a slight difference between the children who lied and those who told the truth about their transgression.

To more specifically test the comparison of the children who lied, we combined the non-peekers and truth-

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Inhibitory Control (Whispers Score)

tellers and conducted a T-Test. This yielded a significant effect; (64) = -2.37, p < 0.05. The liars scored significantly higher (M= 25.07) than the truth-tellers on the Whispers test (M = 21.35). These results remained the same when we controlled for age.

21.57

30 20 10 0

21.26

No Peek Peek but no Lie

25.07

Peek & Lie

Lie-telling (Temptation Resistance)

Figure 1: Mean inhibitory control scores as a function of lie-telling behaviours

Discussion The current study investigated the degree to which children’s inhibitory control predicted their lie-telling behaviour.

Several major findings were obtained.

Firstly, consistent with the current literature, our results showed that the majority of children (79%) peeked at the forbidden toy after being instructed not to do so. Past research conducted across Canada, The United States, Britain and Japan has demonstrated that 3 year olds are half as likely to lie as compared to their older counterparts (Lewis et al., 1989; Peskin, 1992; Talwar & Lee, 2002;).

These results may be explained by poor executive functioning in younger preschool aged children in terms of theory of mind development and inhibitory control (Polak & Harris, 1999; Talwar et al., 2007). As a result, children under the age of three may be unable to produce a false statement and maintain verbal consistency. It is important to note that a large portion of children in our sample were below the age of 3; an age which has been highlighted as a pivotal point in which lying tendencies emerge and remain strong throughout pre-school and

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elementary school years (Talwar & Lee, 2008).

Secondly, we noted significant differences in children’s inhibitory control, as represented by their lie-telling behaviour. Children who lied after having peeked at the forbidden toy, had, on average, greater inhibitory control as compared to children who did not peek or those who peeked but did not lie. While this study is the first to investigate the relationship between deceitful acts and inhibitory control, the results are congruent with previous theoretical findings, which indicate a strong correlation between first order beliefs and executive function (Talwar et al., 2007).

Children who were unable to produce a false statement to conceal their transgression, a function of first order beliefs, also maintained lower scores on the inhibitory control measure.

Furthermore, it is possible that such children have yet to develop theory of mind, given its well-documented relation to inhibitory control in terms of developmental timetable, common brain region and joint absence in psychological disorders (Carlson & Moses, 2001; Carlson et al., 1998). As compared to pre-schoolaged children, elementary school-aged children show a 50% reduction in peeking behaviours when instructed not to, however, out of those who did peek, 93% lied about having done so (Talwar et al., 2007). Consistent with Leslie’s (1987) research, younger children may be unable to engage in the decoupling strategies that their older counterparts do, which enables a divorced representation from current states, allowing for false symbolic belief (Gordon & Olson, 1998). This age related trend is consistent with the existent literature that depicts a normative developmental increase in inhibitory control in the face of tempting situations

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(Carlson & Moses, 2001; N. Mischel & W. Mischel, 1983).

Thirdly, we extrapolated a noteworthy distinction between both groups who did not lie. Although we expected that the children who did not peek would have the greatest inhibitory control as determined by their Whispers score, our result demonstrated the contrary. In terms of inhibitory control, we saw no difference between children who did not peek and children who peeked and did not lie. This finding can be explained in one of three ways. Firstly, the literature has consistently demonstrated that advances in inhibitory control are insufficient to account for the relationship between executive functioning and theory of mind (Carlson & Moses, 2001). Since the children who did not peek did not demonstrate such advancements, it is possible that other aspects of their executive functioning may mediate such mechanisms that would therefore explain their lack of peeking under a tempting situation. Working memory, defined as the ability to hold multiple perspectives in mind at a given time (Carlson et al., 2004), may be the missing variable in the equation. In fact, children who score higher on batteries that assess working memory, tend to have greater inhibitory control, which then correlates with better understanding of theory of mind (Carlson & Moses, 2001; Hughes, 1998). Therefore, although the children who did not peek appeared to have similar inhibitory control as those who peeked but did not lie, it may be imperative to control for working memory in order to reveal the full story. Secondly, the literature dictates that inhibitory control increases as a function of age and intelligence (Carlson et al., 1998; Talwar et al., 2007; Gordon & Olson, 1998).

Although no significant effects were found

as a function of age, it is possible that a difference in intelligence could explain why a small percentage (14%) of children were able to resist temptation and not peek at the mysterious toy. Lastly, it is probable that due to the young nature of this study’s population, some participants may not be familiar with the verbal vocabulary used by the experimenter.

Reports from several parents indicated that some children were not familiar with the terms “peek” or “lie”, which could therefore account for some error in the group who peeked but did not lie.

The current literature includes inconsistent results regarding the role of gender in the relationship between executive control and lie-telling (Depaulo & Bell, 1993; DePaulo, Epstein, & Wyer, 1993; Depaulo et al., 1996; Lewis et al., 1989; Talwar & Lee, 2002; 2008). Results from our study showed no significant differences in the degree to which lietelling abilities were predicted by inhibitory control between females and males. Although some research has found women to be more likely to lie for altruistic purposes (DePaulo & Bell, 1993; DePaulo et al., 1993;), for self-protective purposes (Cole, 1986), and to avoid instilling psychological harm on others (Cole, 1986; Saarni, 1984), it is possible that such cognitive differences that polarize particular ways in which men and women interact and evidently deceive are not in play at the age of three. Such lack of gender differences is congruent with a large portion of the literature that focuses on childhood populations (Carlson et al., 2004; Talwar et al., 2007; Talwar & Lee, 2002; 2008).

Limitations and Future Research Although the results derived from the current study provided new contributions to the literature by demonstrating the role of inhibitory

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control as a necessary cognitive prerequisite for lying, future research is necessary to continue unravelling the complexities surrounding the emergence of lie-telling.

In order to further speculate the differences in inhibitory control between the children who peeked, those who peeked but did not lie, and those who did not peek, it is imperative to assess more than one aspect of executive function, such as working memory.

The literature depicts consistent research that demonstrates the strong correlations between working memory and inhibitory control in predicting social cognition -- the ability to hold in mind multiple perspectives while suppressing those which are irrelevant (Carlson et al., 2004). This ability, in turn, has been shown to predict deceptive capabilities in both theory of mind and false belief measures (Carlson et al., 2004; Hughes, 1998; Carlson et al., 2002). Therefore, in order to assess whether both working memory capacity and inhibitory control are of critical importance to childrenâ&#x20AC;&#x2122;s understanding and execution of lie-telling behaviour, all three variables must be assessed in the same sample. We recommend future research to utilize the temptation resistance paradigm as a measure of lietelling behaviour, as it is the most frequently used method of studying deception in a children population (Talwar & Lee, 2008). In addition to the Whispers Test, it would be helpful to use a second battery of inhibition to control for verbal comprehension, such as Day Night Task (Gerstadt, Hong, & Diamond, 1994), given that some parents reported that their children were not familiar with any of the characters presented. Lastly, measures such as Block Delay or Count/Label could be used to assess childrenâ&#x20AC;&#x2122;s working memory (Carlson & Moses, 2001).

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In addition to controlling for working memory, future research should investigate the required cognitive capacities for lie-telling in a longitudinalbased study. By assessing children whose ages range from two years to eight years, researchers would be able to code for the development of theory of mind understanding, in terms of first and second order beliefs, as reflected by deceptive abilities. We would expect such a study to reveal an ascending developmental trend, in where lie-telling skills increase as a function of increased theory of mind and executive functioning. The majority of young children would be expected to peek when instructed not do so, although only a small percentage would produce a false statement to conceal their transgression (1st order belief), as consistent with our results. The older children, however, would be expected to show a more evenly distribution between those who peek and those who did not peek, nevertheless, the majority of children who peek would lie about having done so and maintain consistency with their subsequent verbal and nonverbal behaviours (2nd order belief).

These results would be in congruence with the existing literature, which demonstrates that the progression of theory of mind understanding parallels that of executive function (including working memory and inhibitory control), which emerges around the age of three and continues developing into the elementary school-ages (Carlson & Moses, 2001; Gordon & Olson, 1998; Talwar et al., 2007; Talwar & Lee, 2002;).

Lastly, minor methodological matters could be adjusted for in future studies in order to minimize the room for error.

Firstly, verbal comprehension should be assessed with the child, the guardian, or both prior to the study to ensure that the children are familiar with

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the terminology utilized in the tasks, such as “lie” or “peek”. Given that all the participants were tested in Montreal, it is possible that a portion of them may be primarily French-speaking, which could explain part of the variance of inhibitory control measures between those who peeked but did not lie and those who did not peek. Secondly, responses to followup questions regarding the identity of the toy should be coded against nonverbal behaviours, in order to ensure that children who are lying are producing conscious assertions, and not just impulsive utterances (Ahern et al., 2011).

This would, in turn, rule out the possibly that children are simply blurting out yes or no responses based on what they perceive to be the experimenter’s expectancies (DePaulo & Kashy, 1998). Lastly, due to the nature of the population, future research may consider limiting the scheduling of testing to hours in which the children are most alert. A portion of our sample’s data had to be excluded from the analysis due to a lack of compliance with the tasks due to fatigue, distraction or depleted attention.

Conclusion Overall, our results provided contributing evidence for the link between inhibitory control and the early emergence of lying. The Whispers scores were predictive of lie-telling abilities, in where children who lied about having peeked when instructed not to do so demonstrated the superior scores, relative to the other two groups. However, no differences in executive functioning were found between children who did not peek, and those who peeked but did not lie. Future research should continue investigating the cognitive pre-requisites that contribute to the emergence of lie telling in children.

Appendix Whispers Task Child Participant:___________ Date of Birth: M/D/Y Testing Date: M/D/Y Whispers Task Behaviour

Score

Whisper

3

Normal Voice

2

Mixed Voice

1

Shout

0

1) 2) 3) 4) 5) 6) 7) 8) 9) 10)

0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3

Total Score: __________ ( /30) Temptation Resistance Paradigm Child Participant:___________ Date of Birth: M/D/Y Testing Date: M/D/Y Toy 1:_________ Toy 2:_________ Toy 3:_________ Did Child Peek?

YES

NO

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Time of Peek (seconds):_________ “Did you turn around and peek at the toy?”

YES NO “What do you think it is?” __________________________________ __________________________________ __________________________________ “Why do you think it’s that?” __________________________________ __________________________________ __________________________________ Did the child lie? YES NO If yes, Did the child eventually confess?

YES NO Post-Testing Comments: __________________________________ __________________________________ __________________________________ __________________________________ __________________________________ __________________________________ __________________________________ __________________________________

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Forward Models in Healthy Subjects and Schizophrenics Jaclyn Marcovitz

A forward model is a proposed internal system that allows organisms to predict the sensory consequences of their movements based on outgoing motor commands (Desmurget et al., 2000).

During active movements the central nervous system sends a parallel copy of the motor command, known as an efference copy, to appropriate sensory areas. The forward model is thought to integrate the efference copy in order to predict the expected sensory feedback of selfgenerated motor movement (Ford et al., 2001). Once a movement has been executed the anticipatory sensory signal is compared with the incoming sensory signal, and the discrepancies between the two are analyzed (Bell, 2001; Blakemore et al., 1999; Cullen, 2012; Miall et al., 1995; Weiller et al., 1996).

A system like the forward model is indispensible since it allows an organism to distinguish and cancel out sensory input due to self-produced commands so that it can focus on more relevant externally produced sensations (Blakemore et al., 2000). The mechanism of subtraction is thought to involve a decrease in the strength of the connections between synapses. It is a form of plasticity that allows the organism to adapt to everchanging circumstances in its environment (Bell, 2001; Desmurget et al., 2000). A forward model therefore provides an organism with a flexible memory-based

cancellation mechanism that can reliably minimize the interference due to predicted re-afferent sensory input (Bell, 2001; Martin et al., 1996; Perret et al., 1993; Wolpert et al., 2001).

The theoretical concept of a forward model is especially essential for motor control and motor learning because sensory feedback loops are thought to be too slow to allow for efficient trajectory regulation. Using both efferent and afferent information, the forward model can estimate the current and future states of a motor process with negligible delays.

Based on this information, the final sensory outcome of a motor command can be predicted and compared to the actual sensory outcome. The mismatch can be used as an error signal to trigger corrective adjustments of the motor command. The forward model thus allows goal-oriented movements to be continuously updated in order to ensure that they are in accordance with the organism’s aims and expectations (Desmurget et al., 2000).

Convincing arguments for the use of a forward model in sensory monitoring were reported in a study by Wolpert et al.

(1995), which brilliantly proposed that a mathematical “Kalman filter” could act as a theoretical framework of the sensorimotor integration process. In the study, subjects made arm movements in darkness while under externally imposed forces, and then indicated their internal

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prediction of their arm’s final state.

Results showed that subjects’ final state estimates were biased; there was a consistent over-estimation of the distance moved that varied depending on the duration of the movement. Externally imposed forces altered the bias, but did not affect the pattern of variation (Wolpert et al., 1995).

Through manipulations of a mathematical model, Wolpert et al. (2005) showed that only a model that relies on both imperfect internal simulation and sensory correction could mathematically account for the consistent biases and the effects of external forces seen in the experimental results. In the proposed Kalman filter model, a Kalman gain mediates the relative weighting of the forward model and the corrective process to the final state estimate. The pattern of overestimation bias seen in the results can be explained by changes in the relative contributions of each process. The Kalman gain initially relies largely on the forward model, causing an overestimation of the distance travelled, and then adjusts its gain so that sensory feedback can correct the forward model’s inaccuracies.

The quick increase and following decline observed in the propagation of the bias with time can thus be explained as a consequence of this trade-off process. The congruence between the computational Kalman filter and the behavioral results provide strong support for the use of a forward model in state estimation tasks (Wolpert et al., 1995).

Just as the forward model has been shown to anticipate the sensory consequences of self-generated movements so that they can be recognized as internally produced, it is also thought to allow individuals to recognize thoughts and speech as their own. A dysfunction in this predictive mechanism could therefore

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give rise to the misattribution of selfproduced actions and speech as externally generated, which is commonly seen in patients with schizophrenia (Weiller et al., 1996). Abnormalities in the selfmonitoring forward mechanism are thought to underlie the passivity experiences and auditory hallucinations accompanying schizophrenia, in which subjects believe that their bodies are moving without their volition or that their self-generated speech is produced by external agencies (Blakemore et al., 2000; Maeda et al., 2012).

There is a significant amount of functional imaging, electroencephalography, and behavioral data supporting the involvement of a forward model in the pathology of schizophrenia (Blakemore et al., 2003; Ford et al., 2001; Spence et al., 1997; Weiller et al., 1996). Functional imaging evidence suggests that the ability to correctly recognize self-produced actions depends on a decrease of neural activity in the parietal operculum and the surrounding parietal cortex during selfgenerated movements (Weiller et al., 1996). Similarly, PET and MEG studies suggest that hyperactivity of the parietal cortex (Blakemore et al., 2003; Spence et al., 1997), cerebellum (Blakemore et al., 2003), and auditory cortex (Ford et al., 2001) contribute to the illusive feeling that active movements, speech, and thoughts are externally generated.

An elegant study by Blakemore et al. (2003) used PET imaging to show that increased activation of the parietal cortex and cerebellum is seen when both hypnotized schizophrenics and healthy subjects misattribute self-generated movements. PET images from healthy subjects indicated that the neural correlates of an active movement correctly attributed to the self were different than

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those when the same active movement was misattributed to an external source. These findings suggest that proper functioning of the cerebellar-parietal network is essential for a normal sense of agency, hinting that this network may be a neurophysiological substrate of a forward model. It is hypothesized that passive experiences in schizophrenics result from a disruption of this cognitive forward process (Blakemore et al., 2003).

Ford et al. (2001) offer further support for the role of forward models in schizophrenia comes from electroencephalography (EEG) studies, which suggest that a disruption in the connections between the frontal and temporal lobes may underlie auditory hallucinations. EEG recordings from healthy subjects provide neurophysiological evidence that a speechrelated efference copy is used to partially suppress self-generated speech in the auditory cortex. The partial subtraction of sensory input due to silently generated and vocal speech can be demonstrated by a reduction in the N1 event-related potential when healthy subjects listen to their own utterances or engage in self-talk.

Conversely, patients with schizophrenia show invariable N1 responses regardless of whether they are listening to their own self-generated speech or to externally produced speech. These results reflect a dysfunction in the auditory cancellation mechanism of schizophrenics. Deficits in this forward model may account for schizophrenics’ experiences of auditory hallucinations.

Direct behavioral support of the involvement of a forward model in schizophrenia has shown that schizophrenics have a general abnormality in their ability to predict the sensory consequences of their actions (Shergill et al., 2005). As demonstrated in the

previously mentioned study by Wolpert et al. (2005), healthy subjects tend to significantly overestimate the amount of self-generated force required to move their arm a given distance. Similarly, it has been shown that healthy subjects consistently underestimate the strength of their self-produced actions, and tend to perceive self-generated forces applied to their skin as weaker than externally generated forces of the same magnitude (Shergill et al., 2003). It has been proposed that healthy subjects underestimate selfgenerated forces as a result of the predictive functions of the forward model which cause self-inflicted sensory consequences to be partially removed from perception (Angel et al., 1982; Blakemore et al., 1998; Chapman et al., 1987; Milne et al., 1988; Shergill et al., 2003). Interestingly, results suggest that schizophrenic patients generally do not underestimate their self-generated forces (Waters & Badcock, 2012). The contrasting findings from patients with schizophrenia thus reflect a failure of the normal sensory attenuation mechanism that is present in healthy individuals.

Additional behavioral evidence for disruption of the predictive model in schizophrenia comes from a force matching study conducted by Shergill et al. (2003) in which subjects experienced an applied force on their left index finger and were then asked to reproduce the force with either their right index finger or a joystick. As expected, all healthy participants consistently overestimated the necessary replication force when using their right index finger. However, subjects were able to match the target force perfectly when using a joystick. In stark contrast, subjects with schizophrenia were able to accurately replicate the applied force in both conditions.

The abnormalities seen in schizophrenics’

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perception can be fully explained by the forward model. Since the cancellation process in schizophrenics does not attenuate the sensory consequences of their self-produced forces, the subjects cannot differentiate sensory input due to their own forces from that due to external forces. This behavioral experiment by Shergill therefore provides optimal support for the involvement of a dysfunctional sensory forward process in the pathology of schizophrenia.

While the current findings provide compelling arguments for the involvement of the forward model in schizophrenia, skeptics argue that such a model of sensorimotor feedback might not be able to fully account for all clinical observations. It has been argued, for instance, that while deficits in the forward model can explain patients’ feelings of not being in control, they cannot necessarily explain why schizophrenics often believe that a separate alien agent is directing their actions (Waters, 2012). While it is evident that the forward model plays some role in providing a sense of self-agency, it may be only one of several sources of information that contribute to the overall sense-of-self that seems to be disrupted in schizophrenia. The explicit cause and nature of the brain mechanisms underlying this disorder still remain to be discovered.

References Angel, R.W., & Malenka, R.C. (1982).

Velocity-dependent suppression of cutaneous sensitivity during movement. Experimental Neurology, 77(2), 266-64.

Bell, C.C. (2001).

Memory-based expectations in electrosensory systems. Curent Opinion in Neurobiology, 11, 481-87.

Blakemore, S.J., Frith, C.D., & Wolpert, D.M. (1999).

Spatio-temporal

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prediction modulates the perception of self-produced stimuli. Journal of Cognitive Neuroscience, 11(5), 551-9.

Blakemore, S.J., Goodbody, S.J., & Wolpert, D.M. (1998). Predicting the consequences of our own actions: the role of sensory estimation. Journal of Neuroscience, 18(18), 7511-8.

Blakemore, S.J., Oakley, D.A., & Frith, C.D. (2003). Delusions of alien control in the normal brain.

Neuropsychologia, 41, 1058-67.

Blakemore, S.J., Wolpert, D., & Frith, C.D. (2000). Why can’t you tickle yourself? Neuroreport, 11(11), 11-6.

Chapman, C.E., Bushnell, M.C., Miron, D., Duncan, G.H., & Lund, J.P. (1987). Sensory perception during movement in man. Experimental Brain Research, 68(3), 516-24.

Cullen, K.E. (2012). The vestibular system: multimodal integration and encoding of self-motion for motor control. Trends in Neuroscience, 353, 185-196. Desmurget M., & Grafton S. (2000). Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences, 4(11), 423-30.

Ford, J.M., Mathalon, D.H., Kalba, S., Whitfield, S., Faustman, W.O., & Roth, W.T. (2001). Cortical responsiveness during inner speech in schizophrenia: an event-related brain potential study. American Journal of Psychiatry, 158, 1914–16.

Ford, J.M., Mathalon, D.H., Heinks, T., Kalba, S., Faustman, W.O., & Roth W.T. (2001). Neurophysiological evidence of corollary discharge dysfunction in schizophrenia.

American Journal of Psychiatry, 158, 2069-71.

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Maeda, T., Kato, M., Muramatsu, T., Iwashita, S., Mimura, M., & Kashima, H. (2012). Aberrant sense of agency in patients with schizophrenia: Forward and backward over-attribution of temporal causality during intentional action. Psychiatry Research, 198, 1-6.

Martin, T.A., Keating, J.G., Goodkin, H.P., Bastian, A.J., & Thach, W.T. (1996). Throwing while looking through prisms.

Focal olivocerebellar lesions impair adaptation. Brain, 119, 1183-98.

Miall, R.C., & Wolpert, D.M. (1995).

Forward Models for Physiological Motor Control. Neural Networks, 9(8), 1265-1279.

Milne, R.J., Aniss, A.M., Kay, N.E., & Gandevia, S.C. (1988). Reduction in perceived intensity of cutaneous stimuli during movement: a quantitative study. Experimental Brain Research, 70(3), 569-76.

Perret, S.P., Ruiz, B.P., & Mauk, M.D. (1993). Cerebellar cortex lesions disrupt learning-dependent timing of conditioned eyelid responses. Journal of Neuroscience, 13, 1708–18.

Shergill, S.S., Bays, P.M., Frith, C.D., & Wolpert, D.M. (2003). Two eyes for an eye: the neuroscience of force escalation. Science, 301, 187.

Shergill, S.S., Samson, G., Bays, P.M., Frith, C.D., & Wolpert, D.M. (2005). Evidence for sensory prediction deficits in schizophrenia.

American Journal of Psychiatry, 162(12), 2384-6.

Spence, S.A., Brooks, D.J., Hirsch, S.R., Liddle, P.F., Meehan, J., & Grasby, P.M. (1997). A PET study of voluntary movement in schizophrenic patients experiencing

passivity phenomena (delusions of alien control). Brain, 120, 1997-2011.

Waters, F.A.V., & Badcock, J.C. (2012).

First-Rank Symptoms in Schizophrenia: Re-examining Mechanisms of Self-recognition.

Schizophrenia Bulletin, 36(3), 510-17.

Weiller, C., Juptner, M., Fellows, S., Rinjntjes, M., Leonhardt, G., Kiebel, S., … Thilmann, F. (1996).

Brain representation of active and passive movements. NeuroImage, 4(2), 105-10.

Wolpert, D.M., & Flanagan, J.R. (2001).

Motor Prediction. Current Biology, 11, R729-R732.

Wolpert, D.M., Ghahramani, Z., & Jordan, M.I. (1995). An Internal Model for Sensorimotor Integration.

Science, 269, 1880-82.

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Social Networks Christian Guay

Abstract Humans are social organisms. Each of us relies on a complex network of social relationships, mediated by so-called social games, to accomplish our everyday tasks. The emerging field of social cognitive neuroscience has provided neuroscientists and social psychologists alike with a wealth of information regarding the brain networks that underlie our idiosyncratic social cognitions. Here, I introduce a novel social neural pathway based on functional and anatomical connections in the human brain. A training mechanism is proposed for this pathway, whereby the medial prefrontal cortex and medial temporal lobes train the posterior cingulate cortex on social games, which can then be used in real-time social conflict resolution. I proceed to use this novel training pathway as the basis for a computational model of autism spectrum disorder. Using sibling-descendant cascade correlation, I simulate synaptic plasticity in the posterior cingulate cortex of healthy and autistic individuals to explore the social learning deficits associated with autism-spectrum disorder. Future neuropsychological studies are proposed.

The ability to navigate complex social environments is fundamental to human civilization and underlies the phenomenon of cultural evolution. The social behavior observed in phylogenetically distant organisms, such as ants, hints at the survival and reproduction benefits associated with superorganism membership (Dorigo, 2000; Seeley, 1989). Indeed, specific areas of the human central nervous system have evolved in such a way to allow for adaptive processing of highly dynamic social environments (Ninan, 2011).

Brain mechanisms of a social organism The evidence accumulated in the field of social cognitive neuroscience suggests a vast range of brain mechanisms underlying social cognition (Adolphs,

2003). In this paper, I consider a novel neural pathway involved in social decision-making and explore its restingstate training mechanism (see Figure 1).

The default mode network (DMN) includes a constellation of correlated brain areas that activate in the absence of salient perceptual stimuli (e.g. when you close your eyes). The networkâ&#x20AC;&#x2122;s activity is thought to modulate self-referential thought, future planning, and mind wandering (Buckner, 2008).

The anterior cingulate cortex (ACC) is anticorrelated with the DMN, and plays an essential role in perceptual and cognitive conflict resolution in the brain (Kerns, 2004). This role extends to social conflict resolution as well (Van Overwalle, 2009). Brain networks are said to be anticorrelated whenever activity in

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Figure 1. A proposed social neural pathway that is trained at rest.

one network reliably predicts relative silence in the other. In other words, two anticorrelated brain networks coordinate each other’s activity via negative feedback mechanisms. Here, I propose a social neural pathway whereby the DMN trains one of its components, the posterior cingulate cortex (PCC), on social games.

PCC social training is accomplished via integration of medial prefrontal cortex (mPFC) and medial temporal lobe (mTL) processing, when an individual’s mind is wandering or is engaged in future planning. A socially trained PCC can function as a decision-making unit in conflict resolution via its anatomical connections with the ACC, and its anticorrelated activity with prefrontalbased motor control circuits (Uddin 2009).

Psychologically, the pathway can be summarized as such: a specific social context is presenting conflicting cues to an individual, who experiences the conflict as anxiety. When at rest (active DMN), the individual rehearses multiple variants of the social situation in an attempt to resolve the cognitive conflict (training). Once a decision needs to be made regarding the conflicting social situation, the individual relies on their simulated experiences to

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guide their decision-making (trained PCC).

This theory generates the hypothesis that individuals with an abnormal DMN will exhibit abnormal behavior in socially conflicting situations.

Indeed, functional connectivity studies have revealed robust differences in the DMN of patients with autism-spectrum disorder (ASD) (Broyd, 2009). To investigate further, I propose a simple computational model of ASD.

Computational mechanisms of a social net Sibling-descendant cascade correlation (SDCC) algorithms are effective at building artificial neural networks (nets) that are capable of solving non-linearly separable problems (Shultz, 2012). The advantage of using SDCC nets for a biopsychosocial model of ASD is two-fold. First of all, the variable connection weights underlying network learning have a well-studied neurobiological analogue: synaptic plasticity (Martin, 2000). Thus, the socalled hidden units recruited in a constructivist neural network can be considered as novel connections to existing neurons, synaptogenesis. Secondly, many social games are non-linearly separable,

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 making them unsolvable for many other learning algorithms.

In this model, SDCC is used to simulate synaptic plasticity in the PCC (see Figure 1). The continuous XOR problem is used to simulate a social game with conflicting social input (i.e. each social factor is quantitatively represented by a number between 0 and 1). In the continuous XOR problem, two independent input variables are used to define 4 state quadrants (see Figure 4, results), which are non-linearly separable.

Therefore, it is a useful problem that may be used to test state discrimination capabilities.

Importantly, learningcessation is integrated in this model to more accurately simulate human autonomous learning patterns (Shultz, 2012). Autism-spectrum disorder is operationalized in this model via the learnability and threshold variables. More specifically, the altered DMN functional connectivity seen in autistic patients is translated into a lower signal/noise ratio used in network training (lower learnability value). Note that learnability can be mapped onto a spectrum to reflect the wide range of severity in ASD. The long-term cumulative effects of social isolation experienced by autistic patients are translated into a higher threshold value (i.e. the individual does not have enough social memory to accurately assess success during the training period).

Methods Twelve autistic nets and five healthy nets were generated for this study, to achieve a total of five victories in each condition. A learnability value of 90% was used for healthy controls, to simulate the noise that is inherent in high-order cognitive processing. A threshold value of

0.25 was used for healthy controls, to simulate the inherent uncertainty in social decision-making. A patience value of 7 was used for all nets to simulate the memorization involved in social scripts.

Results Healthy Autistic # of nets 5/5 (victorious/total)

5/12

Learnability

90%

85%

Threshold

0.25

0.30

Patience

7

7

Table 1.

In the context of this paper, an epoch is the fundamental unit of time, and consists of a complete training cycle. If net A requires less epochs to learn a game than net B, then net A is more efficient at learning that particular game than net B. Perhaps the most interesting result of this simulation is the failure of 7 autistic nets to master the game (see Table 1). This is likely a combined effect of the lower learnability and higher threshold values for autistic nets. This learning deficiency is also seen in the time taken by successful autistic nets to master the game (see Figure 2). On average, autistic nets required more epochs than healthy nets and most of the extra epochs were spent in input phase. In fact, the input-epochs/total-epochs ratio increased from 57% for healthy nets to 59% for autistic nets. The training-error patterns for both healthy controls and autistic nets exhibit the characteristics of standard SDCC learning (see Figure 3). As expected, healthy nets showed a steeper error-reduction curve for this social game than their autistic counter-parts.

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Figure 2.

Figure 3.

Discussion & Conclusion The results of this simulation agree with both colloquial and scientific hallmarks of autism (Adolphs, 2003). The 7 autistic net failures can be interpreted in two non-mutually-exclusive ways. First, each individual net can represent a small neural network in the PCC for a single individual (i.e. 17 individuals generated for the study). Interpreted as such, the 7 failures reflect the inability of autistic patients to learn social games that may seem intuitive to others. A second perspective is to consider all the healthy nets as part of a single healthy individual’s PCC, and all the autistic nets as part of a single autistic individual’s PCC (i.e. 2 individuals generated for the study). This interpretation contributes to the finding that autistic individuals need more time to learn a social game than their healthy

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counter-parts (see Figure 2). In fact, if an autistic individual’s first few attempts to build a social game network in their PCC (via resting-state rehearsals) are failures, their learning time will necessarily increase or they will start thinking of something else entirely and abandon training.

Interestingly, the input/total ratio increase (57% → 59%) suggests that autistic social learning deficits may arise from a difficulty in forming novel neural connections (i.e. synaptogenesis) rather than modulating existing connections.

Furthermore, additional simulations with higher threshold values revealed that threshold value is a strong predictor of learning failure. Given that the threshold value in this model represents the social memory deficits of autistic patients relative to healthy controls, it increases as a function of age. This translates into a declining potential for learning social games in autistic patients as they grow older. Such a prediction is in line with studies on the developmental psychopathology of autism (Pennington, 1996), and emphasizes the importance of interventional social therapy at a young age.

To investigate the psychological predictions of this model, one can easily design a social XOR paradigm (see Figure 4). For instance, subjects could first be presented with two dating-site profiles and instructed to consider both profiles as potential long-term mating partners. An interval of arbitrary length could then be used to allow for DMN training of the PCC (i.e. subjects naturally rehearse potential relationship scenarios in their minds while at rest). After this training period, subjects are spontaneously presented with an actual choice between two mates, and reaction times are measured. In this scenario, a social conflict is arising from the competition between

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 potential mates, which may be operationalized continuously as their attraction coefficients. More specifically, a neural network trained on this game would serve as a decision-making center in the PCC: discriminating between attractive and unattractive partners (Figure 4, dark corners) or relaying information for further processing if the conflict in partner evaluation persists (Figure 4, white corners).

Figure 4.

The present model predicts longer reaction times for autistic subjects on this game, as well as failure to make a decision for a proportion of the autistic population.

To further investigate the social neural pathway introduced here, functional imaging studies may be conducted on subjects participating in a delayed socialconflict paradigm, such as the social XOR game outlined above (see Figure 4).

Abnormal PCC activity for autistic subjects during the resting-state/training interval and shortly preceding the realtime social decision, temporally matched with self-reports of social rehearsals, would provide evidence for this neural pathway.

Social decisions rely heavily on multivariable interactions, which are better modeled by paradigms capable of incorporating an arbitrary number of inputs and outputs.

Adding polydimensional capabilities to this social neural pathway model would empower it

to solve a vast array of social games that are emerging from the fields of behavioral ecology and evolutionary psychology. This improved model would also allow simulations to independently integrate biological, psychological, social, ecological and environmental factors.

References Adolphs, R. (2003).

Cognitive neuroscience of human social behaviour.

Nature Reviews Neuroscience, 4(3), 165-178. doi: 10.1038/nrn1056 Amodio, D. M., & Frith, C. D. (2006).

Meeting of minds: the medial frontal cortex and social cognition.

Nature Reviews Neuroscience, 7(4), 268277. doi: 10.1038/nrn1884 Broyd, S. J., Demanuele, C., Debener, S., Helps, S. K., James, C. J., & Sonuga-Barke, E. J. S. (2009).

Default-mode brain dysfunction in mental disorders: A systematic review. Neuroscience and Biobehavioral Reviews, 33(3), 279-296. doi: 10.1016/j.neubiorev.2008.09.002 Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain's default network - Anatomy, function, and relevance to disease.

In A. Kingstone & M. B. Miller (Eds.), Year in Cognitive Neuroscience 2008 (Vol. 1124, pp. 1-38).

Malden: Wiley-Blackwell.

Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems â&#x20AC;&#x201C; The International Journal of Grid Computing and Escience, 16(8), 851-871. doi: 10.1016/s0167-739x(00)00042-x Kerns, J. G., Cohen, J. D., MacDonald, A. W., Cho, R. Y., Stenger, V. A., & Carter, C. S. (2004). Anterior Cingulate conflict monitoring and

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adjustments in control. Science, 303(5660), 1023-1026. doi: 10.1126/science.1089910 Horovitz, S. G., Fukunaga, M., de Zwart, J. A., van Gelderen, P., Fulton, S. C., Balkin, T. J., & Duyn, J. H. (2008). Low frequency BOLD fluctuations during resting wakefulness and light sleep: A simultaneous EEG-fMRI study.

Human Brain Mapping, 29(6), 671682. doi: 10.1002/hbm.20428 Martin, S. J., Grimwood, P. D., & Morris, R. G. M. (2000). Synaptic plasticity and memory: An evaluation of the hypothesis. Annual Review of Neuroscience, 23, 649-711. doi: 10.1146/annurev.neuro.23.1.649 Ninan, I. (2011). Oxytocin suppresses basal glutamatergic transmission but facilitates activity-dependent synaptic potentiation in the medial prefrontal cortex. Journal of Neurochemistry, 119(2), 324-331. doi: 10.1111/j.14714159.2011.07430.x Pennington, B. F., & Ozonoff, S. (1996).

Executive functions and developmental psychopathology.

Journal of Child Psychology and Psychiatry, 37(1), 51-87. doi: 10.1111/j.14697610.1996.tb01380.x Seeley, T. D. (1989). The honey bee colony as a superorganism.

American Scientist, 77(6), 546-553.

Shultz, T. R., Doty, E., & Dandurand, F. (2012). Knowing when to abandon unproductive learning. In N. Miyake, D. Peebles, & R. P.

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Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 2327- 2332).

Austin, TX: Cognitive Science Society.

Uddin, L. Q., Kelly, A. M. C., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2009).

Functional Connectivity of Default Mode Network Components: Correlation, Anticorrelation, and Causality. Human Brain Mapping, 30(2), 625-637. doi: 10.1002/hbm.20531 Van Overwalle, F. (2009). Social Cognition and the Brain: A MetaAnalysis. Human Brain Mapping, 30(3), 829-858. doi: 10.1002/hbm.20547 Watanabe, T., Yahata, N., Abe, O., Kuwabara, H., Inoue, H., Takano, Y., . . . Yamasue, H. (2012).

Diminished Medial Prefrontal Activity behind Autistic Social Judgments of Incongruent Information. Plos One, 7(6). doi: 10.1371/journal.pone.0039561

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Activating Group I Metabotropic Glutamate Receptors in the Dorsal Hippocampus during the Retention Interval Accelerates Forgetting of Location Memory Jeongho Lyu Supervisor: Oliver Hardt Principal Investigator: Karim Nader

Abstract Long-term depression (LTD) in the dorsal hippocampus (dHPC) is linked to the removal of AMPA receptors (AMPARs) from postsynaptic sites. Blocking AMPAR endocytosis, in turn, prevents the forgetting of long-term object location memory in rats, linking AMPAR removal to forgetting. The removal of AMPARs also underlies the physiological phenomenon of LTD. Several LTD induction pathways have been described, and one critically depends on the activation of group I metabotropic glutamate receptors (mGluRs).

Thus, activation of these receptors might be involved in the forgetting of long-term object location memories. Here, we study the connection between the activation of group I mGluRs and forgetting. We first trained rats in an object location recognition task, which induces a memory for the object locations that lasts for up to 10 days. During six days following training, we infused rats daily with the mGluR agonist 3,5dihydroxyphenylglycine (DHPG) or its vehicle, phosphate-buffered saline (PBS), directly into the dHPC. We found that when tested the day after the last infusions, DHPG-treated rats no longer expressed memory for the object locations, while PBS-infused animals did.

Thus, DHPG seemed to have accelerated the forgetting of object location memory in rats.

This result suggests that there may be a causal relationship between the induction of LTD and forgetting.

Acknowledgements This directed studies project was made possible by the funding of the Nader Lab and the McGill University Faculty of Science. The author is grateful to Ms. Karine Gamache, research assistant at the Nader Lab, for her instruction and guidance in performing contextual fear experiments. The author also thanks Ms. Seunghyun Ko, research assistant at the Nader Lab, for her assistance in carrying out the experiments.

Blocking AMPA receptor (AMPAR) endocytosis with the peptide GluA23Y at post-synaptic sites in the dorsal hippocampus (dHPC) of rats prevents the loss of long-term object location memory (Hardt et al., 2012). It

is also known that blocking NMDA receptor (NMDAR) activity in the dHPC of rats can prevent loss of location memory (Villarreal et al., 2002). A recent study proposes a pathway in which NMDAR activity promotes AMPAR

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endocytosis by causing long-term depression (LTD) in the dHPC (Unoki et al., 2012). Although NMDAR activity has been linked to LTD in the hippocampus (Bartlett et al., 2011; Dalton et al., 2012; Dong et al., 2012), Unoki et al.’s (2012) study is the first that characterizes a pathway that promotes AMPAR withdrawal through LTD. Thus, inducing LTD in the dHPC may promote AMPAR endocytosis, and lead to accelerated forgetting.

We have previously examined whether NMDAR activation can accelerate forgetting (Lyu, unpublished data), and found that daily infusions of the NMDAR agonist d-Serine into the dHPC during the retention interval lead to memory loss. This result suggests that LTD-like processes might be involved in the forgetting of object location memory.

To study whether other LTD-induction pathways also induce forgetting, we examined whether it is possible to accelerate the forgetting by targeting group I metabotropic glutamate receptors (mGluRs). mGluRs have been linked to both long-term potentiation (LTP) and LTD (Neyman and Manahan-Vaughan, 2008; Overstreet et al., 1997). In particular, activation of group 1 mGluRs (mGluR1) causes LTD in the dHPC (Faas et al., 2002). We thus used the agonist 3,5-dihydroxyphenylglycine (DHPG) to activate mGluR1s in the dorsal hippocampus of rats. DHPG is a powerful, selective mGluR1 agonist known to induce hippocampal LTD (Ito et al., 1992; Volk et al., 2006). Our results show that infusion of DHPG was able to accelerate the forgetting of object location memory in rats compared to controls that received phosphate-buffered saline (PBS) infusions. This suggests that LTD in the hippocampus can cause forgetting of location memory, possibly by promoting

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AMPAR endocytosis, and that several LTD-induction pathways can cause forgetting.

In a subsequent control study using the same rats, we tried to replicate the effect of DHPG on the acquisition of contextual fear memory (Maciejak et al., 2003). We performed this study mainly to show that the repeated infusions of DHPG during the first experiment did not accelerate forgetting by lesioning the hippocampus. Our results show that DHPG causes a significant reduction of the time of the freezing response in rats upon re-introduction to the context.

These results lend support to our interpretation that LTD in the hippocampus causes the forgetting of object location memory.

Methods Animals The study used 32 (16 for first DHPG run, 16 for second DHPG run and contextual fear experiment) adult male Long-Evans rats (Charles River, Canada), weighing between 300-400g, that were kept in the Nader lab’s own colony.

All animals underwent acclimatization and handling for 4 days in the colony room prior to the start of the location memory experiments. Animals were kept on an unrestricted diet of Harlan irradiated pellets during all phases of the experiments. The colony room was kept under a cycle of lights on (7am) and lights off (7pm), and all experiments were carried out during the lights on phase. All study procedures used in this study complied with the requirements of the Canadian Council on Animal Care, and were approved by the McGill University Animal Care Committee.

Surgeries Identical surgical procedures to those specified in the study by Hardt et al.

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(2009) were used on all animals. The surgeries were carried out by Dr. Oliver Hardt in the Nader lab. The animals were anaesthetized with a mix of xylazine (3.33 mg/mL), ketamine (55.55 mg/mL), and Domitor (0.27 mg/mL) by intraperitoneal injection, with a volume of 1 mL/kg. Three jeweler screws were implanted into the skull using a Kopf Stereotax, and two steel cannulas (22 gauge) were subsequently placed bilaterally, targeting the dHPC (A/P - 3.6 mm, M/L 63.1mm, D/V - 2.4 mm, 10° away from midline). The cannulas were stabilized with dental cement, and obturators (“dummies”) were inserted in the guides to prevent blocking. An analgesic (buprenorphine, 0.324mg/kg) was given through an intramuscular injection after the surgery. An intraperitoneal injection of Antisedan (7.5 mg/kg) suspended anesthesia (Hardt et al, 2009, p.229).

Drugs DHPG was used as the treatment drug at a concentration of 20µg/1µL of solution, dissolved in PBS. PBS was also used for control infusions. The DHPG solution and the PBS control were injected directly into the hippocampal CA1 region, at a volume of 1µL per hemisphere per infusion, with a

microinjector connected to a Hamilton syringe with plastic tubing.

Behavioural Apparatus Location memory. An opaque cardboard box measuring 24”x24”x24” was used as context for habituation, training, and probe sessions. The box was secured on top of a platform and the inside flooring was covered with sawdust, which was evenly mixed up before each rat entered the context. Eight holes were drilled into inside flooring on the platform, corresponding to cardinal directions. These holes were used to affix the objects (two identical black 2Y tubes, attached to inverted mason jars) for training and probe sessions. A digital camera was suspended above the context to record the training and probe sessions.

Contextual fear memory.

Two identical training chambers were used for each session; each chamber was equipped with a stainless steel grid flooring connected to a shocker. A fan was kept on throughout the procedure and provided constant background noise.

The walls of the chamber were made of Plexiglas. A single light was mounted on one of the sidewalls of the chamber and was kept on throughout the procedure. A digital camera mounted in front of the

Figure 1. The object location memory experimental paradigm. Animals were habituated for 4d in the empty context, and subsequently underwent 7d of training with fixed objects. They then received dHPC infusions of either DHPG or PBS for 6d. They were tested the day after the last day of infusions in the same context as their training, with one object moved to a novel location.

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chamber door was used to record the animals’ behaviour.

Histology A histological follow-up was carried out in which the animals were deeply anesthesized and subsequently decapitated. The brains were extracted and preserved in formalin. 50µm-thick sections were cut using a Microm HM 505E cryostat machine and were analyzed under a microscope to verify proper cannulation.

Behavioural and Infusion Procedures Location memory. (Figure 1) All animals were habituated without objects in the sandbox context for 5 minutes per animal per day over four consecutive days. Each animal was carried to the box from the adjacent room and placed in the context facing a different corner on each day of habituation. The order of animals for the procedure was reversed each day (e.g. 1 à 16 on day 1, then 16 à 1 on day 2) Beginning from the day after the last day of habituation, the animals were trained with objects in place for 10 minutes per animal per day over seven consecutive days. Two objects were affixed, one in each of either NW/SE or NE/SW holes of the context. Each half of the animals was trained with either orientation. The animals were then placed in a random empty corner of the context, with the

order of animals reversed each day. The animals were assigned to either the experimental or control group, and received infusions of either DHPG or its vehicle, PBS. Contextual fear memory.

(Figure 2) The animals were acclimatized in a room adjacent to the context for 30 minutes, and each rat was subsequently handled for 3 minutes. The next day, the rats underwent contextual fear training: each rat was put into the conditioning apparatus, and after 3 minutes in the box, received one foot shock of 1mA strength (1s duration). A second, identical shock was administered 30s later. 60s after the second foot shock, the animal was removed from the box and immediately underwent infusion of either DHPG or PBS. The following day, 24h after the infusions, the animals were re-introduced to the conditioning box context for 5 minutes per rat and their behaviour was recorded. The second day’s training and third day’s probe were carried out with two rats at a time.

Measurement and Statistics Location memory.

The preference of novelty was measured as a ratio between the time the animal spent exploring the object in the new location vs. the time the animal spent exploring the object in the old location. Exploration was defined as when the animal’s head

Figure 2. The contextual fear conditioning experimental paradigm. Animals were put into the context and a shock was administered after 3mins. A second shock was administered after 30s. After 60s in the context, the animals were removed from the context and infused with either DHPG or PBS. The following day, the animals were re-introduced to the context for 5mins and their behaviour was recorded.

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was pointing directly at the object within a small distance of it (~1cm). The results were analyzed using one-way ANOVA. Contextual fear memory. The retention of fear memory was measured as the percentage of time that the animal spent motionless within the context.

Freezing was defined as when the animal made no discernible movement apart from mild movements related to breathing. Shaking of the head, grooming, and other minor movements were not classified as periods of freezing.

The results were analyzed using one-way ANOVA.

Results DHPG Accelerates Forgetting of Object Location Memory Rats were first trained in the object location task. The animals were then infused bilaterally in the dHPC with 1µL of DHPG (20µg/1µL) solution or 1µL of PBS (pH=7.2). The infusions were carried out once per day for six consecutive days, starting the day after the last day of training. Twenty-four hours after the last infusion rats were tested in a probe trial for memory for the object locations acquired during training (this was done by moving one of the objects to a novel location). As rats prefer to explore novelties, preferential exploration of the moved object indicates memory for the original locations.

Exploratory activity during the probe was analyzed with one-way ANOVA, and revealed a significant difference in preference for novelty between the DHPG and control groups; F(1,14) = 8.87, p = 0.0107 (Figure 3.1). d1stmin was defined as (time exploring novelty – time exploring old object) / (time exploring novelty + time exploring old object),

where d1stmin = 0 is the “no preference” mark. The results show that the control group had a significant preference for novelty (d1stmin = 0.264), whereas the DHPG group show no such preference (d1stmin = -0.162). A distribution of d1stmin scores for the two groups corroborated this result (Figure 3.2). Tested against a hypothetical mean of 0 (indicating absence of exploratory preference and thus the absence of memory), the DHPG group showed no preference for either the novelty or the old object. The control group showed a significant preference for the novel location; t(6) = 2.30, p = 0.0306.

It must be noted that one extreme outlier was removed from the control group for data analysis. With the outlier included, the initial ANOVA of object exploration scores yielded marginal insignificance at F(1,15) = 3.25, p = 0.093, but nevertheless shows a clear trend identical to the one seen in Figure 3.1.

Figure 3.1. DHPG vs. PBS (control) box plot.

Average value is indicated by the green line in the centre of the diamond. VEH rats showed a significantly higher preference for novelty compared to the DHPG treated rats. The population size was 15, defined as the number of animals, with one control gross outlier removed.

Inclusion of the outlier caused data to fall into range of marginal insignificance, but with a preserved visual trend. The DHPG group showed no preference for the novelty with average d1stmin = -0.162.

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Figure 3.2. A) Distribution graph of d1stmin values for the DHPG group. Insignificant difference from a test mean of 0 (p = 0.110) indicating no preference for novelty or old object. B) Distribution graph of d1stmin values for the control group. There was a significant positive difference from test mean of 0 (p = 0.0306), indicating preference for a novelty of location.

sum reached [20s] (seconds)

The time taken for the animals to accumulate a total of 20 seconds of exploratory time (sum reached [20s]) was 42.57 seconds on average for the DHPG group and 36.12 seconds for the control group. This difference was not significant (F<1, Figure 3.3). This shows that DHPG infusions did not reduce overall exploratory activity, which might explain a lack of object preference.

50 45 40 35 30 25 20

Discussion DHPG

VEH

Group Figure 3.3. Sum reached [20s], DHPG vs. control, shown with standard error. There is no significant difference between the groups (F = 0.194), indicating that the groups took statistically similar amounts of time to accrue 20s of exploration time.

DHPG Reduces Freezing Response in Contextual Fear Conditioning The same animals from the first study were used again for this experiment.

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The animals underwent contextual fear conditioning and were subsequently infused bilaterally in the dHPC with either 1µL of 20µg/1µL DHPG in PBS solution for the experimental condition, or 1µL of PBS for the control condition.

The group assignments were counterbalanced, such that half of the rats that had received DHPG in the objectlocation experiment received PBS, the other half DHPG (the same for rats previously infused with PBS). 24h after the infusions, the animals were reintroduced to the context and their freezing response was measured. The freezing response time was converted into percentage values and analyzed with oneway ANOVA. The ANOVA yielded a significant difference between the two groups for the average freezing response over the 5 minutes spent in context (Figure 4), indicating that the control group froze significantly more (61.95%) than the DHPG group (31.73%); F = 13.43, p = 0.0025. Analysis of only the first minute spent in context also yielded a significant difference between the two groups in the average freezing response, with the control group freezing more (41.60%) compared to the DHPG group (18.45%); F = 7.32, p = 0.0171.

Our results show that the mGluR1 agonist DHPG accelerates the forgetting of object location memory. We further replicate the effect that DHPG attenuates consolidation of contextual fear memory (Maciejak et al., 2003). In the object location memory experiment, the animals that received DHPG spent approximately equal amounts of time exploring the object in a novel location and in a previously learned location. The control animals that retained location memory spent more time exploring the object at the preferred novel location. In

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Figure 4. Freezing response time in context shown as percentages. A) The percentage of time spent frozen by animals measured over 5 minutes. The VEH group showed a significantly higher freezing response time (average 61.95%) compared to the DHPG group (average 31.73%) (F = 13.43, p = 0.0025). B) The percentage of time spent frozen by animals in the first minute after re-introduction to context. The VEH group again showed a significantly higher freezing response time (average 41.60%) compared to the DHPG group (average 18.45%) (F = 7.32, p = 0.0171).

the contextual fear conditioning experiment, the animals that received DHPG showed less freezing response time compared to those that had received control infusions, indicating that the DHPG animals no longer associated the context with the shock given during the training session.

These results show that DHPG is able to induce forgetting of object location memory in rats. As DHPG is known to cause LTD when administered in the dHPC, our results suggest a relationship between LTD and forgetting.

If such a relationship does indeed exist, Unoki et al.’s (2012) NMDAR-LTDAMPAR endocytosis pathway could be linked to Hardt et al. (2010) and Migues et al. (2010)’s findings of AMPAR withdrawal mediated forgetting. We will examine this possibility in a follow-up experiment using the peptide GluA23Y. GluA23Y is a peptide comprising a binding motif found on the GluA2 subunit of the AMPAR. This motif is critical for the activity-induced removal of AMPARs as GluA23y can block AMPAR

endocytosis by competitively binding to key molecules in the endocytosis pathway (Scholz et al., 2010; Yu et al., 2008). If GluA23Y infusions could prevent the accelerated forgetting caused by DHPG, it would show that the forgetting caused

by DHPG is dependent upon an AMPAR withdrawal pathway. This may serve as the link between Unoki et al. (2012)’s suggested pathway and the behavioural phenomenon of forgetting.

However, several key points must be considered for the interpretation of our results. Firstly, although LTD was initially seen as a potentially dominant force behind forgetting (Stanton, 1996; Tsumoto, 1993), various problems with the hypothesis soon became apparent, such as the fact that certain cases of LTD can act in favour of memory formation (Kemp and Manahan-Vaughan, 2007).

The inherent variety in LTD induction pathways complicates the situation; many different kinds of hippocampal LTDs exist, and different mechanisms are required to elicit them (Kemp et al., 2000). It is plausible that these LTD types

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differ in function as well, and thus further research may be required before a causal relationship can be firmly established between DHPG’s ability to induce LTD and its ability to accelerate forgetting. It must also be noted that the specific type of LTD caused by DHPG in the dHPC is not the same as that caused by synaptic activation; the saturation of one does not prevent the induction of the other (Palmer et al., 1997). Thus, even if the LTD caused by DHPG can be causally linked to the phenomenon of forgetting, this may not fully represent how forgetting occurs in the real life of an animal without such pharmacological intervention. Inducing LTD through synaptic activation and analyzing its effects on forgetting may be a solution to this problem, barring the possibility of causing multiple types of LTD at once.

Another interesting point of consideration arises from a recent study

by Casimiro et al. (2011) which reports that while both mGluR and NMDAR activity can cause AMPAR endocytosis, they internalize different types of AMPAR. Specifically, mGluR activity was shown to internalize GRIP1-bound AMPARs, whereas NMDAR activity internalized AMPAR without direct associations with GRIP1/2. GRIP1 is a protein that binds to the AMPAR subunit GluA2 and participates in the endocytosis of AMPAR (Burette et al., 2001; Davidkova and Carroll, 2007). If mGluR activity specifically targets GRIP1-bound AMPARs for endocytosis, it is plausible that the form of LTD induced by mGluR activity is connected to the GluA2dependent AMPAR endocytosis studied

by Migues et al. (2010), as GRIP1 is a protein that binds to GluA2 subunits.

Conversely, the NMDAR-induced LTD, which does not target GRIP-associated AMPAR, might not have causal

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relationship with the endocytosis of GluA2-dependent AMPAR. If this is true then it may be difficult to connect the Unoki et al. (2012) pathway of NMDAR activity-LTD-AMPAR endocytosis to the type of forgetting discussed in Migues et al.’s results. The AMPAR endocytosis caused by DHPG stimulation of mGluR, on the other hand, may then be more likely to have connections with the Migues et al. study.

The contextual fear conditioning experiment that we carried out replicates and corroborates the results of Maciejak et al. (2003), who also found that intrahippocampal infusions of DHPG reduce freezing response in rats.

However, it should be noted that the concentrations of DHPG used in the Maciejak et al. study were in the nanomolar range, which is extremely low compared to the 20µg/1µL (approx.

0.1M) concentration used in our experiment. Maciejak et al. also speculate in their study that a form of LTD may have been responsible for the result.

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