Pictorial and mental arid landscape images by Aline Bittencourt

Journal of Environmental Psychology 45 (2016) 30e39

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Pictorial and mental arid landscape images reduce the motivation to change negative habits Idit Shalev Department of Education and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, POB 653, Beer-Sheva, 8410501, Israel

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 October 2014 Received in revised form 26 September 2015 Accepted 22 November 2015 Available online 26 November 2015

Recent research has demonstrated that physical or environmental cues may signal the availability of resources for goal pursuit. However, the effects that pictorial and mental arid environments may have on one's estimated levels of resources for habit change are not known. Three studies examined the idea that an arid landscape is associated with reduced subjective vitality and, consequently, low motivation for change. Consistent with our prediction, the first two studies indicated that viewing pictorial images or visualizing mental images of a desert (versus a landscape with water or a control) reduced participant confidence in their ability to change negative habits. The relations between the type of environment and the motivation for change were mediated by subjective vitality. The third study supported these findings, suggesting pictorial images of arid landscapes were perceived as more depleting and stressful than images of landscapes with water but less stressful and more attractive than urban environment images. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Embodied cognition Self regulation Subjective vitality Motivation Depletion Environment

1. Introduction 1.1. The motivation for change At the core of behavioral change is the issue of the motivation for change (Overholser, 2005; Ryan & Deci, 2008a; Ryan, Lynch, Vansteenkiste, & Deci, 2011). However, individuals tasked with changing a maladaptive behavior often display ambivalence and resistance to change that behavior in the face of what they describe as a difﬁcult challenge (Engle & Arkowitz, 2006; MacKinnon, Michaels, & Buckley, 2006; Miller & Rollnick, 1991; Prochaska & DiClemente, 1986). This difﬁculty apparently stems from the duality of change, which affords an opportunity for a better lifestyle, on the one hand, but also entails a psychological threat, on the other (Miller & Rollnick, 1991). Thus, research has demonstrated that readiness to behavioral change occurs through a gradual process of overcoming restraining forces (e.g., task demands) and resistance to change (Prochaska & DiClemente, 1986). There is evidence that different situational constraints can also hinder the desire for change. For example, ambiguous or partial information about the consequences of change increase the need for cognitive closure (Kruglanski, 2004; Kruglanski & Webster, 1996) that leads

to conservatism and resistance to change (Jost, Glaser, Kruglanski, & Sulloway, 2003; Kruglanski, Pierro, Higgins, & Capozza, 2007; Lewin, 1951; Shalev, 2015a). However, how the motivation to change a negative habit is affected by physical and environmental determinants is not clear. As with the general process of goal setting (Gollwitzer & Sheeran, 2006), the decision of whether to invest mental resources to change a negative habit (e.g., procrastination or overeating) is influenced by energy estimations, defined as the perceived available physical or mental resources to invest in a given goal pursuit. Estimation of resources influences the drive to facili!langer, Chen, Ko € petz, Pierro, & tate change (Kruglanski, Be Mannetti, 2012). Thus, when energy is estimated to be low, individuals conserve their resources and invest mainly in short-term goals (Hagger, Wood, Stiff, & Chatzisarantis, 2010). Based on this reasoning, several models have highlighted the need that motivation for change be a prerequisite to the commencement of behavioral change (Prochaska & DiClemente, 1986; Ryan et al., 2011; Treasure & Ward, 1997). 1.2. Motivational influence on energy estimation The scientific literature has long recognized the effect of motivational factors on the experience of being depleted of energy, suggesting that the energy available for self-regulation is a limited

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39

resource (Baumeister, Bratlavsky, Muraven, & Tice, 1998; Baumeister & Vohs, 2007; Hagger et al., 2010; Inzlicht & Schmeichel, 2012; Job, Dweck, & Walton, 2010; Muraven & Slessareva, 2003). For instance, Muraven, Shmueli, and Burkley (2006) argued that participants are reluctant to invest selfregulatory efforts in a subsequent self-control task, preferring instead to conserve available resources. Similarly, subjective vitality (i.e., feelings of aliveness and energy; Ryan & Frederick, 1997, p. 529) has been directly linked with behavioral outcomes of mental !, & Rosman, 2008; resource depletion (e.g., Muraven, Gagne Muraven, et al., 2006). Subjective vitality represents the energy that one can harness or regulate for purposive actions, and it entails positively toned, energized states (Ryan & Deci, 2008b). Interestingly, some findings suggest that one's perception of his or her available regulatory capacity influences self-regulatory abilities independent of objective capacity measures (Clarkson, Hirt, Jia, & Alexander, 2010). Moreover, recent findings indicate that beliefs about having willpower regulate glucose metabolism (Job, Walton, Bernecker, & Dweck, 2013). Pursuing this line of thought, accumulating evidence supports the assumption that motivational aspectsdbut not necessarily motivation arising from a conscious, volitional sourcedinfluence perceived energy states (Papies & Hamstra, 2010; Papies, Stroebe, & Aarts, 2007). 1.3. Environmental influence on energy estimation The evidence to date suggests that environmental cues unconsciously influence the perception of energy. For example, findings indicate that being outdoors (as opposed to being indoors) is associated with greater subjective vitality (Ryan et al., 2010; Weinstein, Przybylski, & Ryan, 2009). Likewise, research suggests that window views and images of nature reduce sympathetic nervous activation and increase parasympathetic activity (e.g. Brown, Barton, & Gladwell, 2013; Gladwell et al., 2012). Such accumulative evidence on the physical and psychological consequences of exposure to nature supports research based on the attention restoration theory (Kaplan, 1995; Kaplan & Kaplan, 1989), according to which the exposure to a natural, green environment nourishes depleted energy. Findings demonstrate, for example, that participants who went on a 90-min walk through a natural environment reported lower levels of rumination and showed reduced neural activity in an area of the brain linked to risk for mental illness compared with those who walked through an urban environment (Bratman, Hamilton, Hahn, Daily, & Gross, 2015). Research indicates that the energy-nourishing properties of the actual environment can also be obtained by simply viewing pictorial images of nature, an activity that has been found to improve emotional coping. For instance, viewing pictures of natural (as opposed to urban) environments has been associated with lower feelings of stress and depression (Van den Berg, Koole, & van der Wulp, 2003). In addition, people exposed to pictures of restorative environments (e.g., lakes, trees or mountains) were able to perform an executive function task better than people presented with pictures of nonrestorative environments (skyscrapers, buildings, streets, etc.) or with geometric figures (Berto, 2005). By contrast, there is evidence that a wilderness environment impaired self regulation and was more strongly associated with death ruminations than was a cultivated or an urban environment (Koole & Van den Berg, 2004; 2005). Some research has also demonstrated the associative links between features of the physical environment and different psychological experiences. For example, there is evidence that spectacular mountain scenes or impressive waterfalls triggered feelings among people that they are small and humble (Joye & Bolderdijk, 2014). However, the mechanism underlying these physical-psychological associations has not been fully

studied. Taking together, the bulk of the research of environment and the experience of energy for action has mostly compared natural to urban environments, and little is known of how different natural environments may affect subjective estimated energy and, in turn, the motivation for change. 1.4. Embodied cognition, environment and energy Theories of embodied cognition (e.g., Barsalou, 2008; Landau, Meier, & Keefer, 2010; Meier, Schnall, Schwarz, & Bargh, 2012; Winkielman, Niedenthal, Wielgosz, Eelen, & Kavanagh, 2015) provide one possible explanation for the relations between the physical landscape and psychological experiences. These theories suggest that abstract psychological concepts are grounded in physical sensations or environmental conditions. These cues create a stored mental representation (Barsalou, 2008; Wilson, 2002), such that concept activation automatically spreads from the somatosensory experiences to the metaphorically related psychological concepts (Lakoff & Johnson, 1980; Lee & Schwarz, 2014; Meier et al., 2012; Williams, Huang, & Bargh, 2009). For example, heavy objects made job candidates appear more important, rough objects made social interactions appear more difficult, and hard objects increased rigidity in negotiations (Ackerman, Nocera, & Bargh, 2010) This perspective emphasizes the perception-action link (Chartrand & Bargh, 1999; Zmigrod & Hommel, 2013), indicating that behavior is automatically influenced by the activation of physical or perceptual signals. Shalev (2015b) suggested that the integration of embodied features is also determined by the individual's momentary goals as well as individual and cultural differences. There is evidence for the functionality of environmental cues aiding in self-regulatory processes by providing diagnostic input of the economy of action (Balcetis & Cole, 2009; Bargh & Shalev, 2012; Shalev, 2014, 2015b). For example, research indicates that wearing a heavy backpack caused hills to appear steeper and distances to targets to appear greater (Proffitt, 2006). Similarly, Schnall et al. (2010) demonstrated that participants who had consumed a glucose-containing drink perceived the slant of a hill to be less steep than did participants who had consumed a drink containing non-caloric sweetener, suggesting that the perception of the environment's spatial layout is influenced by the energetic resources available for locomotion within it. Ample research demonstrates that physical deficits in homeostasis (e.g., thirst, hunger, pain, temperature sensation) reduce energy resources (Bushman, DeWall, Pond, & Hanus, 2014; Baumeister & Vohs, 2007; Gailliot, 2013; Shalev, 2014; Xu, Schwarz, & Wyer, 2015). Based on this reasoning, Shalev (2014) suggested that perceived homeostatic signals (e.g., thirst) are embodied cues for the estimated economy of action. For example, research has demonstrated that physical, semantic or visual cues of thirst as well as pictorial images of a desert landscape (versus a landscape with water and versus control) reduced subjective persistence in a non-solvable anagram task, increased procrastination and perceived thirst and tiredness, and decreased the perceived subjective vitality, indicating that the activation of embodied homeostatic cues signal the need for energy conservation that inhibits the investment in long-term goals. Additional theoretical perspectives and findings point to a link between arid landscapes and subjective vitality. The association between arid landscapes and low energy resources is also ingrained in culture and historyeeindeed, civilization has historically flourished along coasts rather than deserts. Likewise, models of psychological energy, both past (Selye, 1976) and present (Baumeister & Vohs, 2007), suggest that subjective energy states are a byproduct of

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39

both physical-somatic and psychological factors. Whereas research has long recognized the phenomenological similarity between mental imagery and the visual perception of a physical environment (Kosslyn, Ganis, & Thompson, 2001), little is known of this similarity in the context of embodied cues. Despite evidence for the possible association between embodied effects and environmental signals (e.g., green products, see Mazar & Zhong, 2010), little is known of the possible association between these theoretical perspectives. In addition, environmental psychology research has mainly focused on the differences between indoor and outdoor environments and on their effects on perceived energy; however, the effect of different natural environments on the motivation for change is largely unknown. 1.5. The present research To bridge these gaps in knowledge, in the present set of studies we examined the effect of different natural environments on the motivation to change a maladaptive habit. Based on the findings of Shalev (2014), we predicted that exposure to an arid landscape, using either pictorial or mental images, will deplete perceived energy and reduce one's confidence in their ability to change a maladaptive habit. This prediction was tested in a series of three studies. Study 1 tested the hypothesis that exposure to pictorial images of arid landscapes (versus images of landscapes with water and versus control images) would decrease confidence in the ability to change a maladaptive habit. We also hypothesized that because confidence in the ability to change is influenced by energy estimation, subjective vitality will mediate the relations between environment and the motivation for a change. In Study 2, we conceptually replicated the findings of Study 1 and hypothesized that the mental imagery of a desert versus of a landscape with water would decrease confidence in the ability to change negative habits. Likewise, we predicted the relations between imagined environment and motivation for a change would be mediated by subjective vitality. Finally, to provide additional evidence for the association between an arid landscape and the experience of feeling energy depletion, in Study 3 we further tested the prediction that arid pictorial images would be associated with concepts related to experience of depletion. 2. Study 1 Study 1 was designed to test the hypothesis that images of an arid environment would decrease the motivation to change a maladaptive habit and that subjective vitality would mediate the relations between the environment and the motivation for change. To test these hypotheses, the participants first described a habit that they would like to change and were then assigned to conduct an immersion manipulation task based on the visualization of one of three pictorial imagesdarid landscape, landscape with water, or a control imagedand to rate their confidence in their ability to change their maladaptive habit. 2.1. Methods 2.1.1. Participants A sample of 207 Mechanical Turk respondents (91 females, 116 males) was recruited. Mechanical Turk is a crowd sourcing Internet marketplace of Amazon website services in the US. The age of participants ranged between 18 and 65 years with a mean age of 31.0 years (SD ¼ 11.3 years). We excluded 5% of the participants who did not follow the instructions. The issue of attrition-based dataquality (incomplete responses) is a common problem on Mechanical Turk that has been discussed and addressed by others (e.g.,

Horton, Rand, & Zeckhauser, 2011; Rand, 2012; Robertson & Yokum, 2012; Shalev & Bargh, 2015). 2.1.2. Procedure The study was presented as two unrelated tasks. The participants were first requested to think of a habit that they would like to change and to describe it in their own words in 5e7 sentences. They were then requested to evaluate the extent to which this habit impairs their life on a 10-point scale (1 ¼ not at all and 10 ¼ very much). Next, they were assigned to conduct an immersion manipulation task that involved viewing one of three pictorial images (arid landscape, landscape with water, or a control image; see Appendix 1). The landscape images were identical with the exception of their foregrounds that were taken of other photos and were pasted using Adobe Photoshop software. We used one-way ANOVA to perform an immersion manipulation check (see Shalev, 2014; Study 3; Weinstein et al., 2009). The participants read the following instructions: Imagine yourself in this place. Look around and notice all aspects of your environment. Pay attention to the colors. Notice the textures. Imagine yourself breathing in the air; notice any smells that may be present. Imagine any sounds you may hear. Let yourself take in all the aspects of the environment in front of you. Next, participants were requested to rate, on a 5-point scale (1 ¼ not at all to 5 ¼ very much), the extent to which they felt as if they were in the place described, and they were then asked the following question: “At this moment, how confident are you that you will change your habit?” Participants were asked to rate their answers on the following scale: 0 ¼ “I do not think that I will achieve my goal”; 50 ¼ “I have a 50 percent chance of achieving my goal; 100 ¼ “I think that I will definitely achieve my goal”. They were then requested to complete the Subjective Vitality Scale (SVS; Ryan & Frederick, 1997) based on how they felt at that moment. The SVS consists of seven items: “I feel alive and vital; “I have energy and spirit”; “I don't feel very energetic”; “I feel alert and awake”; “I look forward to each new day”; “I feel energized”; and “I feel so alive I just want to burst.” Responses were noted on a scale of 1 (strongly disagree) to 7 (strongly agree) with respect to how participants felt at that exact moment. Internal consistency for the scale in the present sample was within an acceptable range (Cronbach's a ¼ .87). Next, following several single item measures of mood (Larsen, Norris, McGraw, Hawkley, & Cacioppo, 2009; Russell, Weiss, & Mendelsohn, 1989) the participants completed a 10-point current mood scale (1 ¼ very negative to 10 ¼ very positive). Finally, participants were debriefed about their awareness of the experimental hypotheses. None of the participants was able to identify the purpose of the study with any accuracy. 2.2. Results and discussion 2.2.1. Habits The participants were interested in changing their habits of procrastination (22.2%), overeating (14.9%), smoking (14.9%), nail picking (9.1%), drinking (3%), laziness (3%), rumination (2.4%) and other sporadic habits (e.g., impulsivity, organization, money spending). Missing data for the habit participants wanted to change accounted for 3%. 2.2.2. Immersion manipulation check As expected, a one-way ANOVA revealed no differences in immersion between the experimental groups (F(2,203) ¼ 1.65,

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39

p ¼ .19). Summary statistics for the three conditions are as follows: arid landscape (M ¼ 3.34, SD ¼ 1.09, 95%CI 3.08e3.61), landscape with water (M ¼ 3.36, SD ¼ 1.17, 95%CI 3.08e3.64), and control (M ¼ 3.04, SD ¼ 1.18, 95%CI 2.76e3.33). 2.2.3. The effect of the maladaptive habit on life quality No differences were found in the effect of the maladaptive habit on life quality between the three conditions (F(2,204) ¼ 1.30, p ¼ .27). Summary statistics for the three conditions are as follows: arid landscape (M ¼ 5.91, SD ¼ 2.32, 95%CI 5.35e6.47), landscape with water (M ¼ 6.47, SD ¼ 2.33, 95%CI 5.91e7.03), and control (M ¼ 5.88, SD ¼ 2.57, 95%CI 5.26e6.51). To examine the relationships between the dependent variables of mood, subjective vitality and self-confidence in the ability to change a habit, Pearson's product-moment correlation coefficients were computed before performing the main analyses. A significant, positive correlation was found between subjective vitality and selfconfidence in the ability to change (r ¼ .27, p < .01, N ¼ 207, twotailed), and between mood and self-confidence in the ability to change the maladaptive habit (r ¼ .16, p ¼ .01, N ¼ 207, two-tailed). Additionally, a positive correlation was found between subjective vitality and mood (r ¼ .61, p < .01, N ¼ 207, two-tailed). A one-way between-group MANOVA with the environmental pictorial images as the independent variable and the different measures (self-confidence in the ability to change, subjective vitality and mood) as the dependent variables was conducted to evaluate the effect of the images on the motivation for change. This analysis revealed a significant multivariate main effect for the image (Wilks's l ¼ .91, F(6,404) ¼ 3.03, p < .01, hp 2 ¼ .04). Given the significance of the overall test, a one-way ANOVA was conducted on each of the variables independently. Next, based on our predictions, planned comparisons were conducted between the arid landscape and the control conditions and between the arid landscape and the landscape with water conditions. These comparisons were conducted because we hypothesized e based on the findings of Shalev (2014) and in light of the general principle that “bad is stronger than good” in terms of stimulus effects on judgment, subjective states, and social behavior (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001) e that the effect would be driven primarily by the arid landscape condition. 2.2.4. Confidence in the ability to change a habit We hypothesized that participants who viewed the arid landscape image would be less motivated to change their habit than participants who viewed the image of a landscape with water or the control condition. Indeed, a significant main effect was found for the image condition (F(2,204) ¼ 4.78, p < .01, hp 2 ¼ .04). The analysis was followed by planned comparison tests that suggested that the score for “confidence in the ability to change a habit” was lower in the arid landscape condition (M ¼ 48.62, SD ¼ 21.34, 95%CI 43.49e53.75) than in the landscape with water (M ¼ 59.28, SD ¼ 22.85, 95%CI 53.83e64.74, t(204) ¼ 2.75, p < .01, hp 2 ¼ .03) and control (M ¼ 58.73, SD ¼ 24.18, 95%CI 52.88e64.58, t(204) ¼ 2.59, p ¼ .01, hp 2 ¼ .03) conditions. 2.2.5. Subjective vitality We hypothesized that participants who viewed the arid landscape image would experience lower subjective vitality than participants who viewed the image of a landscape with water or the control condition. As expected, a significant main effect was found for the image condition (F(2,204) ¼ 5.85, p < .01, hp 2 ¼ .05). The analysis was followed by planned comparison tests that suggested that the “subjective vitality” score was lower in the arid landscape condition (M ¼ 3.92, SD ¼ 1.26, 95%CI 3.61e4.22) than in the landscape with water (M ¼ 4.55, SD ¼ 1.33, 95%CI 4.32e4.79,

t(204) ¼ 3.11, p < .01, hp 2 ¼ .04) and control (M ¼ 4.45, SD ¼ .1.33, 95%CI 4.32e4.79, t(204) ¼ 2.60, p ¼ .01, hp 2 ¼ .03) conditions. 2.2.6. Mood In this sample, no differences were observed in mood between the three conditions (F(2,204) ¼ .813, p ¼ .44). Summary statistics for the three conditions are as follows: arid landscape condition (M ¼ 6.31, SD ¼ 1.62, 95%CI 5.92e6.70), landscape with water condition (M ¼ 6.57, SD ¼ 1.66, 95%CI 6.17e6.96), and control condition (M ¼ 6.69, SD ¼ 1.94, 95%CI 6.22e7.16). Following research of ego depletion (Baumeister & Vohs, 2007) and similar findings of Shalev (2014), we speculated that mood will not differ between the conditions. One reason is that perceived low energy may not necessarily be associated with negative mood (See also Thayer, 1997). We further tested the effect of mood using a PANAS (Watson, Clark, & Tellegen, 1988) in Study 2. Because our theory suggests that motivation for change is influenced by energy estimation, we speculated that subjective vitality mediates the effect of pictorial images on the motivation for change. To test this speculation, the data were analyzed by using an SPSS macro designed to test the effects of multiple mediators within the same analysis (MEDIATE; Hayes & Preacher, 2010). Based on the ordinary least squares method of estimation, this macro applied a bootstrapping sampling procedure for the assessment of mediation effects, as recommended by Preacher and Hayes (2008). Within the current investigation, 5000 samples were drawn and 95% bias-corrected confidence intervals were used to determine the significance of mediation effects. All variables were standardized before being introduced into each mediation model. When zero is not in the 95% confidence interval, one can conclude that the indirect effect is significantly different from zero at p < .05 and, thus, that the effect of the independent variable (the comparison between the two experimental conditions) on the dependent variable (motivation for change) is mediated by the proposed mediating variable (subjective vitality). To examine a possible mediating effect of subjective vitality, we compared the arid landscape condition to the landscape with water condition and to the control condition. An examination of indirect pathways from the comparisons to each dependent variable through subjective vitality revealed that when comparing the arid landscape condition to the landscape with water condition, subjective vitality mediated the effect of the experimental condition on the motivation for change (b ¼ .10, 95% CI [.02, .23]). Similarly, when comparing the arid landscape condition to the control condition, subjective vitality mediated the effect of experimental condition on the motivation for change (b ¼ .12, 95% CI [.04, .24]). These findings are consistent with our hypothesis that subjective vitality mediates the relations between pictorial images of different environmental conditions and the confidence in energy resources that are available for changing a maladaptive habit. Although the magnitudes of the effects are relatively small, they are similar to the effect sizes obtained using subtle procedures in a between-subject design in social psychology experiments. 3. Study 2 The results of Study 1 support the hypothesis that pictorial images of an arid environment reduce the motivation for change. Study 2 was designed to test the hypothesis that imagining these images would similarly influence the motivation for change, irrespective of differences in the individuals' respective mental images. The participants were thus requested to mentally visualize an arid landscape or a landscape with water, after which they rated their motivation to change their maladaptive habit.

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39

3.1. Methods 3.1.1. Participants A sample of 240 Mechanical Turk respondents in the US (148 females, 92 males) was recruited. Participant ages ranged between 18 and 70 years of age, with a mean age of 31. 67 years (SD ¼ 9.70 years). Prior to the analysis, we excluded 6.6% of the participants who did not follow the instructions for the visual imagery task. These participants reported either that they did not visualize anything or that they visualized a landscape other than that requested, e.g., oasis. 3.1.2. Procedure The study was presented as two unrelated tasks. The participants were first requested to think about a habit that they would like to change and to write it down. They were then requested to evaluate the extent to which this habit impairs their life on a 6point scale (1 ¼ not at all to 6 ¼ very much). Next, they were assigned to visualize one of two images (arid landscape or landscape with water), and read the instructions for visual imagery task: Please imagine that you were sent to work in a desert and you are currently touring the area for the first time. Look around and notice all aspects of the landscape. Pay attention to the colors Notice the textures. Imagine yourself breathing in the air; notice any smells that may be present. Imagine any sounds you may hear in a desert. Let yourself take in all the aspects of the landscape in front of you. Please describe what you have imagined in 5e7 sentences in the box below text. Next, as in Study 1, the participants were asked to rate the extent to which they felt, at that moment in time, confident that they would change their maladaptive habit. Specifically, the participants read the following instructions: While you are still touring the landscape with water, you think of your life-style habits. Specifically, you remember the negative habit you would like to change. At this moment, how confident are you that you will change your habit according to the following scale: 0 ¼ “I do not think I that will achieve my goal”; 50 ¼ “I have a 50 percent chance of achieving my goal; 100 ¼ “I think that I will definitely achieve my goal. Next, they filled out the vitality scale (SVS; Ryan & Frederick, 1997) (Cronbach's a ¼ .91) and the positive and negative affect schedule (PANAS, Watson et al., 1988) based on how they “currently feel”. The PANAS (Watson et al., 1988) is composed of two 10-item scales, one each to measure positive affect (Cronbach's a ¼ .94) and negative affect (Cronbach's a ¼ .90). In both scales, the participants were asked to respond based on how they currently feel. Finally, the participants were debriefed as to their awareness of the experimental hypotheses. None of the participants was able to identify the purpose of the study. Five participants thought the study tested the effect of environment or environment visualization on mood states or self esteem, but none of the participants accurately identified the specific research hypothesis. 3.2. Results and discussion 3.2.1. Habits The participants were interested in changing habits related mostly to capacity for self-control. Specifically, the habits cited were procrastination (22%), smoking (13%), overeating (10%),

unhealthy eating (8.3%), anger/aggression (5.4%), drinking (4%), assertiveness (3.7%), money spending (3.3%), nail picking (2.4%), and other sporadic habits (e.g., organization, laziness, porn). 3.2.2. The effect of the maladaptive habit on life quality No differences were found in the effect of the maladaptive habit on life quality between the two conditions (t(238) ¼ .439, p ¼ .66). Summary statistics for the two conditions are as follows: arid landscape (M ¼ 3.93, SD ¼ 1.43, 95%CI 3.67e4.19), landscape with water (M ¼ 4.01, SD ¼ 1.19, 95%CI 3.79e4.23). Before analyzing the effects of the mental imagery on the four dependent variables, Pearson's product-moment correlation coefficients were computed to examine the relationships between the dependent variables (see Table 1). As can be seen in Table 1, significant correlations were found between the dependent variables. In light of these results, we conducted a one-way between-group MANOVA with the mental imagery condition as the independent variable and four measures (confidence in the ability to change, vitality, positive affect and negative affect) as the dependent variables was conducted to evaluate the effect of mental imagery (arid landscape versus landscape with water) on the motivation for change. The analysis revealed a significant multivariate main effect for the environmental mental image (Wilks's l ¼ .94, F(4,234) ¼ 3.76, p < .01, hp 2 ¼ .06). Given the significance of the overall test, one way ANOVA tests were conducted to determine differences between the two conditions of the arid landscape and the landscape with water (see Table 2). The findings showed that mental imagery of an arid landscape versus that of a landscape with water significantly influenced the motivation for change and subjective vitality. Additionally, the results indicate that participants under the arid landscape visualization condition were lower in positive affect than participants under the landscape with water visualization condition. However, because visualizing a 'neutral environment' is difficult, the source of the observed positive effect is unclear. The difference between groups in negative affect was not significant. We speculated that similar to Study 1, subjective vitality mediates the effect of the visualized images on the motivation for change. To test this speculation, the data were analyzed using an SPSS macro designed to test the effects of multiple mediators within the same analysis (MEDIATE; Hayes & Preacher, 2010). Also as in Study 1 and as recommended by Preacher and Hayes (2008), in the current investigation, 5000 samples were drawn and 95% bias-corrected confidence intervals were used to determine the significance of mediation effects. All variables were standardized. When zero is not in the 95% confidence interval, one can conclude that the indirect effect is significantly different from zero at p < .05 and thus, that the effect of the independent variable (the comparison between the two experimental conditions) on the dependent variable (motivation for change) is mediated by the proposed mediating variable (subjective vitality). To examine a possible mediating effect of subjective vitality, we compared the arid landscape condition to the landscape with water Table 1 Correlations between self-confidence in the ability to change, vitality, positive affect and negative affect in Study 2 (N ¼ 240). Variables

1. 2. 3. 4.

e .27** .26** ".04

e .71** ".32**

e ".08

Change Vitality Positive affect Negative affect

*p < .05. **p < .01. ***p < .001.

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39 Table 2 ANOVAS, means and standard deviations and 95% conﬁdence intervals by experimental group in Study 2 (N ¼ 240). Scale

Change Vitality Positive affect Negative affect

Landscape

3.90

.04

.01

13.43

.00

.05

5.16

.02

2.27

.13

.00

F(1,238)

Arid

Water

57.04 (28.14) 51.93e62.15 4.10 (1.43) 3.84e4.36 2.99 (.98) 2.81e3.17 1.60 (.69) 1.47e1.72

63.55 (22.49) 59.48e67.62 4.72 (1.15) 4.51e4.93 3.27 (.91) 3.10e3.43 1.47 (.63) 1.35e1.58

Note: For ‘change’ df ¼ (1,237).

condition. Examination of indirect pathways from the comparisons to each dependent variable through subjective vitality revealed that when comparing the arid landscape condition to the landscape with water condition, subjective vitality mediated the effect of the experimental condition on the motivation for change (b ¼ ".12, 95% CI [".24, ".04]). These findings provide additional support of our hypothesis that subjective vitality mediates the relations between both visual and imagined images of different environmental conditions and self-confidence in the ability to change a maladaptive habit. 4. Study 3 Study 3 was designed to test the hypothesis that compared to landscape images with water or of urban settings images of an arid environment will activate depletion related associations. This Study was designed to address the limitations of previous studies. In Study 1, only one instance of each of environment type was presented and the photo used for the control group may look artificial. In Study 2 there was no control group. In Study 3, the participants were shown one of nine pictorial images from Israel of either a desert, a landscape with water or an urban street, and they were asked to rate the extent to which the images are depleting, stressful, relaxing and beautiful. A second goal of this study was to examine the associations activated by different urban street landscapes as compared to natural landscapes. By that we were hoping to better generalize our findings with previous environmental psychology findings. 4.1. Methods 4.1.1. Participants A sample of 209 Mechanical Turk respondents in the US (106 females, 103 males) was recruited. Participant ages ranged between 18 and 64 years, with a mean age of 35.16 years (SD ¼ 11.55 years). Prior to the analysis, we excluded 5.4% of the participants who, in describing a pictorial image different than that presented, did not follow the instructions. 4.1.2. Procedure The study was presented as research of photo evaluation. The participants were exposed to one of nine images (a desert, a landscape with water or an urban setting, see Appendix), after which they read the immersion instructions, as in Study 1. Next, participants were requested to rate, on a 5-point scale (1 ¼ not at all to 5 ¼ very much), the extent to which they felt as if they were in the place described. They were then asked to rate the extent to which the pictorial image is depleting, stressful, beautiful and relaxing on a 7-point scale (1 ¼ not at all to 7 ¼ very much). Next, they filled out

the positive affect (Cronbach's a ¼ .92) and negative affect schedule (Cronbach's a ¼ .92) (PANAS, Watson et al., 1988) based on how they felt at that moment. Finally, they were asked to describe the photo they viewed and were debriefed about their awareness of the experimental hypotheses. Several participants thought the study was about perceptions of nature, but none of the participants was able to accurately identify the study hypotheses. 4.2. Results and discussion 4.2.1. Immersion manipulation check As expected, a one-way ANOVA revealed no differences in immersion between the experimental groups (F(2,206) ¼ 1.06, p ¼ .34). Summary statistics for the three conditions are as follows: arid landscape (M ¼ 3.56, SD ¼ .87, 95%CI 3.35e3.76), landscape with water (M ¼ 3.70, SD ¼ 1.03, 95%CI 3.46e3.95), and urban location (M ¼ 3.47, SD ¼ .94, 95%CI 3.24e3.7). Before analyzing the effects of the mental imagery on the four dependent variables, Pearson's product-moment correlation coefficients were computed to examine the relationships between the dependent variables (see Table 3). As can be seen in Table 3, significant correlations were found between the dependent variables. Therefore, we conducted a oneway between-group MANOVA with the pictorial image condition as the independent variable and six measures (depleting, stressful, beautiful, relaxing, positive affect and negative affect) as the dependent variables was conducted to evaluate the effect of pictorial image (arid landscape versus landscape with water versus urban street) on evaluation and mood. The analysis revealed a significant multivariate main effect for the environmental mental image (Wilks's l ¼ .75, F(12,402) ¼ 4.95, p < .01, hp 2 ¼ .12). Given the significance of the overall test, one way ANOVA tests were conducted to determine differences between the two conditions of arid landscape and landscape with water. 4.2.2. The image is depleting We predicted that the arid landscape images would be evaluated as more depleting than the landscape with water or the urban street images. Consistent with our hypothesis, a significant difference was found between the images (F(2,206) ¼ 7.47, p < .01, hp 2 ¼ .06). The analysis was followed by planned comparison tests that suggested that the depletion score was higher in the arid images condition (M ¼ 3.08, SD ¼ 1.83, 95%CI 2.65e3.51) than in the landscape with water images condition (M ¼ 2.01, SD ¼ 1.49, 95%CI 1.66e2.37), t(206) ¼ "3.84, p < .01, hp 2 ¼ .06), but there was no difference between the arid image (M ¼ 3.08, SD ¼ 1.83, 95%CI 2.65e3.51) and urban image conditions (M ¼ 2.65, SD ¼ 1.63, 95%CI 2.25e3.05), (t(206) ¼ "1.52, p ¼ .12, hp 2 ¼ .01). 4.2.3. The image is stressful Based on our prediction that the arid landscape images would be evaluated as more stressful than the landscape with water or Table 3 Correlations between image evaluations, positive affect, and negative affect in Study 3 (N ¼ 209). Variables

1. 2. 3. 4. 5. 6.

e .57** ".43** ".47** ".06 .24**

e ".53** ".55** ".09 .38**

Depleting Stressful Beautiful Relaxing Positive affect Negative affect

*p < .05. **p < .01. ***p < .001.

e .77** .38** ".13*

e .33** ".12

e ".10

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urban street images, a significant main effect was found for the image condition (F(2,206) ¼ 17.44, p < .01, hp 2 ¼ .14). Consistent with our prediction, planned comparison tests indicated that the stress score was higher in the arid landscape images (M ¼ 2.25, SD ¼ 1.64, 95%CI 1.86e2.64) than in the landscape with water (M ¼ 1.35, SD ¼ .86, 95%CI 1.15e1.56) (t(207) ¼ "3.69, p < .01, hp 2 ¼ .05). However, arid landscapes were less stressful than the urban street images (M ¼ 2.80, SD ¼ 1.72, 95%CI 2.38e3.23), (t(207) ¼ 2.19, p ¼ .02, hp 2 ¼ .02). 4.2.4. The image is beautiful We predicted that the arid landscape images would be evaluated as less beautiful that the landscape with water image. A significant difference was found between the image conditions (F(2,206) ¼ 13.96, p < .0001, hp 2 ¼ .11). Planned comparison tests indicated that, consistent with the prediction, the arid landscape images (M ¼ 4.31, SD ¼ 1.64, 95%CI 3.92e4.69) were perceived as less beautiful than the landscape with water images (M ¼ 5.00, SD ¼ 1.74, 95%CI 4.59e5.41), (t(206) ¼ 2.47, p ¼ .01, hp 2 ¼ .02). However, the arid images were perceived as more beautiful than the urban street images (M ¼ 3.48, SD ¼ 1.63, 95%CI 3.08e3.89), (t(206) ¼ "2.87, p < .01, hp 2 ¼ .03). 4.2.5. The image is relaxing We predicted that the arid landscape images would be evaluated as less relaxing than the landscape with water images. Consistent with our hypothesis, a significant difference was found between the image conditions (F(2,206) ¼ 13.69, p < .01, hp 2 ¼ .11). Planned comparison tests indicated that the arid images were perceived as less relaxing (M ¼ 4.50, SD ¼ 1.67, 95%CI 4.11e4.9) than the landscape with water images (M ¼ 5.46, SD ¼ 1.70, 95%CI 5.06e5.87) (t(206) ¼ 3.27, p < .01, hp 2 ¼ .04). However these images were perceived as more relaxing than the urban street images (M ¼ 3.91, SD ¼ 1.91, 95%CI 3.44e4.38) (t(206) ¼ "1.96, p ¼ .05, hp 2 ¼ .01). 4.2.6. Negative affect A one-way ANOVA yielded no differences in immersion between the experimental groups (F(2,206) ¼ 2.60, p ¼ .07, hp 2 ¼ .02). As expected, planned comparison tests indicated that the arid images (M ¼ 1.40, SD ¼ .67, 95%CI 1.24e1.56) were not perceived as more negative than the than the landscape with water images (M ¼ 1.21, SD ¼ .45, 95%CI 1.10e1.31), (t(206) ¼ "1.92, p ¼ .06, hp 2 ¼ .01) and urban location (M ¼ 1.41, SD ¼ .63, 95%CI 1.26e1.57). (t(206) ¼ .13, p ¼ .89, hp 2 ¼ 0). 4.2.7. Positive affect A one-way ANOVA revealed no differences in positive affect between the experimental groups (F(2,206) ¼ 1.82, p ¼ .16, hp 2 ¼ .01). As expected, planned comparison tests indicated that the arid images (M ¼ 3.07, SD ¼ 1.03, 95%CI 2.83e3.32) were not perceived as more positive than the than the landscape with water images (M ¼ 3.11, SD ¼ 1.05, 95%CI 2.87e3.36), (t(206) ¼ .26, p ¼ .79, hp 2 ¼ .00) and urban location (M ¼ 2.81, SD ¼ .87, 95%CI 2.60e3.03), (t(206) ¼ "1.52, p ¼ .12, hp 2 ¼ .01). These findings provide additional support for our hypothesis that different arid images are associated with the experience of depletion. Interestingly, Lazarus (1996) defined stress in terms of a lack of resources, such that the person perceives that demands of a given situation exceed the personal and social resources the individual is able to mobilize, a notion that helps explain why the arid landscape was perceived as stressful. Furthermore, the findings of the present research provide additional evidence that urban streets are perceived as more stressing, less beautiful and less relaxing than the natural environment. Finally, this research provides additional evidence that

perceived energy is not necessarily associated with mood states. 5. Discussion Extensive environmental psychology research has demonstrated that natural environments replenish people's energy levels (Kaplan, 1995; Kaplan & Kaplan, 1989). However, little is known about the effects that different environmental conditions can have on the motivation for change. The three studies conducted here unanimously showed that both visual perception and mental imagery of an arid landscape (versus a landscape with water and versus a control) reduced perceived energy and the motivation to change a maladaptive habit. In Study 1, a pictorial image of an arid landscape reduced confidence in the ability to change a maladaptive habit. Perceived vitality was found to mediate the relations between environment and the motivation for change, suggesting that motivation for change is influenced by energy estimation. In Study 2, mental images of arid landscapes reduced the motivation for change relative to images of landscapes with water, indicating the interplay of semantic concepts and physical sensations as well as the similarity between visual perception and “seeing with the mind's eye”. As in Study 1, perceived vitality mediated the relations between environment and the motivation for change. Finally, in Study 3, participants evaluated arid images as more depleting and stressful than images of landscapes with water. This study also indicated that urban streets where perceived as more stressful, less beautiful and less relaxing than natural landscapes, a finding that is in line with past environmental psychology research (Koole & Van den Berg, 2005; Van den Berg et al., 2003). These results support our prediction that natural environments influence perceived available resources, suggesting that an arid landscape is associated with reduced subjective vitality and, consequently, low motivation to invest in long-term goals (e.g., change habitual behavior). Our results are also consistent with the embodied cognition perspective, according to which physical experiences spread to their metaphorically-related social concepts and may influence judgment and behavior (Meier et al., 2012; Meier, Scholer, & Fincher-Kiefer, 2014). The present research thus demonstrates, in line with the findings of Shalev (2014), that an arid landscape is an embodied signal for need for energy conservation. Research of embodied cognition has shown that the activation of embodied signals may lead to different patterns of activation across different contexts (Barsalou, 2008). Based on the reasoning that embodied dryness cues are perceptually integrated according to their momentary functions in terms of each individual's goals (Shalev, 2015b), the research provides evidence that when the goal was to change a negative habit, the arid landscape images restrained goal progress. The effect of motivational states on embodied cues integration also helps explain the finding in Study 3 indicating that the exposure to arid images evoked among the study participants different associations related to deficiency (e.g., stress, depletion) and not only a single association. These findings are also in line with the somatic marker hypothesis (Damasio, Tranel, & Damasio, 1991), according to which the perception of somatic state information influences our motivational state vis# a-vis whether to approach or to avoid a given situation. Moreover, these somatic signals can also function at a nonconscious level, where the individual is unaware of his or her bodily activity, thus indicating functionality related to bodily signaling (Bargh & Shalev, 2012; Bargh, Schwader, Hailey, Dyer, & Boothby, 2012). The study results support previous findings in environmental psychology indicating that the urban environment is less enriching than the natural environment. Our findings, however, provide additional insight by examining how different natural landscapes are perceived, indicating that the source of perceived differences

I. Shalev / Journal of Environmental Psychology 45 (2016) 30e39

between landscapes is not necessarily associated with affective evaluation. There are several notable limitations to these studies. First, the study findings were based primarily on data collected through selfreport measures. Second, the findings of Study 1 were based on a single pictorial image for each condition. Finally, the present study includes online Mturk participants, which reduces our control over the environmental conditions under which data were collected. Despite these limitations, however, the present work investigates previously unexplored relations between pictorial and mental images of arid landscapes, subjective vitality and the motivation to change negative habits. In so doing, it contributes to the growing body of studies that examines the relevance of environmental conditions to the motivation for change (Shalev, 2015a). Our findings suggest that visual perception or mental imagery of an arid environment functions similarly toward goal activation (Bargh & Morsella, 2010; Shalev, 2015a), insofar as either leads to biases in the estimations people make about their own energy levels (Kahneman, 2003). Based on this reasoning, we suggest that future

research should examine whether awareness of bodily homeostatic condition as an additional source of bias in judgment can offset the

potential effects of energy estimations on decision. At the operational level, our findings have potential implications for coping with individuals' resistance to change by using intervention techniques based on mental imagery, physical sensation or visual images (Shalev & Bargh, 2011). Our three studies revealed a consistent effect of pictorial images and mental imagery of arid landscapes on motivation to change a maladaptive habit, suggesting environmental signals influence energy estimation and could have potentially important implications for procedures designed to change habitual behavior. Interestingly, relaxation techniques involve visual imagery of landscapes with water (Overholser, 1991). The current research provides support for the influence of arid pictorial and mental images on one's energy state. Future studies should examine the effects of arid landscapes on different self control applications. For example, research should explore the possibility that pictorial images of arid landscapes on a marketing website may hinder efforts to consume healthy food. Likewise,

future research can also investigate the effects that pictures of arid landscapes have on individuals' motivation to improve their unhealthy lifestyles when given the opportunity to attend sports clubs or to visit special clinics dedicated to diet. As with most psychological situations, the process of change may elicit different reactions in different people. Understanding the relations between psychological background, cultural conditions and physical environmental conditions remains a challenge for future research (Leung, Qiu, Ong, & Tam, 2011). Acknowledgment Thanks you Tzlil Alfasi, Ester Friedman, Roni Gertel, Maya Lach, Shahar Levy, Gal Noach, Hadar Tal and Yael Vitelson for your help in data collection and useful discussion over lab meetings. Appendix Photos used for Study 1.

Samples of photos used in Study 3.

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