David galbraith juin 2013

Building content during writing: A dual process model David Galbraith d.w.galbraith@soton.ac.uk Veerle Baaijen, University of Groningen ANR ĂŠcritures, Paris 21 June 2013

Introduction • Writing as discovery • 2 contrasting models – Problem solving models – Dual process model

• Keystroke analysis – Processes and text quality – Idea change and discovery

Writing as problem solving (Hayes, 1996; Bereiter and Scardamalia, 1987)

• The thinking behind the text – Retrieval of content from long-term memory – Manipulation in working memory

• Knowledge telling v knowledge transforming – Adapting to external rhetorical constraints – Managing cognitive load (Kellogg, 1994)

• Problem solving all the way down?

– Text production as local planning – Passive output process

Descriptions by expert writers (selected from Murray, 1978) • W. H. Auden. Language is the mother, not the handmaiden, of thought; words will tell you things you never thought or felt before.

• Wright Morris: The language leads, and we continue to follow where it leads.

Self-monitoring (Gangestad & Snyder, 2000) • High self-monitors – Manage self-presentation to suit social situation – Assume that they are more likely to direct their writing towards rhetorical goals (external constraints).

• Low self-monitors – More concerned with self-expression – They are more likely to express their ideas directly as they unfold (internal constraints).

New ideas as a function of self-monitoring and mode of writing Galbraith (1992, 1996) 8 7 6 5 Low SM High SM

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2 1 0 Notes

Prose

Dual process model (Galbraith, 2009) •

Knowledge-retrieval process – Retrieval of ideas from explicit memory store (hippocampus) – Manipulation of ideas in working memory to create rhetorically appropriate global model – Dependent on spatial component of working memory – Leads to creation of single knowledge object in episodic memory (but not understanding)

•

Knowledge-constituting process – – – –

Synthesis of ideas within semantic memory (neo-cortex) Dispositionally guided text production Sequential process, not dependent on spatial component of working memory Leads to formulation of ideas corresponding to writer’s implicit understanding of the topic

A simple feedforward network • Units sum up input activation and pass it on to next layer • Superpositional storage – Fixed weights represent knowledge and guide processing

• Contextually specific synthesis of output in response to input

Knowledge-constituting process

• Writer’s disposition = fixed connections between features in a high dimensional semantic space (internal constraints) • Ideas created by constraint satisfaction within network (content synthesis) • Successive utterances produced by inhibitory feedback from output to disposition (self-movement of thought)

Relationship between the 2 processes • Both processes required for effective writing • Fundamental conflict because processes are optimised under opposing conditions • Low and high self-monitors prioritise different components – Low SM prioritise knowledge-constituting and use bottomup strategy – High SM prioritise knowledge-transforming and use topdown strategy

Knowledge constituting in text production Baaijen (2012); Baaijen, Galbraith & de Glopper (2010)

• 84 university students writing article for university newspaper about dependence on computers and internet.

– Low and high self-monitors – Outline planned v synthetically planned (5 minutes; single draft) – Process measures derived from key-stroke logs

Procedure • Before writing – List ideas about topic (10 minutes) – Rate knowledge of topic (7 point scale)

• During writing – 5 minutes planning (outline or synthetic) – 30 minutes to write article

• After writing – Rate knowledge of topic again – List ideas again – Rate correspondence of ideas in list 1 and 2

Keystroke measures Baaijen, Galbraith & de Glopper, (2012) • Pauses – Within words, between words, sentences, paras

• Bursts – P bursts (full bursts) – R bursts (incomplete revised bursts)

• Transitions – Linear / nonlinear percentage – Ww, bw, bss, bs,

Principal Component Analysis Summary of Principal Component Analysis with Varimax Rotation for 2 Factor Solution Rotated Factor Loadings Variables Component 1 Component 2 Planned sentence Global linearity production Percentage of R-bursts -.945 -.025 Percentage of leading edge R-bursts -.931 .133 Percentage of P- bursts .834 .393 Total time spent on reflection .710 .468 Number of pauses >2 seconds between words controlled .701 -.038 for text length Percentage of revision deletions of total process words -.620 -.382 Mean pause time between sentences .599 .258 Percentage of Insertion-Bursts .103 -.851 Sentence Linearity Index .174 .846 Total time spent on revision between sentences, -.022 -.797 paragraphs and word transitions Percentage of linear sentence transitions .092 .775 Number of cycles -.236 -.717 Number of pauses >2seconds between sentences .455 .611 controlled for text length Percentage of linear word transitions .373 .561 Eigenvalues 6.3 2.8 % of variance 44.98 20.24 Îą .89 .87 Note that factor loadings over .5 appear in bold 14

Key-strokes: Principal component analysis (Baaijen, Galbraith & de Glopper, 2012)

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› Planned sentence production 

 

Long pauses between and within sentences, combined with cleaner bursts (Less R-Bursts, more P-Bursts) and less within sentence revision High scores: Long pauses, clean bursts 7 measures; 45% variance; a = .89

› Global linearity 

 

Linear transitions between sentences, fewer insertions earlier in the text and fewer revisions between sentences and paragraphs.

High scores: Linearly produced texts 7 measures; 22% variance; a = .87

Relationship between processes and text quality

Relationships between idea change and discovery Baaijen (2012) Baaijen et al (in prep)

• Identify “old” and “new” ideas in the text – “old” = ideas reproduced after writing – “new” = ideas only produced after writing – 2 judges • 93% agreement about occurrence of ideas in text • 86% agreement about location

• Relate to burst order in keystroke log – Scaled as a proportion of total number of bursts

• Generalized additive mixed models using mgcv R package (Baayen, 2008; Wieling , 2012)

Conclusions for idea change • Two distinct kinds of knowledge change – Top-down (High SM outline) • old ideas contextualised within new framework • not dependent on text production

– Bottom-up (Low SM synthetic) • new ideas emerge in course of text production • does it depend on active reconstitution of old ideas or do these spontaneously recur? (global linearity measure?)

• Mixture in other conditions?

General conclusions • 2 different processes involved in discovery • Individual differences in how these are combined – Broad support for dual process model

• Exploring how content is built during writing – Text combined with time – Actions are turned into objects