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Integrating neuro-psychological habit research into consumer choice models

2025· article· en· W4415341739 on OpenAlexafffund
Ryan Webb, Jessica Fong, Peter Landry, Julia Levine, Alex Steiny Wellsjo, Olivia Natan, Asaf Mazar, Clarice Zhao, Phillippa Lally, Sanne de Wit, John P. O’Doherty, Andrew T. Ching, Raphael Thomadsen, Matthew Osborne, Mark E. Bouton, Wendy Wood, Colin Camerer

Bibliographic record

VenueInternational Journal of Research in Marketing · 2025
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcGill UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHabitConsumer choiceContext (archaeology)Consumer behaviourField (mathematics)Consumer research

Abstract

fetched live from OpenAlex

We discuss how habit is defined across disciplines that study human choices. In particular, we examine the role of learning in forming habits, the roles of automaticity, context and cues, limited attention/consideration, and whether habits reflect a change in preferences or a choice system which is partly decoupled from preferences. These constructs are used to create a framework for economics, psychology, and marketing research that is suitable for the analysis of consumer choice experiments and field datasets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.315
GPT teacher head0.614
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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