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Record W3197388750 · doi:10.1037/rev0000325

On the role of similarity in mental accounting and hedonic editing.

2021· article· en· W3197388750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Review · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsBooth University College
Fundersnot available
KeywordsCategorizationMental accountingPsycINFOSimilarity (geometry)PsychologySalientMental representationPreferenceProcess (computing)Mental processMental modelCognitive psychologySocial psychologyComputer scienceCognitionActuarial scienceStatisticsArtificial intelligenceEconomicsMEDLINEMathematicsCognitive science

Abstract

fetched live from OpenAlex

, which generates testable behavioral predictions on people's preferences over the timing of outcomes given similarity-based constraints on mental accounting operations. Six studies provide support for the predictions: People prefer to experience similar losses close together in time and spread dissimilar losses apart; the reverse is true for gains, with a preference for dissimilar gains close together in time and similar gains spread apart across time. Importantly, our model is able to rationalize prior evidence that has found only limited support for the predictions of mental accounting and hedonic editing. Once the psychological process of similarity and categorization is explicitly incorporated into a formal model of mental accounting, its predictions are supported by the data. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.171
GPT teacher head0.447
Teacher spread0.276 · 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