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Record W2008482689 · doi:10.1177/1948550610379425

Trait Approach Motivation Relates to Dissonance Reduction

2010· article· en· W2008482689 on OpenAlexaff
Cindy Harmon‐Jones, Brandon J. Schmeichel, Michael Inzlicht, Eddie Harmon‐Jones

Bibliographic record

VenueSocial Psychological and Personality Science · 2010
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCognitive dissonancePsychologySelf-perception theorySelf-justificationSocial psychologyTraitProcess (computing)Action (physics)PersonalityCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

Over 50 years of work on cognitive dissonance theory has suggested that dissonance reduction is a motivated process. However, no research has unambiguously demonstrated the direction of this motivation—whether it is approach or avoidance oriented. The action-based model of dissonance proposes that dissonance reduction is an approach-related process that assists in the implementation of decisions. It follows from the action-based model that approach-related personality traits should be related to greater dissonance reduction. The current research tested this idea. Study 1 found that trait behavioral approach sensitivity (BAS) related to more spreading of alternatives (more liking for the chosen over the rejected decision alternative) following a difficult decision. Study 2 found that BAS related to attitudes being more consistent with recent induced compliance behavior. This research therefore suggests that dissonance reduction is an approach-motivated process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.156
GPT teacher head0.456
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations49
Published2010
Admission routes1
Has abstractyes

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