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Record W2117374026 · doi:10.1080/02699930341000437

Self‐induced memory distortions and the allocation of processing resources at encoding and retrieval

2004· article· en· W2117374026 on OpenAlexaff
Matthew S. Shane, Jordan B. Peterson

Bibliographic record

VenueCognition & Emotion · 2004
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecallEncoding (memory)PsychologyTask (project management)Cognitive psychologyInformation processingNegative informationFree recallSocial psychology

Abstract

fetched live from OpenAlex

The present study evaluated the possibility that memory distortions characteristic of repression are due, at least in part, to the reduced allocation of processing resources to unwanted or threatening information. Such reduced processing could occur early, during encoding processes, or conversely, could occur later, during more elaborative, or retrieval‐based processes. Repressors and nonrepressors completed a free recall task, which included positively, negatively, and neutrally valenced words, and also completed a go/no‐go task previously designed to evaluate the willingness to allocate processing resources to both positive and negative contingent feedback, at encoding, and at retrieval. Results indicated that repressors did evidence reduced memory for negative, but not positive or neutral words, on the free recall task. Repressors also manifested reduced allocation of additional processing resources toward negative contingent feedback as compared to nonrepressors. Finally, the allocation of processing resources at retrieval, but not at encoding, was found to mediate the relationship between participant's self‐deceptive enhancement scores and the number of negative words recalled. These results support a model of repression based on motivated attempts to strategically avoid cognitively processing aversive information.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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.

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

Citations18
Published2004
Admission routes1
Has abstractyes

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