Self‐induced memory distortions and the allocation of processing resources at encoding and retrieval
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".