Emotion, directed forgetting, and source memory
Bibliographic record
Abstract
We investigated the role of emotion on item and source memory using the item method of directed forgetting (DF) paradigm. We predicted that emotion would produce source memory impairment because emotion would make it more difficult to distinguish between to-be-remembered (R items) and to-be-forgotten items (F items) by making memory strength of R and F items similar to each other. Participants were presented with negatively arousing, positively arousing, and neutral pictures. After each picture, they received an instruction to remember or forget the picture. At retrieval, participants were asked to recall both R and F items and indicate whether each item was an R or F item. Recall was higher for the negatively arousing than for the positively arousing or neutral pictures. Further, DF occurred for the positively arousing and neutral pictures, whereas DF was not significant for the negatively arousing pictures. More importantly, the negatively arousing pictures, particularly the ones with violent content, showed a higher tendency of producing misattribution errors than the other picture types, supporting the notion that negative emotion may produce source memory impairment, even though it is still not clear whether the impairment occurs at encoding or retrieval.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".