Directed forgetting of emotionally toned items and mental health: a meta-analytic review
Bibliographic record
Abstract
Item- and list-method directed forgetting paradigms have been used to study forgetting of emotionally toned items in clinical and control group populations for several decades. Meta-analysis of item-method studies found that clinical populations retained more remember- than forget-cued items of each valence. These effects were comparable to that shown by control populations for positive and negative items, but less than that shown by controls on neutral items. Encoding deficits may underlie clinical populations' item-method directed forgetting since those populations retained fewer remember-cued items of each valence compared to control populations. Moderator analysis indicated larger effect size variability for some clinical populations (e.g., anxiety disorders) than other populations (e.g., PTSD, schizophrenia). Meta-analysis of list-method directed forgetting among clinical populations revealed only List 1 forgetting or costs for neutral items; i.e., better memory for to-be-remembered than forgotten List 1 neutral items, but no List 2 enhancements or benefits; i.e., better memory for List 2 items among those told to forget than remember List 1 items, for any item valence. Control populations showed costs and benefits for all item valences. Results from both paradigms are discussed in terms of clinical-control population differences in executive processes. Limitations of the meta-analyses and suggestions for future research are presented.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".