Coherence of attention and memory biases in currently and previously depressed women
Bibliographic record
Abstract
Previous research has found that depression is characterised by biased processing of emotional information. Although most studies have examined cognitive biases in isolation, simultaneous examination of multiple biases is required to understand how they may interact and influence one another to produce depression vulnerability. In this study, the attention and memory biases of currently depressed, previously depressed, and never depressed women were examined using the same stimuli and a unified methodology. Participants viewed negative, positive, and neutral words while their eye gaze was tracked and recorded. After a distraction task, participants completed an incidental recognition test that included words from the eye-tracking task and new words. The results supported the hypothesised mediation model for positive words: currently depressed women had a reduced attention bias for positive words and, in turn, had poorer memory for positive words relative to never depressed women. Previously depressed women, however, showed a lack of coherence between attention and memory biases for positive words. The groups did not differ in their attention or memory biases for negative words. The findings provide novel evidence in support of a causal link between the absence of protective attention and memory biases for positive information in clinical depression.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".