Testing the Cognitive Vulnerability Hypothesis in Previously Depressed Women: Effects of a Sad Mood Induction on Attention and Memory Biases
Bibliographic record
Abstract
Researchers have documented biases in attention and memory in depressed and dysphoric individuals. Several studies have examined these biases among previously depressed individuals, and while there is evidence that attention and memory biases persist in remitted depression, the findings are not consistent. One limitation of previous research is that few studies have used a stress or negative mood induction (MI) to activate the cognitive vulnerability posited to underlie attention and memory biases. The present study used a free-viewing eye-tracking paradigm to assess previously depressed participants’ attention biases for positive and negative words before and after a sad MI. Memory biases were assessed using tests of incidental recognition. Only participants who were successfully mood-induced were included in the analyses. Unlike previously depressed participants, never depressed participants exhibited attention and memory biases that favored positive over neutral words, both before and after the sad MI, suggesting a protective bias that is maintained in a sad mood state. After the sad MI, previously depressed participants had smaller attention biases for positive words and smaller memory biases for negative words compared to never depressed participants. The findings are generally supportive of the notion that cognitive vulnerability is maintained to some extent in remitted depression and can be influenced by a sad mood.
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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 | 0.003 |
| 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 | 0.000 |
| 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".