Biased attention retraining in dysphoria: a failure to replicate
Bibliographic record
Abstract
The present study replicated Wells and Beevers [(2010). Biased attention and dysphoria: Manipulating selective attention reduces subsequent depressive symptoms. Cognition & Emotion, 24, 719-728] and examined the longitudinal effects of attentional retraining on symptoms of depression. Dysphoric undergraduate psychology students were randomly assigned into either a neutral or control training condition. Training was administered using a dot-probe task that presented participants with pairs of pictures (of sad and neutral content) that were followed by a probe that participants had to respond to. Training took place over four sessions during a two-week period, followed by a final follow-up session two weeks later. Mood was measured at baseline, post-training, and at follow-up. All participants showed a significant reduction in depressive symptoms throughout the study, F(1.7, 73.55) = 21.19, p < .001; but the attentional retraining did not demonstrate any advantage over the control condition. Results were inconsistent with those of Wells and Beevers [(2010). Biased attention and dysphoria: Manipulating selective attention reduces subsequent depressive symptoms. Cognition & Emotion, 24, 719-728]. Implications of the findings on research on attentional retraining in the context of depression are discussed.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".