Affective priming in major depressive disorder
Bibliographic record
Abstract
Research on cognitive biases in depression has provided considerable evidence for the impact of emotion on cognition. Individuals with depression tend to preferentially process mood-congruent material and to show deficits in the processing of positive material leading to biases in attention, memory, and judgments. More research is needed, however, to fully understand which cognitive processes are affected. The current study further examines the impact of emotion on cognition using a priming design with facial expressions of emotion. Specifically, this study tested whether the presentation of facial expressions of emotion affects subsequent processing of affective material in participants with major depressive disorder (MDD) and healthy controls (CTL). Facial expressions displaying happy, sad, angry, disgusted, or neutral expressions were presented as primes for 500 ms, and participants' speed to identify a subsequent target's emotional expression was assessed. All participants displayed greater interference from emotional vs. neutral primes, marked by slower response times to judge the emotion of the target face when it was preceded by an emotional prime. Importantly, the CTL group showed the strongest interference when happy emotional expressions served as primes whereas the MDD group failed to show this bias. These results add to a growing literature that shows that depression is associated with difficulties in the processing of positive material.
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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) | 0.002 | 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".