Depression Levels Are Associated with Reduced Capacity to Learn to Actively Avoid Aversive Events in Young Adults
Bibliographic record
Abstract
Depression and anxiety are often characterized by altered reward-seeking and avoidance, respectively. Yet less is known about the relationship between depressive symptoms and specific avoidance behaviors. To address this gap, we conducted two studies. In Study 1, undergraduates and online workers completed an uninstructed go/no-go avoidance task ( N Total = 465) as a reverse translation of a rodent paradigm. Participants exhibited a wide range of symptom scores on the Beck Depression Inventory-II (BDI-II), ranging from low to severe. In Study 1, cues were used to signal the response type (go/active vs no-go/inhibitory) required to avoid an aversive sound. Higher depressive scores were associated with poorer acquisition of active avoidance in undergraduates. Overall participants showed lower accuracy for active than inhibitory avoidance. To examine whether the better no-go trial performance reflected a prepotent response to avoid aversive outcomes, in Study 2, undergraduates ( N Total = 330) completed a version of the task that included reward-seeking. Here all participants showed higher accuracy for active reward-seeking and inhibitory avoidance, consistent with a prepotent response to inhibit action to avoid aversive consequences. These findings suggest that in young adults, depressive symptoms are associated with difficulty in overriding prepotent responses to actively avoid aversive outcomes in the absence of reward. This work bridges the gap between preclinical animal models and clinical research, offering insights that could guide the development of more targeted clinical interventions.
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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 | 0.000 | 0.001 |
| 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".