Stress promotes generalization of older but not recent threat memories
Bibliographic record
Abstract
Stress broadly affects the ability to regulate emotions and may contribute to generalization of threat-related behaviors to harmless stimuli. Behavioral generalization also tends to increase over time as memory precision for recent events gives way to more gist-like representations. Thus, acute stress coupled with a delay in time from a negative experience may be a strong predictor of the transition from normal to generalized fear expression. Here, we investigated the effect of a single-episode acute stressor on generalization of aversive learning when stress is administered either immediately after an aversive learning event or following a delay. In a between-subjects design, healthy adult volunteers underwent threat (fear) conditioning using a tone-conditioned stimulus paired with an electric shock to the wrist and another tone not paired with shock. Behavioral generalization was tested to a range of novel tones either on the same day (experiment 1) or 24 h later (experiment 2) and was preceded by either an acute stress induction or a control task. Anticipatory sympathetic arousal [i.e., skin conductance responses (SCRs)] and explicit measures of shock expectancy served as dependent measures. Stress administered shortly after threat conditioning did not affect behavioral generalization. In contrast, stress administered following a delay led to heightened arousal and increased generalization of SCRs and explicit measures of shock expectancy. These findings show that acute stress increases generalization of older but not recent threat memories and have clinical relevance to understanding overgeneralization characteristics of anxiety and stress-related disorders.
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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 | 0.000 |
| 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".