Learning to downregulate fear associations: Evidence from overexpectation in females.
Bibliographic record
Abstract
At the core of adaptive behavior is the ability to accurately predict relationships between environmental events. Such predictions require associative relationships to be updated in the face of changing contingencies. One example of such updating is the overexpectation effect. Prior investigations into overexpectation in the appetitive domain revealed that female rats require additional training to manifest the effect compared to males. This finding raises two possibilities, namely, that females are also slower at updating (reducing) fear expectancies in overexpectation, reflecting a general learning trait across valence domains, or, conversely, that they are comparable or perhaps even faster at reducing fear expectancies compared to males. To test these hypotheses, we trained male and female rats in aversive overexpectation. Our results show that while males show the overexpectation effect following two trials of overexpectation training, females are less likely to do so given the same parameters. Increasing the number of overexpectation training trials from two to four yielded a successful overexpectation effect in females. These results align with prior research in the appetitive domain (Lay, Frate, et al., 2020), providing evidence that females require more trials to downregulate previously acquired associations, whether the outcome is appetitive or aversive. These data carry important implications for the behavioral, neural, and hormonal mechanisms that support reduction in conditioned responding in both sexes and may shed light on sex differences reported in anxiety-related disorders. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".