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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".