Evidence for the differential salience of disgust and fear in episodic memory.
Bibliographic record
Abstract
Studies of emotional memory typically focus on the memory-enhancing effects of emotional dimensions such as arousal and valence. However, it is unclear to what extent different emotional categories could have distinct effects on memory over and above these dimensional influences. We tested this possibility by investigating the impact of two negative, highly arousing, and withdrawal-related emotions-disgust and fear--on attention and subsequent memory. To index differential attention during encoding, participants performed a speeded line discrimination task (LDT) while viewing disgusting and fearful photographs of similar valence and arousal, which were assessed for later memory. LDT performance was slower, and subsequent recall and recognition were greater, for disgusting compared to both fearful and neutral images. Disgust enhancement of memory remained significant even when controlling for attention at encoding and for arousal, visual salience, and conceptual distinctiveness. Receiver-operating curve analyses indicated that disgust enhancement of memory was due to increased sensitivity, rather than response bias. Thus, disgust appears to have a special salience in memory relative to certain other emotions, suggesting that a purely dimensional model of emotional influences on cognition is inadequate to account for their effects. We speculate that disgust enhancement of memory could arise from an origin in conditioned taste aversion, a highly enduring form of implicit memory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".