The Potentiation of Associative Memory by Emotions: An Event-Related FMRI Study
Bibliographic record
Abstract
Establishing associations between pieces of information is related to the medial temporal lobe (MTL). However, it remains unclear how emotions affect memory for associations and, consequently, MTL activity. Thus, this event-related fMRI study attempted to identify neural correlates of the influence of positive and negative emotions on associative memory. Twenty-five participants were instructed to memorize 90 pairs of standardized pictures during a scanned encoding phase. Each pair was composed of a scene and an unrelated object. Trials were neutral, positive, or negative as a function of the emotional valence of the scene. At the behavioral level, participants exhibited better memory retrieval for both emotional conditions relative to neutral trials. Within the right MTL, a functional dissociation was observed, with entorhinal activation elicited by emotional associations, posterior parahippocampal activation elicited by neutral associations, and hippocampal activation elicited by both emotional and neutral associations. In addition, emotional associations induced greater activation than neutral trials in the right amygdala. This fMRI study shows that emotions are associated with the performance improvement of associative memory, by enhancing activity in the right amygdala and the right entorhinal cortex. It also provides evidence for a rostrocaudal specialization within the MTL regarding the emotional valence of associations.
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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) | 0.001 | 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".