Remembering one year later: Role of the amygdala and the medial temporal lobe memory system in retrieving emotional memories
Bibliographic record
Abstract
The memory-enhancing effect of emotion can be powerful and long-lasting. Most studies investigating the neural bases of this phenomenon have focused on encoding and early consolidation processes, and hence little is known regarding the contribution of retrieval processes, particularly after lengthy retention intervals. To address this issue, we used event-related functional MRI to measure neural activity during the retrieval of emotional and neutral pictures after a retention interval of 1 yr. Retrieval activity for emotional and neutral pictures was separately analyzed for successfully (hits) vs. unsuccessfully (misses) retrieved items and for responses based on recollection vs. familiarity. Recognition performance was better for emotional than for neutral pictures, and this effect was found only for recollection-based responses. Successful retrieval of emotional pictures elicited greater activity than successful retrieval of neutral pictures in the amygdala, entorhinal cortex, and hippocampus. Moreover, in the amygdala and hippocampus, the emotion effect was greater for recollection than for familiarity, whereas in the entorhinal cortex, it was similar for both forms of retrieval. These findings clarify the role of the amygdala and the medial temporal lobe memory regions in recollection and familiarity of emotional memory after lengthy retention intervals.
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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 | 0.001 |
| 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".