Neural Patterns Reflect Shared Emotional History
Bibliographic record
Abstract
Emotions shape episodic memories, with emotional context-the affective quality or "hue" of an experience-persisting as part of the event in memory, scaffolding connections between events, and guiding our impressions of the environment. We propose that events encoded in a similar emotional context also exhibit similar patterns of brain activation during retrieval, particularly when such events are negative. To explore this idea, we scanned 33 human participants of all genders using functional magnetic resonance imaging as they completed a two-phase episodic memory task. During encoding, participants viewed trial-unique image pairs: a neutral object alongside a complex picture evoking either a negative or neutral emotional context. Across conditions, images were closely matched on low-level perceptual features. During retrieval, participants were shown the neutral objects again and rated their pleasantness, implicitly recalling their emotional context. To determine whether there is a neural signature that reflects salient emotional contexts, we employed trial-level representational similarity analysis, focusing on three brain areas previously linked to emotional memory, appraisal, and/or affective schemas: ventral visual stream (VVS), hippocampus, and ventromedial prefrontal cortex (vmPFC). Our results demonstrate strong converging evidence of emotional context coding in the VVS, reflecting a shared signature of negative emotional context across retrieval and reinstatement of encoding activation patterns, particularly for negative events. Meanwhile, the hippocampus and vmPFC played a more nuanced role. These findings reveal that content with a shared emotional context evokes brain activity patterns reflecting the essence of its emotional history, highlighting the brain's flexible capacity to integrate affective content into mnemonic representations.
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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".