Hippocampus Guides Adaptive Learning during Dynamic Social Interactions
Bibliographic record
Abstract
How do we evaluate whether someone will make a good friend or collaborative peer? A hallmark of human cognition is the ability to make adaptive decisions based on information garnered from limited prior experiences. Using an interactive social task measuring adaptive choice (deciding who to reengage or avoid) in male and female participants, we find the hippocampus supports value-based social choices following single-shot learning. These adaptive choices elicited a suppression signal in the hippocampus, revealing sensitivity for the subjective perception of a person and how well they treat you during choice. The extent to which the hippocampus was suppressed was associated with flexibly interacting with prior generous individuals and avoiding selfish individuals. Further, we found that hippocampal signals during decision-making were related to subsequent memory for a person and the offer they made before. Consistent with the hippocampus leveraging previously executed choices to solidify a reliable neural signature for future adaptive behavior, we also observed a later hippocampal enhancement. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning. SIGNIFICANCE STATEMENTAdaptively navigating social interactions requires an integration of prior experiences with information gleaned from the current environment. While most research has focused on striatal-based feedback learning, open questions remain regarding the role of hippocampal-based episodic memory systems. Here, we show that during social decisions based on prior experience, hippocampal suppression signals were sensitive to adaptive choice, while hippocampal enhancements was related to subsequent memory for the original social interaction. These findings highlight the hippocampus playing a multifaceted role in socially adaptive learning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".