The hippocampus is critical for value‐based decisions guided by dissociative inference
Bibliographic record
Abstract
The hippocampus supports flexible decision-making through memory integration: bridging across episodes and inferring associations between stimuli that were never presented together ('associative inference'). A pre-requisite for memory integration is flexible representations of the relationships between stimuli within episodes (AB) but also of the constituent units (A,B). Here we investigated whether the hippocampus is required for parsing experienced episodes into their constituents to infer their re-combined within-episode associations ('dissociative inference'). In three experiments male rats were trained on an appetitive conditioning task using compound auditory stimuli (AB+, BA+, CD-, DC-). At test either the compound or individual stimuli were presented as well as new stimuli. Rats with hippocampal lesions acquired and retained the compound discriminations as well as controls. Single constituent stimuli (A, B, C, D) were presented for the first time at test, so the only value with which they could be associated was the one from the compound to which they belonged. Controls inferred constituent tones' corresponding values while hippocampal rats did not, treating them as merely familiar stimuli with no associated value. This finding held whether compound training occurred before or after hippocampal lesions, suggesting that hippocampus-dependent inferential processes more likely occur at retrieval. The findings extend recent discoveries about the role of the hippocampus in intrinsic value representation, demonstrating hippocampal contributions to allocating value from primary rewards to individual stimuli. Importantly, we discovered that dissociative inferences serve to restructure or reparse patterns of directly acquired associations when animals are faced with environmental changes and need to extract relevant information from a multiplex memory. The hippocampus is critical for this fundamental flexible use 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 | 0.001 |
| 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".