Representation of Task Structure in Human Hippocampus and Orbitofrontal Cortex
Bibliographic record
Abstract
ABSTRACT The hippocampus is thought to support episodic memory, or memory for specific events, but recent work also suggests that it may be involved in extracting structure from the world to guide future decisions and predictions. Recent evidence in rodents suggests that the hippocampus supports decision-making in cooperation with orbitofrontal cortex (OFC), possibly based on representation of task structure. Here, we used functional magnetic resonance imaging (fMRI) to test how the human hippocampus and OFC represents decision-relevant information extracted from previous experiences. Participants performed a task in which they learned values of different foods in grocery store contexts. The task was structured such that we could examine the degree to which neural representations could reflect generalized information about different task structures. Specifically, we manipulated whether a food’s desirability varied with store context or not. Some foods were desirable in some store contexts and not in others; some foods were always desirable or undesirable. Participants needed to extract these two task sub-structures (i.e., context-determined vs. context-invariant) from the task structure. We examined hippocampal and OFC activity patterns during a decision-making task after participants were trained with the task structure. Our results showed that both hippocampus and OFC carried task structure information that was relevant to the decision outcomes. Hippocampal and lateral OFC representations differentiated between context-determined (deterministic) and context-invariant (probabilistic) task structures. The degree of this differentiation, an index of task structure representation, was highly correlated between hippocampus and lateral OFC. These results add to a mounting evidence suggesting that the hippocampus and OFC support decision-making by representing task relevant information to the decision outcomes after the task structure is learned.
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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".