The role of the orbitofrontal cortex in creating cognitive maps
Bibliographic record
Abstract
Abstract We use internal models of the external world to guide behavior, but little is known about how these cognitive maps are created . The orbitofrontal cortex (OFC) is typically thought to access these maps to support model-based decision-making, but it has recently been proposed that its critical contribution may be instead to integrate information into existing and new models. We tested between these alternatives using an outcome-specific devaluation task and a high-potency chemogenetic approach. We found that selectively inactivating OFC principal neurons when rats learned distinct cue-outcome associations, but prior to outcome devaluation, disrupted subsequent model-based inference, confirming that the OFC is critical for creating new cognitive maps. However, OFC inactivation surprisingly led to generalized devaluation. Using a novel reinforcement learning framework, we demonstrate that this effect is best explained not by a switch to a model-free system, as would be traditionally assumed, but rather by a circumscribed deficit in defining credit assignment precision during model construction. We conclude that the critical contribution of the OFC to learning is regulating the specificity of associations that comprise cognitive maps. One Sentence Summary OFC inactivation impairs learning of new specific cue-outcome associations without disrupting model-based learning in general.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".