Bibliographic record
Abstract
Clinical accounts of the effects of damage to orbitofrontal cortex (OFC) have provided important clues about the functions of this region in humans. Patients with OFC injury can demonstrate relatively isolated difficulties with decision making, and the development of laboratory tasks that captured these difficulties was an important advance. However, much of the work to date has been limited by the use of a single, complex decision-making task and by a narrow focus on risky decisions. A fuller understanding of the neural basis of decision making requires identification of the simpler components that underlie this complex behavior. Here, I review evidence that OFC lesions disrupt reversal learning in humans, as in animals, and show that this deficit in reversal learning is an important mechanism underlying the difficulties of such patients in the Iowa gambling task. Reversal learning, in turn, can be decomposed into simpler processes: a failure to rapidly learn from negative feedback may be the critical difficulty for OFC patients. OFC damage can also affect forms of decision making that do not require trial-by-trial learning. Preference judgment is a simple form of decision making that requires comparing the relative value of options. Humans with OFC lesions are more inconsistent in their choices, even in very simple preference judgment tasks. These results are broadly consistent with the view that OFC is critically involved in representing the relative value of stimuli, but also raise the possibility that this region plays distinct roles in reinforcement learning and value-based judgment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".