Viewing orbitofrontal cortex contributions to decision-making through the lens of object recognition.
Bibliographic record
Abstract
Decision neuroscience research has consistently implicated orbitofrontal and adjacent ventromedial prefrontal cortex in value-based decision-making. These areas are thought to reflect subjective value, a generic indicator of the personal motivational relevance of different options that allows them to be compared on a common scale. There are a number of unanswered questions arising from this model. We review findings from studies in patients with focal damage to the ventral frontal lobe that led us to reconsider how decision options are evaluated, applying perspectives from research on object recognition in the ventral visual stream. While decision-making is often approached from an abstract economic perspective in the lab, most of our everyday decisions, whether about food, goods, or people, are between directly perceived complex objects made up of multiple value-predictive attributes. It is not clear how multiple attributes are integrated to produce a global value estimate. We know the objects themselves are represented in the ventral visual stream at different levels of complexity, ranging from individual features to unique combinations of such features, but what about the values of those objects? Here, we suggest distinctions between configural and elemental evaluation echoing distinctions in visual processing. We discuss evidence that orbitofrontal-ventromedial prefrontal cortex is not required for all value-based decisions, but rather is specifically critical for recognizing value when it is predicted by configural relationships between attributes. We also consider how this perspective connects with emerging views of orbitofrontal cortex as an abstract cognitive map, and the debate on whether subjective value is a neurobiologically meaningful construct. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".