Are You Upset? Distinct Roles for Orbitofrontal and Lateral Prefrontal Cortex in Detecting and Distinguishing Facial Expressions of Emotion
Bibliographic record
Abstract
Navigating our complex social world requires effective processing of subtle emotional signals, such as those conveyed by facial expressions. Failure to do so may underlie some of the disabling social-emotional deficits common in a range of neuropsychiatric and neurological conditions. Prefrontal cortex (PFC) has long been implicated in these processes, but the particular contributions of subregions within PFC remain unclear. We used a sensitive facial emotion rating task in patients with focal lesions to different regions within PFC to identify distinct contributions of 2 prefrontal regions to recognizing emotions from facial expressions. A combination of region-of-interest and voxel-based lesion-symptom mapping established that damage to ventromedial PFC impaired the detection of subtle facial expressions of emotion. Such patients had difficulty distinguishing emotional from neutral expressions. In contrast, patients with left ventrolateral PFC were able to detect the presence of emotional signals but had difficulty discriminating between specific emotions. These effects were regionally specific: Dorsomedial prefrontal damage had no effect on either aspect of emotion recognition. These findings suggest that separable processes relying critically on distinct regions within PFC responsible, on the one hand, for detecting emotional signals from facial expressions and, on the other, for correctly classifying such signals.
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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 | 0.001 |
| 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) | 0.000 | 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".