Aberrant patterns of visual facial information usage in schizophrenia.
Bibliographic record
Abstract
Deficits in facial emotion perception have been linked to poorer functional outcome in schizophrenia. However, the relationship between abnormal emotion perception and functional outcome remains poorly understood. To better understand the nature of facial emotion perception deficits in schizophrenia, we used the Bubbles Facial Emotion Perception Task to identify differences in usage of visual facial information in schizophrenia patients (n = 20) and controls (n = 20), when differentiating between angry and neutral facial expressions. As hypothesized, schizophrenia patients required more facial information than controls to accurately differentiate between angry and neutral facial expressions, and they relied on different facial features and spatial frequencies to differentiate these facial expressions. Specifically, schizophrenia patients underutilized the eye regions, overutilized the nose and mouth regions, and virtually ignored information presented at the lowest levels of spatial frequency. In addition, a post hoc one-tailed t test revealed a positive relationship of moderate strength between the degree of divergence from "normal" visual facial information usage in the eye region and lower overall social functioning. These findings provide direct support for aberrant patterns of visual facial information usage in schizophrenia in differentiating between socially salient emotional states.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".