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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".