Visual processing of social context during mental state perception in schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To examine schizophrenia patients' visual attention to social contextual information during a novel mental state perception task. METHOD: Groups of healthy participants (n = 26) and schizophrenia patients (n = 24) viewed 7 image pairs depicting target characters presented context-free and context-embedded (i.e., within an emotion-congruent social context). Gaze position was recorded with the EyeLink I Gaze Tracker while participants performed a mental state inference task. Mean eye movement variables were calculated for each image series (context-embedded v. context-free) to examine group differences in social context processing. RESULTS: The schizophrenia patients demonstrated significantly fewer saccadic eye movements when viewing context-free images and significantly longer eye-fixation durations when viewing context-embedded images. Healthy individuals significantly shortened eye-fixation durations when viewing context-embedded images, compared with context-free images, to enable rapid scanning and uptake of social contextual information; however, this pattern of visual attention was not pronounced in schizophrenia patients. In association with limited scanning and reduced visual attention to contextual information, schizophrenia patients' assessment of the mental state of characters embedded in social contexts was less accurate. CONCLUSION: In people with schizophrenia, inefficient integration of social contextual information in real-world situations may negatively affect the ability to infer mental and emotional states from facial expressions.
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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 | 0.000 | 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) | 0.002 | 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 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".