Eye-tracking reveals a slowdown of social context processing during intention attribution in patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is associated with poor theory of mind (ToM), particularly in the attribution of intentions to others. It is also associated with abnormal gaze behaviours and contextual processing. This study investigated to what extent impaired ToM in patients with schizophrenia is related to abnormal processing of social context. METHODS: We evaluated ToM using a nonverbal intention attribution task based on comic strips depicting social/nonsocial and contextual/noncontextual events while eye movements were recorded. Eye-tracking was used to assess processing time dedicated to visual cues contained in regions of interest identified in a pilot study. We measured cognitive contextual control on a separate task. RESULTS: We tested 29 patients with schizophrenia and 29 controls. Compared with controls, patients were slower in intention attribution but not in physical reasoning. They looked longer than controls at contextual cues displayed in the first 2 context pictures of the comic strips, and this difference was greater for intention attribution than for physical reasoning. We found no group difference in time spent looking at noncontextual cues. Patients' impairment in contextual control did not explain their increased reaction time and gaze duration on contextual cues during intention attribution. LIMITATIONS: Difficulty may not have been equivalent between intention attribution and physical reasoning conditions. CONCLUSION: Overall, schizophrenia was characterized by a delay in intention attribution related to a slowdown of social context processing that was not explained by worse executive contextual control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".