An Intact Social Cognitive Process in Schizophrenia: Situational Context Effects on Perception of Facial Affect
Bibliographic record
Abstract
BACKGROUND: Impaired facial affect recognition is the most consistent social cognitive finding in schizophrenia. Although social situations provide powerful constraints on our perception, little is known about how situational context modulates facial affect recognition in schizophrenia. METHODS: Study 1 was a single-site study with 34 schizophrenia patients and 22 healthy controls. Study 2 was a 2-site study with 68 schizophrenia patients and 28 controls. Both studies administered a Situational Context Facial Affect Recognition Task with 2 conditions: a situational context condition and a no-context condition. For the situational context condition, a briefly shown face was preceded by a sentence describing either a fear- or surprise-inducing event. In the no-context condition, a face was presented without a sentence. For both conditions, subjects rated how fearful or surprised the face appeared on a 9-point Likert scale. RESULTS: For the situational context condition of study 1, both patients and controls rated faces as more afraid when they were paired with fear-inducing sentences and as more surprised when they were paired with surprise-inducing sentences. The degree of modulation was comparable across groups. For the no-context condition, patients rated faces comparably to controls. The findings of study 2 replicated those from study 1. CONCLUSIONS: Despite previous abnormalities in other types of context paradigms, this study found intact situational context processing in schizophrenia, suggesting that patients benefit from situational context when interpreting ambiguous facial expression. This area of relative social cognitive strength in schizophrenia has implications for social cognitive training programs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".