Neural Substrates of Empathic Accuracy in People With Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Empathic deficits in schizophrenia may lead to social dysfunction, but previous studies of schizophrenia have not modeled empathy through paradigms that (1) present participants with naturalistic social stimuli and (2) link brain activity to "accuracy" about inferring other's emotional states. This study addressed this gap by investigating the neural correlates of empathic accuracy (EA) in schizophrenia. METHODS: Fifteen schizophrenia patients and 15 controls were scanned while continuously rating the affective state of another person shown in a series of videos (ie, targets). These ratings were compared with targets' own self-rated affect, and EA was defined as the correlation between participants' ratings and targets' self-ratings. Targets' self-reported emotional expressivity also was measured. We searched for brain regions whose activity tracked parametrically with (1) perceivers' EA and (2) targets' expressivity. RESULTS: Patients showed reduced EA compared with controls. The left precuneus, left middle frontal gyrus, and bilateral thalamus were significantly more correlated with EA in controls compared with patients. High expressivity in targets was associated with better EA in controls but not in patients. High expressivity was associated with increased brain activity in a large set of regions in controls (eg, fusiform gyrus, medial prefrontal cortex) but not in patients. DISCUSSION: These results use a naturalistic performance measure to confirm that schizophrenic patients demonstrate impaired ability to understand others' internal states. They provide novel evidence about a potential mechanism for this impairment: schizophrenic patients failed to capitalize on targets' emotional expressivity and also demonstrate reduced neural sensitivity to targets' affective cues.
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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".