Processing of Emotions in Speech in Forensic Patients With Schizophrenia: Impairments in Identification, Selective Attention, and Integration of Speech Channels
Bibliographic record
Abstract
Individuals with schizophrenia show deficits in recognition of emotions which may increase the risk of violence. This study explored how forensic patients with schizophrenia process spoken emotion by: (a) identifying emotions expressed in prosodic and semantic content separately, (b) selectively attending to one speech channel while ignoring the other, and (c) integrating the prosodic and the semantic channels, compared to non-clinical controls. Twenty-one forensic patients with schizophrenia and 21 matched controls listened to sentences conveying four emotions (anger, happiness, sadness, and neutrality) presented in semantic or prosodic channels, in different combinations. They were asked to rate how much they agreed that the sentences conveyed a predefined emotion, focusing on one channel or on the sentence as a whole. Forensic patients with schizophrenia performed with intact identification and integration of spoken emotions, but their ratings indicated reduced discrimination, larger failures of selective attention, and under-ratings of negative emotions, compared to controls. This finding doesn't support previous reports of an inclination to interpret social situations in a negative way among individuals with schizophrenia. Finally, current results may guide rehabilitation approaches matched to the pattern of auditory emotional processing presented by forensic patients with schizophrenia, improving social interactions and quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".