Processing of Spoken Emotions in Schizophrenia: Forensic and Non-forensic Patients Differ in Emotional Identification and Integration but Not in Selective Attention
Bibliographic record
Abstract
Patients with schizophrenia (PwS) typically demonstrate deficits in visual processing of emotions. Less is known about auditory processing of spoken-emotions, as conveyed by the prosodic (tone) and semantics (words) channels. In a previous study, forensic PwS (who committed violent offenses) identified spoken-emotions and integrated the emotional information from both channels similarly to controls. However, their performance indicated larger failures of selective-attention, and lower discrimination between spoken-emotions, than controls. Given that forensic schizophrenia represents a special subgroup, the current study compared forensic and non-forensic PwS. Forty-five PwS listened to sentences conveying four basic emotions 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. Their performance was compared to that of 21 forensic PwS (previous study). The two groups did not differ in selective-attention. However, better emotional identification and discrimination, as well as better channel integration were found for the forensic PwS. Results have several clinical implications: difficulties in spoken-emotions processing might not necessarily relate to schizophrenia; attentional deficits might not be a risk factor for aggression in schizophrenia; and forensic schizophrenia might have unique characteristics as related to spoken-emotions processing (motivation, stimulation).
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".