Amusia and protolanguage impairments in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Both language and music are thought to have evolved from a musical protolanguage that communicated social information, including emotion. Individuals with perceptual music disorders (amusia) show deficits in auditory emotion recognition (AER). Although auditory perceptual deficits have been studied in schizophrenia, their relationship with musical/protolinguistic competence has not previously been assessed. METHOD: Musical ability was assessed in 31 schizophrenia/schizo-affective patients and 44 healthy controls using the Montreal Battery for Evaluation of Amusia (MBEA). AER was assessed using a novel battery in which actors provided portrayals of five separate emotions. The Disorganization factor of the Positive and Negative Syndrome Scale (PANSS) was used as a proxy for language/thought disorder and the MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognition. RESULTS: Highly significant deficits were seen between patients and controls across auditory tasks (p < 0.001). Moreover, significant differences were seen in AER between the amusia and intact music-perceiving groups, which remained significant after controlling for group status and education. Correlations with AER were specific to the melody domain, and correlations between protolanguage (melody domain) and language were independent of overall cognition. DISCUSSION: This is the first study to document a specific relationship between amusia, AER and thought disorder, suggesting a shared linguistic/protolinguistic impairment. Once amusia was considered, other cognitive factors were no longer significant predictors of AER, suggesting that musical ability in general and melodic discrimination ability in particular may be crucial targets for treatment development and cognitive remediation in schizophrenia.
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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 | 0.001 | 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".