Reduction in Tonal Discriminations Predicts Receptive Emotion Processing Deficits in Schizophrenia and Schizoaffective Disorder
Bibliographic record
Abstract
INTRODUCTION: Schizophrenia patients show decreased ability to identify emotion based upon tone of voice (voice emotion recognition), along with deficits in basic auditory processing. Interrelationship among these measures is poorly understood. METHODS: Forty-one patients with schizophrenia/schizoaffective disorder and 41 controls were asked to identify the emotional valence (happy, sad, angry, fear, or neutral) of 38 synthesized frequency-modulated (FM) tones designed to mimic key acoustic features of human vocal expressions. The mean (F0M) and variability (F0SD) of fundamental frequency (pitch) and absence or presence of high frequency energy (HF500) of the tones were independently manipulated to assess contributions on emotion identification. Forty patients and 39 controls also completed tone-matching and voice emotion recognition tasks. RESULTS: Both groups showed a nonrandom response pattern (P < .0001). Stimuli with highest and lowest F0M/F0SD were preferentially identified as happy and sad, respectively. Stimuli with low F0M and midrange F0SD values were identified as angry. Addition of HF500 increased rates of angry and decreased rates of sad identifications. Patients showed less differentiation of response across frequency changes, leading to a highly significant between-group difference in response pattern to maximally identifiable stimuli (d = 1.4). The differential identification pattern for FM tones correlated with deficits in basic tone-matching ability (P = .01), voice emotion recognition (P < .001), and negative symptoms (P < .001). CONCLUSIONS: Specific FM tones conveyed reliable emotional percepts in both patients and controls and correlated highly with deficits in ability to recognize information based upon tone of voice, suggesting significant bottom-up contributions to social cognition and negative symptom impairments in schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".