Bradyphrenia and Bradykinesia Both Contribute to Altered Speech in Schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relative contributions of motor and cognitive symptoms on speech output in persons with schizophrenia (SZ). BACKGROUND: Studies of speech production in SZ suggest that atypical prosody (eg, pause) is related to clinical symptoms manifest in flat affect and alogia. Others have suggested that a more general motor slowing, bradykinesia, leads to measurable speech changes. METHOD: Thirteen participants with SZ and age-matched control subjects were included for between-group and by-task comparisons. Two levels of task complexity were analyzed acoustically to determine distinct and overlapping features of speech pause. RESULTS: For the free-speech task, group differences were found on measures of average pause duration, pause variability, percent pause, and cumulative pause time. Conversely, for the rote-speech task, group differences were found only on measures of average pause duration and pause variability. CONCLUSIONS: In persons with SZ, differences in the average and variability of pause duration may be reflected in speech motor slowing, whereas more global measures (eg, percentage pause) may better reflect a paucity of thought and idea generation related to the cognitive-linguistic aspects of free speech. These findings corroborate and extend the paucity of thought hypothesis in SZ to include an influence of motor slowing on speech production.
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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 | 0.000 | 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) | 0.002 | 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".