GPT-3 reveals selective insensitivity to global <i>vs.</i> local linguistic context in speech produced by treatment-naïve patients with positive thought disorder
Bibliographic record
Abstract
Abstract Background Early psychopathologists proposed that certain features of positive thought disorder, the disorganized language output produced by some people with schizophrenia, suggest an insensitivity to global, relative to local, discourse context. This idea has received support from carefully controlled psycholinguistic studies in language comprehension. In language production, researchers have so far remained reliant on subjective qualitative rating scales to assess and understand speech disorganization. Now, however, recent advances in large language models mean that it is possible to quantify sensitivity to global and local context objectively by probing lexical probability (the predictability of a word given its preceding context) during natural language production. Methods For each word in speech produced by 60 first-episode psychosis patients and 35 healthy, demographically-matched controls, we extracted lexical probabilities from GPT-3 based on contexts that ranged from very local— a single preceding word: P(Wn | Wn-1)—to global— up to 50 preceding words: P(Wn|Wn-50, Wn-49, …, Wn-1). Results We show, for the first time, that disorganized speech is characterized by disproportionate insensitivity to global, versus local, linguistic context. Critically, this global-versus-local insensitivity selectively predicted clinical ratings of positive thought disorder, above and beyond overall symptom severity. There was no evidence of a relationship with negative thought disorder (impoverishment). Conclusions We provide an automated, interpretable measure that can potentially be used to quantify speech disorganization in schizophrenia. Our findings directly link the clinical phenomenology of thought disorder to neurocognitive constructs that are grounded in psycholinguistic theory and neurobiology.
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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) | 0.000 | 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".