Exploring the interplay between language use and cognitive function in schizophrenia spectrum disorders: Insights from patients, first degree relatives, and healthy controls
Bibliographic record
Abstract
Background: For people with schizophrenia spectrum disorders (SSD), communication characterized by disrupted language use is common. However, the role of cognitive function in everyday language disruptions in SSD remains unclear. Family studies help control for confounding factors such as symptom burden, medication use and environment, offering insight into the interplay between language and cognition in SSD. Study design: We examined linguistic features in naturalistic speech from 176 individuals (51 with SSD, 77 first-degree relatives [50 parents, 27 siblings], and 48 healthy controls). Tasks included conversations, picture descriptions, story narration, reading and recall. We assessed cognitive domains: attention, verbal/visual memory, working memory, executive function, processing speed, motor dexterity, and theory of mind. We then studied associations between cognition and language. Results: Different patterns emerged across groups. In controls, longer speech and fewer pronouns were linked to better cognition. In SSD, greater adposition use and fewer pronouns related to better memory, executive function, and IQ. Among parents, more coordinating conjunctions during narration correlated with better visual memory and motor dexterity. Siblings showed the strongest, broadest associations: better cognition predicted richer language and fewer pronouns, especially tied to global and motor function. Story narration revealed the richest cognitive-linguistic links. Conclusions: In people with SSD and their relatives, specific cognitive deficits are reflected in everyday speech, regardless of content. These findings highlight the role of discourse context in shaping language-cognition relationships and support future research using language markers in psychosis.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".