Referential communication in people with recent-onset schizophrenia-spectrum disorders
Bibliographic record
Abstract
People with schizophrenia present with language production impairments, yet very few studies examine language production in the context of collaborative, verbal interaction tasks performed with a real interaction partner. The current study relied on a referential communication paradigm in which participants with schizophrenia (SZ) and healthy controls (HC) presented a series of movie characters to their interaction partner, whose role was to identify and place the characters in the same order. The HC spontaneously provided more information when presenting characters that their interaction partner was unlikely to know than when presenting very well-known characters, and the magnitude of this adjustment was positively correlated with their performance on a theory of mind task. In contrast, people with SZ showed a significantly reduced (absent) adjustment to the likely-known vs. likely-unknown nature of the characters, and no correlation emerged with ToM. Further examination of the verbal productions revealed that HC often combined movie-related information (ex: character's name or movie title) and descriptive information whereas people with SZ more often used description only to present the characters. Overall, this study adds to our knowledge about referential choices in SZ in the context of collaborative verbal interactions with a real interaction partner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".