Toward computational measures of social cognition in patients with schizophrenia spectrum disorders
Bibliographic record
Abstract
Computational methods are increasingly being explored to detect language markers of schizophrenia spectrum disorders (SSD).Despite the importance of social cognition in diagnosis and functional outcomes in this clinical population, it remains underexamined in computational language analysis.This exploratory study applies machine methods from natural language processing and computational linguistics to transcribed speech from patients with SSD and healthy controls to determine whether they are capable of extracting and analyzing linguistic features of social cognition in text samples.We evaluated linguistic features from three categories related to social cognition: perspective taking, emotion processing, and linguistic complexity.In addition, we explored various implementations of these methods, e.g.grouping them together and contextualizing them, which might point to additional ways of quantifying social cognition that could be integrated into future computational text analyses.Patients exhibited increased first-person pronoun use, reduced relative third-person pronoun use, lower modal verb frequency, and decreased linguistic complexity.Emotional content distributions also differed between groups and varied depending on pronoun context, with notable contrasts in the emotional content of speech containing third-person pronouns versus pronoun-absent speech.Our results demonstrate that computational methods can effectively identify relevant linguistic features of social cognition in text.This work establishes a basis to continue to refine computational methods to assess social cognition in SSD that will ultimately advance objective, rapid, and scalable approaches for personalized diagnosis and treatment.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".