Schizotypal personality traits and multisensory integration: An investigation using the McGurk effect
Bibliographic record
Abstract
Multisensory integration, the process by which sensory information from different sensory modalities are bound together, is hypothesized to contribute to perceptual symptomatology in schizophrenia, in whom multisensory integration differences have been consistently found. Evidence is emerging that these differences extend across the schizophrenia spectrum, including individuals in the general population with higher levels of schizotypal traits. In the current study, we used the McGurk task as a measure of multisensory integration. We measured schizotypal traits using the Schizotypal Personality Questionnaire (SPQ), hypothesizing that higher levels of schizotypal traits, specifically Unusual Perceptual Experiences and Odd Speech subscales, would be associated with decreased multisensory integration of speech. Surprisingly, Unusual Perceptual Experiences were not associated with multisensory integration. However, Odd Speech was associated with multisensory integration, and this association extended more broadly across the Disorganized factor of the SPQ, including Odd or Eccentric Behaviour. Individuals with higher levels of Odd or Eccentric Behaviour scores also demonstrated poorer lip-reading abilities, which partially explained performance in the McGurk task. This suggests that aberrant perceptual processes affecting individuals across the schizophrenia spectrum may relate to disorganized symptomatology.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".