The Relationship Between Multisensory Temporal Processing and Schizotypal Traits
Bibliographic record
Abstract
Recent literature has suggested that deficits in sensory processing are associated with schizophrenia (SCZ), and more specifically hallucination severity. The DSM-5's shift towards a dimensional approach to diagnostic criteria has led to SCZ and schizotypal personality disorder (SPD) being classified as schizophrenia spectrum disorders. With SCZ and SPD overlapping in aetiology and symptomatology, such as sensory abnormalities, it is important to investigate whether these deficits commonly reported in SCZ extend to non-clinical expressions of SPD. In this study, we investigated whether levels of SPD traits were related to audiovisual multisensory temporal processing in a non-clinical sample, revealing two novel findings. First, less precise multisensory temporal processing was related to higher overall levels of SPD symptomatology. Second, this relationship was specific to the cognitive-perceptual domain of SPD symptomatology, and more specifically, the Unusual Perceptual Experiences and Odd Beliefs or Magical Thinking symptomatology. The current study provides an initial look at the relationship between multisensory temporal processing and schizotypal traits. Additionally, it builds on the previous literature by suggesting that less precise multisensory temporal processing is not exclusive to SCZ but may also be related to non-clinical expressions of schizotypal traits in the general population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".