Reduced MMN-indexed auditory change detection in a non-clinical high schizotypy sample
Bibliographic record
Abstract
Schizotypal traits include abnormalities in cognition, behavior, and interpersonal relationships that are similar, yet less severe than psychotic symptomology. It is estimated that approximately 5% of the general population displays psychotic symptoms and experiences that can be considered schizotypal in nature, but there is little research examining the neurological correlates of these traits. The mismatch negativity (MMN) event-related potential is an objective measure of auditory change detection derived from electroencephalography. The current study contributes to the limited body of evidence examining the neurobiological underpinnings of schizotypy in a non-clinical sample using the MMN. Participants were recruited from the general population and divided into high and low-schizotypy groups for comparison. Individuals with high schizotypal traits displayed reduced MMN amplitudes in response to frequency and location deviants, and longer MMN latencies in response to location deviants. Specific sub-traits of schizotypy were uniquely related to frequency and location amplitudes, suggesting the previously reported inconsistencies in the literature may be due to diverse samples and differing deviant tone types. Finally, impulsivity and sensation-seeking likely contributed to the slower processing seen in location deviance detection. Ultimately, the current results provide evidence that the neurobiological abnormalities seen in clinical populations of schizotypal personality disorder and psychosis also extend to non-clinical populations.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".