A Neural “Tuning Curve” for Multisensory Experience and Cognitive-Perceptual Schizotypy
Bibliographic record
Abstract
Our coherent perception of external events is enabled by the integration of inputs from different senses occurring within a range of temporal offsets known as the temporal binding window (TBW), which varies from person to person. A relatively wide TBW may increase the likelihood that stimuli originating from different environmental events are erroneously integrated and abnormally large TBW has been found in psychiatric disorders characterized by unusual perceptual experiences. Despite strong evidence of inter-individual differences in TBW, both within clinical and nonclinical populations, the neurobiological underpinnings of this variability remain unclear. We adopted an integrated strategy linking TBW to temporal dynamics in functional magnetic resonance imaging (fMRI)-resting-state activity and cortical excitation/inhibition (E/I) balance. E/I balance was indexed by glutamate/Gamma-AminoButyric Acid (GABA) concentrations and common variation in glutamate and GABA genes in a healthy sample. Stronger resting-state long-range temporal correlations, indicated by larger power law exponent (PLE), in the auditory cortex, robustly predicted narrower audio-tactile TBW, which was in turn associated with lower cognitive-perceptual schizotypy. Furthermore, PLE was highest and TBW narrowest for individuals with intermediate levels of E/I balance, with shifts towards either extreme resulting in reduced multisensory temporal precision and increased schizotypy, effectively forming a neural "tuning curve" for multisensory experience and schizophrenia risk. Our findings shed light on the neurobiological underpinnings of multisensory integration and its potentially clinically relevant inter-individual variability.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".