Individual Alpha Frequency Predicts Perceived Visuotactile Simultaneity
Bibliographic record
Abstract
Temporal encoding is a key feature in multisensory processing that leads to the integration versus segregation of perceived events over time. Whether or not two events presented at different offsets are perceived as simultaneous varies widely across the general population. Such tolerance to temporal delays is known as the temporal binding window (TBW). It has been recently suggested that individual oscillatory alpha frequency (IAF) peak may represent the electrophysiological correlate of TBW, with IAF also showing a wide variability in the general population (8-12 Hz). In our work, we directly tested this hypothesis by measuring each individual's TBW during a visuotactile simultaneity judgment task while concurrently recording their electrophysiological activity. We found that the individual's TBW significantly correlated with their left parietal IAF, such that faster IAF accounted for narrower TBW. Furthermore, we found that higher prestimulus alpha power measured over the same left parietal regions accounted for more veridical responses of non-simultaneity, which may be explained either by accuracy in perceptual simultaneity or, alternatively, in line with recent proposals by a shift in response bias from more conservative (high alpha power) to more liberal (low alpha power). We propose that the length of an alpha cycle constrains the temporal resolution within which perceptual processes take place.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".