Not alpha power: prestimulus beta power predicts the magnitude of individual temporal order bias for audiovisual stimuli
Bibliographic record
Abstract
Abstract Individuals exhibit significant variations in audiovisual temporal order perception. Previous studies have investigated the neural mechanisms underlying these individual differences by analyzing ongoing neural oscillations using stimuli specific to each participant. This study explored whether these effects could extend to different paradigms with the same stimuli across subjects in each paradigm. The two human participants groups performed a temporal order judgment (TOJ) task in two experimental paradigms while recording EEG. One is the beep-flash paradigm, while the other is the stream-bounce paradigm. We focused on the correlation between individual temporal order bias (i.e., point of subjective simultaneity (PSS)) and spontaneous neural oscillations. In addition, we also explored whether the frontal cortex could modulate the correlation through a simple mediation model. We found that the beta band power in the auditory cortex could negatively predict the individual’s PSS in the beep-flash paradigm. Similarly, the same effects were observed in the visual cortex during the stream-bounce paradigm. Furthermore, the frontal cortex could influence the power in the sensory cortex and further shape the individual’s PSS. These results suggested that the individual’s PSS was modulated by auditory or visual cortical excitability depending on the experimental stimuli. The frontal cortex could shape the relation between sensory cortical excitability and the individual’s PSS in a top-down manner. In conclusion, our findings indicated that the prefrontal cortex could effectively regulate an individual’s temporal order bias, providing insights into audiovisual temporal order perception mechanisms and potential interventions for modulating temporal perception.
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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 | 0.000 | 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) | 0.003 | 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".