Prestimulus neural variability affects behavioral performances mediated by poststimulus-evoked responses at the intraindividual and interindividual levels
Bibliographic record
Abstract
Abstract There are significant intra-individual and inter-individual variabilities in audiovisual temporal perception. Previous studies have shown that prestimulus neural variability could reflect behavioral variabilities. We aimed to investigate whether prestimulus neural variability can predict behavioral variability in audiovisual temporal perception. Furthermore, We also explored whether prestimulus neural variability directly influences behavioral responses or indirectly impacts perceptual decisions through post-stimulus-evoked responses. We analyzed the electroencephalography (EEG) data from a paradigm where the twenty-eight human subjects performed a simultaneity judgment (SJ) task in the beep-flash stimulus. The prestimulus weighted permutation entropy (WPE) was the indicator of neural variability in this study. We found that prestimulus frontal WPE could predict the individual’s TBW in auditory- and visual-leading conditions. In addition, increased prestimulus parietal WPE was associated with more asynchronous responses. Prestimulus frontal WPE may be associated with top-down cognitive control, while parietal WPE may be related to bottom-up cortical excitability. Furthermore, poststimulus evoked responses could mediate the relation between prestimulus WPE and the individual’s TBW or perceptual responses. These results suggested that prestimulus WPE was a marker in reflecting intra-individual and inter-individual variabilities in audiovisual temporal perception. Significantly, prestimulus WPE might influence perceptual responses by affecting poststimulus sensory representations.
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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) | 0.000 | 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".