Beta-band power is an index of multisensory weighting during self-motion perception
Bibliographic record
Abstract
Human self-motion perception largely relies on the integration of the visual, vestibular and proprioceptive systems. Much behavioral research has been conducted in order to understand this integration process; however, little is known about the online processes in humans during self-motion perception. Of the few studies to physically move human participants with full-body motion while recording the brain, most have used EEG due to its relative mobility. Past research provides evidence that multisensory self-motion perception elicits theta, alpha, and beta oscillations. It is important, however, to understand the individual contribution of each sense to fully understand how these oscillatory frequencies contribute to self-motion perception. To our knowledge, there has yet to be a study that directly compares the EEG correlates of visual self-motion with a no-motion physical input, versus physical-self motion with a no-motion visual input. We recorded event-related spectral power within a motion simulator controlled by a MOOG Stewart platform. Participants were given a visual or physical stimulus and made heading direction judgments. Compared to physical-only trials, visual-only trials produced earlier theta ERS and alpha ERD early in the trial, and more robust beta ERS late in the trial. We suggest beta-band power is likely associated with the process of visual-vestibular weighting. Moreover, within the right motor area, we found differences in theta power associated with left versus right headings. Theta ERS in the right motor area appears to be associated with heading processing for both the visual and vestibular systems but is minimally affected by multisensory weighting.
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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 | 0.001 | 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".