β- And γ-band EEG power predicts illusory auditory continuity perception
Bibliographic record
Abstract
Because acoustic landscapes are complex and rapidly changing, auditory systems have evolved mechanisms that permit rapid detection of novel sounds, sound source segregation, and perceptual restoration of sounds obscured by noise. Perceptual restoration is particularly important in noisy environments because it allows organisms to track sounds over time even when they are masked. The continuity illusion is a striking example of perceptual restoration with sounds perceived as intact even when parts of them have been replaced by gaps and rendered inaudible by being masked by an extraneous sound. The mechanisms of auditory filling-in are complex and are currently not well-understood. The present study used the high temporal resolution of EEG to examine brain activity related to continuity illusion perception. Masking noise loudness was adjusted individually for each subject so that physically identical sounds on some trials elicited a continuity illusion (failure to detect a gap in a sound) and on other trials resulted in correct gap detection. This design ensured that any measurable differences in brain activity would be due to perceptual differences rather than physical differences among stimuli. We found that baseline activity recorded immediately before presentation of the stimulus significantly predicted the occurrence of the continuity illusion in 10 out of 14 participants based on power differences in γ-band EEG (34-80 Hz). Across all participants, power in the β and γ (12- to 80-Hz range) was informative about the subsequent perceptual decision. These data suggest that a subject's baseline brain state influences the strength of continuity illusions.
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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".