Attention modulates neural measures associated with beat perception
Bibliographic record
Abstract
A growing body of evidence suggests that steady-state evoked potentials may be a useful measure of beat perception, particularly when obtaining traditional, explicit measures of beat perception is difficult, such as with infants or non-human animals. Although attending to a stimulus is not necessary for most traditional applications of steady-state evoked potentials, it is unknown how attention affects steady-state evoked potentials that arise in response to beat perception. Additionally, most applications of steady-state evoked potentials to measure beat perception have used repeating rhythms or real music. Therefore, it is unclear how the steady-state response relates to the robust beat perception that occurs with non-repeating rhythms. Here, we used electroencephalography to record participants' brain activity as they listened to non-repeating musical rhythms while either attending to the rhythms or while distracted by a concurrent visual task. Non-repeating auditory rhythms elicited steady-state evoked potentials at perceived beat frequencies (perception was validated in a separate sensorimotor synchronization task) that were larger when participants attended to the rhythms compared with when they were distracted by the visual task. Therefore, although steady-state evoked potentials appear to index beat perception to non-repeating musical rhythms, this technique may be limited to when participants are known to be attending to the stimulus.
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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 | 0.004 |
| 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".