Duration, Sequence and Beat Perception across Modalities
Bibliographic record
Abstract
Certain rhythmic sequences spontaneously induce the perception of a beat: a psychologically salient pulse that marks equally spaced points in time. However, individuals vary considerably in beat perception ability. This variability may arise from variability at multiple levels of a perceptual timing hierarchy of durations, sequences and beat. Specifically, variability could reflect individual differences in basic duration perception, even for single intervals, as single intervals are the building blocks for sequences. Alternatively, it could relate to differences in the timing or recall of temporal sequences despite good single-interval timing, as a sequence needs to be encoded accurately for an underlying beat to be perceived. Finally, it is possible that beat perception variability is specifically related to listeners' ability to extract the beat from temporal sequences. To determine whether evidence supports this three-level perceptual timing hierarchy, and how beat perception ability relates to single-duration and sequence perception ability, we tested performance on single-interval timing, nonbeat sequence timing, and beat sequence timing tasks using a three-alternative forced-choice paradigm. Moreover, we presented both visual and auditory stimuli to determine whether perceptual abilities across sensory modalities are related or independent. We applied a k-means clustering algorithm to partition participants based on task performance. The results support the three-level perceptual hierarchy in the auditory modality, and that beat perception deficits can arise from deficits at any level of the hierarchy. However, the hierarchy did not hold for the visual modality, suggesting rhythm and beat perception vary across modalities.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".