Neural Basis of Rhythm Perception
Bibliographic record
Abstract
To understand and enjoy music, it is important to be able to hear the beat and move your body to the rhythm. However, impaired rhythm processing has a broader impact on perception and cognition beyond music-specific tasks. We also experience rhythms in our everyday interactions, through the lip and jaw movements of watching someone speak, the syllabic structure of words on the radio, and in the movements of our limbs when we walk. Impairments in the ability to perceive and produce rhythms are related to poor language outcomes, such as dyslexia, and they can provide an index of a primary symptom in movement disorders, such as Parkinson’s disease. The chapter summarizes a growing body of literature examining the neural underpinnings of rhythm perception and production. It highlights the importance of auditory-motor relationships in finding and producing a beat in music by reviewing evidence from a number of methodologies. These approaches illustrate how rhythmic auditory information capitalizes on auditory-motor interactions to influence motor excitability, and how beat perception emerges as a function of nonlinear oscillatory dynamics of the brain. Together these studies highlight the important role of rhythm in human development, evolutionary comparisons, multi-modal perception, mirror neurons, language processing, and music.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".