The cognitive, affective, and motoric correlates of rhythmic complexity
Bibliographic record
Abstract
Although a great deal of research has delved into the perception of rhythm and metre, relatively few studies have focused on how listeners perceive and aesthetically evaluate complex musical rhythms. Here, we ask what it means to perceive something as rhythmically complex, and whether there are affective, cognitive, and motoric correlates of rhythmic complexity in the context of twentieth-century music from Europe and North America. This paper examines how listeners respond to complex polyrhythms in terms of how they convey ‘mood’, ‘energy’, and ‘movement’, as well as three descriptors borrowed from Bartel’s (Citation1992) Cognitive-Affective Response Test (‘exciting’, ‘structured’, and ‘complex’). Listeners’ ratings are compared with features derived from a corpus of short polyrhythmic examples analysed using a number of possible metrics for structural ‘complexity’. The findings point to significant effects of selected rhythmic variables, such as composite event density (notes per seconds in human performance), nested ratio (proportion of coinciding events across parts), as well as the ratio of event density and the differential in note-to-note variability between component rhythmic layers. Composite event density and nested ratio were positively correlated with most rated qualities, while event density ratio and note-to-note variability had variegated effects. Effects of several pitch-related features such as pitch height and sonority dissonance, as well as more basic aspects (duration and number of staves) were also observed. This paper argues that elements of rhythmic complexity play a significant role on perceived affective, cognitive, and motoric qualities of music, and proposes methods and measurements for further investigation.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".