Bibliographic record
Abstract
Auditory rhythms play a central role in human culture and communication, through both speech and music. The ability to track and predict the organization of events in time helps humans optimize attention, perceive emotion, coordinate actions, and understand social affiliations. The importance of these functions has inspired substantial efforts to model rhythm perception. However, despite a wealth of evidence that pitch influences rhythm perception, with higher speech and music perceived as faster, leading theories and models of rhythm perception have yet to incorporate these effects of pitch. This thesis addresses several empirical questions that have stood in the way of integrating pitch into these models. Specifically, 1) whether the perception of higher pitches as faster generalizes across more than two octaves and above 1000 Hz, 2) whether pitch influences synchronized motor tempo, and 3) whether pitch–timing interactions are bidirectional, such that tempo changes also influence perceived pitch. To answer these questions, we present data from ten experiments including subjective tempo ratings, sensorimotor timing, temporal discrimination, and pitch discrimination tasks. Our results suggest the existence of two separate effects of pitch on perceived timing. First, we present evidence in Chapters 2 and 3 for a unidirectional, negative quadratic effect of absolute pitch on perceived tempo. In this effect, both subjective and sensorimotor tempo rise with pitch between 110 and 440 Hz, peak somewhere between 440 and 1760 Hz, and decrease with pitch above that peak. In Chapters 4 and 5, we present evidence for a bidirectional and approximately linear bias to perceive higher pitches as faster and earlier sounds as higher. We propose that the former effect is most likely innate and a product of the structure of the auditory system, whereas the latter is learned from world structure and originates from cue integration at a later stage of processing.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".