Timing-induced illusory percepts of pitch
Bibliographic record
Abstract
It has long been proposed that the brain integrates pitch and timing cues during auditory perception. If true, the pitch of a sound should influence its perceived timing, and its timing should influence its perceived pitch. Previous research has found that higher-pitched sounds tend to be perceived as faster than lower-pitched sounds, and in the present study we investigated whether sounds that arrive earlier or later than expected are similarly perceived as higher or lower in pitch. In Experiment 1, participants heard isochronous, repeating standard tones followed by a pitch-shifted probe, and indicated if the pitch increased or decreased. We observed a strong biasing effect of the probe’s timing on its perceived pitch, such that later probes were more likely to be perceived as lower than the standard. Correct, bias-conforming responses to mistimed probes were also significantly faster than responses to on-beat probes. In Experiment 2, we used an adaptive difficulty procedure to investigate whether this timing-induced bias strengthens under conditions of low discriminability. We did not find evidence that bias varies with the magnitude of pitch change or with individual differences in pitch sensitivity. In conjunction with past findings of pitch-induced illusory timing changes, our results support the hypothesis that pitch and time are perceptually integrated. We discuss this integration within a Bayesian predictive coding framework, as possibly learned from real-world correlations between pitch and timing that derive from latent properties of sound sources.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".