An Active Inference Model of Meter Perception and the Urge to Move to Music
Bibliographic record
Abstract
Why do some rhythms make us want to move and not others? A predictive processing account suggests that prediction errors drive this phenomenon, but this hypothesis remains underspecified. Here, we operationalized this account as a Bayesian model that infers whether a rhythmic sequence is caused by a metered or unmetered template, coupled with an active inference rule in which movement occurs if the sensory feedback from movement would reduce the prediction errors generated by this inference process. Surprisal, as an index of prediction error, was calculated for each rhythm with and without a metronome (a proxy for the feedback from moving along), with delta surprisal as the difference. Surprisal increased linearly as a function of rhythmic complexity, while delta surprisal showed a similar pattern with urge to move ratings shown in previous studies, and this relation was confirmed in an online study. These results suggest that the urge to move to music is driven by the potential to reduce meter-based prediction errors via the expected feedback from moving along to the beat. This work provides a crucial update to the predictive processing account and highlights a key role of active inference and prediction-based learning in our musical experiences.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".