Maternal Gait Contributes To Development Of Beat Perception And Urge To Move To Music In A Predictive Processing Network Model
Bibliographic record
Abstract
Humans uniquely perceive periodic structure in complex rhythms and spontaneously move to music—abilities rare among animals. Though training and experience contribute to our sense of rhythm, basic beat perception and the urge to move to rhythm are present even in young infants. But might prenatal experience play an essential role in shaping these faculties? We propose that maternal gait during pregnancy provides critical scaffolding for rhythm development through correlated auditory-vestibular inputs that train predictive neural circuits. We implemented a recurrent predictive coding network with parallel vestibular and auditory sensory pathways, trained via Hebbian learning to minimize prediction error. Training paired discrete auditory pulses with continuous triangular vestibular waveforms mimicking maternal locomotion. These networks learned to anticipate beats and spontaneously generated vestibular predictions from auditory-only input, which, under the principles of active inference, are expected to evoke bodily movement. Critically, continuous vestibular input was necessary for successful training. This input bridges temporal gaps between auditory events, solving the credit assignment problem that makes rhythm learning computationally difficult. The resulting rhythm-induced vestibular predictions offer a possible explanation for why humans spontaneously move to music. This work illustrates how simple sensory correlations during development can give rise to complex musical behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".