The dominance of haptics over audition in controlling wrist velocity during striking movements
Bibliographic record
Abstract
Skilled interactions with sounding objects, such as drumming, rely on resolving the uncertainty in the acoustical and tactual feedback signals generated by vibrating objects. Uncertainty may arise from mis-estimation of the objects' geometry-independent mechanical properties, such as surface stiffness. How multisensory information feeds back into the fine-tuning of sound-generating actions remains unexplored. Participants (percussionists, non-percussion musicians, or non-musicians) held a stylus and learned to control their wrist velocity while repeatedly striking a virtual sounding object whose surface stiffness was under computer control. Sensory feedback was manipulated by perturbing the surface stiffness specified by audition and haptics in a congruent or incongruent manner. The compensatory changes in striking velocity were measured as the motor effects of the sensory perturbations, and sensory dominance was quantified by the asymmetry of congruency effects across audition and haptics. A pronounced dominance of haptics over audition suggested a superior utility of somatosensation developed through long-term experience with object exploration. Large interindividual differences in the motor effects of haptic perturbation potentially arose from a differential reliance on the type of tactual prediction error for which participants tend to compensate: vibrotactile force versus object deformation. Musical experience did not have much of an effect beyond a slightly greater reliance on object deformation in mallet percussionists. The bias toward haptics in the presence of crossmodal perturbations was greater when participants appeared to rely on object deformation feedback, suggesting a weaker association between haptically sensed object deformation and the acoustical structure of concomitant sound during everyday experience of actions upon objects.
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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 | 0.005 |
| 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) | 0.001 | 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 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".