Multisensory integration in percussion performance
Bibliographic record
Abstract
We investigated how auditory and haptic information concerning objects hardness is integrated for the purpose of controlling the velocity with which we strike an object. Our experimental manipulations and data analyses considered a variety of factors that should be integrated in a theory of multisensory perception: expertise of the perceiver; context (unimodal vs. multimodal); inter-modality congruence; inter-participants agreement in sensory weighting; performance. On each trial, participants struck a virtual object with a constant target velocity and received feedback on correctness. When the performance criterion was reached, feedback was eliminated, the auditory and/or haptic hardness of the struck object were changed, and the effects on subsequent striking velocity and performance were measured. In unimodal trials only the haptic or auditory display was presented. In multisensory trials, the audio-haptic changes could be congruent (e.g., both increased in hardness) or incongruent. We recruited participants with different levels of expertise with the task: percussionists, nonpercussionist musicians and nonmusicians. For both modalities, striking velocity increased with decreasing hardness, and vice versa. With the vast majority of participants, changes in haptic hardness were perceptually more relevant because they in-fluenced striking velocity to a greater degree than did changes in auditory hardness. The perceptual
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".