When Ears Drive Hands: The Influence of Contact Sound on Reaching to Grasp
Bibliographic record
Abstract
BACKGROUND: Most research on the roles of auditory information and its interaction with vision has focused on perceptual performance. Little is known on the effects of sound cues on visually-guided hand movements. METHODOLOGY/PRINCIPAL FINDINGS: We recorded the sound produced by the fingers upon contact as participants grasped stimulus objects which were covered with different materials. Then, in a further session the pre-recorded contact sounds were delivered to participants via headphones before or following the initiation of reach-to-grasp movements towards the stimulus objects. Reach-to-grasp movement kinematics were measured under the following conditions: (i) congruent, in which the presented contact sound and the contact sound elicited by the to-be-grasped stimulus corresponded; (ii) incongruent, in which the presented contact sound was different to that generated by the stimulus upon contact; (iii) control, in which a synthetic sound, not associated with a real event, was presented. Facilitation effects were found for congruent trials; interference effects were found for incongruent trials. In a second experiment, the upper and the lower parts of the stimulus were covered with different materials. The presented sound was always congruent with the material covering either the upper or the lower half of the stimulus. Participants consistently placed their fingers on the half of the stimulus that corresponded to the presented contact sound. CONCLUSIONS/SIGNIFICANCE: Altogether these findings offer a substantial contribution to the current debate about the type of object representations elicited by auditory stimuli and on the multisensory nature of the sensorimotor transformations underlying action.
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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 | 0.000 | 0.008 |
| 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 | 0.001 | 0.000 |
| 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".