Can you hear that peak? The effect of auditory feedback on reducing the spatial variability of peak limb velocity
Bibliographic record
Abstract
The presence of multiple sensory cues is known to yield more reliable percepts than when a single modality is available (e.g., Ernst & Bulthoff, 2004). In contrast, the relative contribution of visual and auditory cues can change as a function of limb velocity (e.g., Tremblay & Nguyen, 2010). This study sought to assess the extent to which auditory and visual cues are integrated during the high velocity stages of a reaching movement. If sensory integration takes place during a movement, the variability of the limb position should be significantly smaller when both auditory and visual cues are provided together, compared to when only one or no cues are provided. Participants (n = 13) were asked to “fling” their limb through the centre of a virtual target, located 30 cm from the start position. This task required reaching peak limb velocity as the index finger passed through the centre of the target, which is analogous to a throwing task. A piezo-electric buzzer and an LED were affixed to the fingertip of the moving limb and provided augmented feedback when peak velocity was reached. Results first indicated that peak limb velocity, and the time taken to reach it, did not reliably differ across experimental conditions. More importantly, the variability in the spatial location of the finger at peak velocity was only significantly reduced when auditory feedback was provided (i.e., as compared to the no feedback condition). Overall, the results challenge the idea of multisensory integration because endpoint precision was significantly reduced only when an auditory cue alone indicated to the participant when they reached peak limb velocity, while this was not the case when both auditory and visual cues were provided. In addition, the results indicate that providing aurally based feedback may be optimal to reduce the variability of a throwing task. Acknowledgments: This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, and the Ontario Research Fund
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".