Modulation of multisensory processing during rapid reaching movements
Bibliographic record
Abstract
Humans appear to make extensive use visual information during rapid upper limb reaches (e.g., Elliott et al., 2010). In an attempt to assess the visual regulation of goal-directed action from a multisensory processing perspective, we recently employed an audio-visual illusion (e.g., Shams et al., 2000) presented at different times during rapid reaching movements. These previous results demonstrated that susceptibility to the fusion illusion (i.e., perceive one flash when two flashes are presented with one beep) is reduced at high limb velocities (Tremblay & Nguyen, 2010). That is participants were more likely accurately report two flashes in the fusion illusion condition only when their limb traveled at more than 1.5 m/s. However, one missing component of that study was a resting control condition, which we added in the present study. As in the previous study, we always presented either 1 or 2 beeps with 1 or 2 flashes. Our experimental design included one control resting condition, performed at the beginning or the end of the protocol (i.e., counterbalanced across participants). As well, in the main experimental phase, one of the 4 audio-visual conditions was presented at one of 5 times relative to the onset of a rapid reaching movement (0, 50, 100, 150, and 200 ms after movement start). All experimental phase conditions were presented pseudo-randomly, 12 times each. This current study first replicated the influence of limb velocity on the fusion illusion (i.e., more likely to accurately perceive both flashes when the limb travels the fastest). Also, we observed that participants were as likely to experience the fusion illusion in the resting control condition than early in the movement (i.e., at low limb velocity). Therefore, this study suggests that visual information processing is enhanced at high limb velocities, and that is, at least compared to auditory processing.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".