The impact of distractor timing on reaching movements to visual and auditory targets
Bibliographic record
Abstract
Knowledge of multisensory-motor integration is important for understanding humans’ interactions with their environments and the design of human-machine interfaces. The present study explored the effects of multisensory distractors presented either early (120ms) or late (240ms) during movement execution. Twelve right-handed participants (7 male, M =21 (SD=2.39) years) were trained to perform 300ms reaching movements. Experimental trials were performed in two separate blocks: 1) visual targets paired with auditory distractors; 2) auditory targets paired with visual distractors. The distractor was either in the same location, or to the left or right of the target. Red lights and audio buzzers (1000kHz) provided visual and auditory stimuli respectively. All stimuli were 100ms and were installed behind a black fabric screen to occlude the locations. Three dimensional spatiotemporal data was collected using an Optotrak 3D Investigator at 400Hz. Performance measures were analyzed using a 2 Target Modality (visual, auditory) by 3 Distractor Condition (early, late, none) repeated measures ANOVA. Trajectories were analyzed separately for target modality using a 2 Distractor Condition by 5 Distractor Location by 4 Movement Proportion repeated measures ANOVA. Main effect for reaction time was consistent with our prediction that movement preparation towards an auditory target (426ms) takes longer than a visual target (324ms). While there were no differences in movement time (MT), participants reached significantly higher peak velocities when aiming towards a visual (1650mm/s) versus auditory (1143mm/s) target. Analyses of the horizontal trajectories of visual targets revealed a significant distractor condition by movement proportion interaction. Participants continued to adjust their movements throughout the trajectory when an auditory distractor occurred late in movements. If the distractor occurred early, they did not adjust their movements after 75% of MT. Participant’s ability to perform online control was impacted by the presence of an early auditory distractor when aiming towards a visual target. Acknowledgments: Natural Sciences and Engineering Research Council, and Manitoba Health Research Council.
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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) | 0.001 | 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) | 0.001 | 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".