The effect of predictive visual stimuli on perceived location of auditory targets: Kinematic evidence
Bibliographic record
Abstract
Previous research showed that visual information is integrated into the movement plan when reaching to a sound target. Movement planning and execution were negatively impacted, even in the presence of a relevant visual stimulus. The present experiment examined if the voluntary allocation of attention alters the degree to which a secondary stimulus is integrated with the primary stimulus during a goal-directed reaching task. Twenty-two adult participants were assigned to either the Told(T) or Not-Told(NT) condition and asked to reach to the perceived location of a 200ms burst of white noise. Two sound targets located in right and left hemispace were paired with a 200ms light that was presented at the same location on 80% of trials. Participants in the T condition were explicitly aware of the relationship while those in the NT were not. Movement trajectories were recorded using Optotrak 3D Investigator at 300Hz. RT analysis revealed no significant differences between T and NT conditions. Greater spatial variability was observed at peak acceleration in the primary axis when participants were NT compared to being T, but was reduced by movement endpoint compared to the latter. Comparing congruent and incongruent trials, participants exhibited increased variability in the x-axis that was reduced by movement endpoint. The findings indicate participants adopted different movement control strategies based on the amount of information available for accurate limb movement. Precisely, additional information about the probability of the auditory target influenced the strategies adopted by the participants when reaching to the perceived location of auditory targets. Acknowledgments: Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Research Manitoba, and the Canadian Foundation for Innovation
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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 | 0.005 |
| 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".