Rapid Audiovisual Integration Guides Predictive Actions
Bibliographic record
Abstract
Natural movements, such as catching a ball or capturing prey, typically involve multiple senses. Yet, laboratory studies on human movements commonly focus solely on vision and ignore sound. Here, we ask how visual and auditory signals are integrated to guide interceptive movements. Human observers tracked the brief launch of a simulated baseball, randomly paired with batting sounds of varying intensities, and made a quick pointing movement at the ball. Movement end points revealed systematic overestimation of target speed when the ball launch was paired with a loud versus a quiet sound, although sound was never informative. This effect was modulated by the availability of visual information; sounds biased interception when the visual presentation duration of the ball was short. Amplitude of the first catch-up saccade, occurring ∼125 ms after target launch, revealed early integration of audiovisual information for trajectory estimation. This sound-induced bias was reversed during later predictive saccades when more visual information was available. Our findings suggest that auditory and visual signals are integrated to guide interception and that this integration process must occur early at a neural site that receives auditory and visual signals within an ultrashort time span.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".