Performing rapid actions affects audiovisual processing
Bibliographic record
Abstract
Performing rapid upper-limb movements influence audiovisual integration (i.e., Loria et al., 2016a). The re-weighting of target-congruent sensory information, such as the real-time up-regulation of visual processing during visuomotor tasks, may explain the influence of action on audiovisual processing (e.g., Tremblay & Nguyen, 2010). Yet, presenting temporal order judgment (TOJ) stimuli during a rapid movement towards visual and auditory targets has been shown to facilitate visual and auditory processing, while modulating audiovisual processing (i.e., Loria et al., 2016b). The purpose of the current study was to further probe the influence of action on audiovisual integration. Participants performed a flinging movement, requiring the index finger to align with an audiovisual target at peak velocity (PV). At PV, participants were presented with asynchronous auditory, visual, and audiovisual cues from both sides of the target. After each trial, participants judged which sensory cue was presented first in a TOJ task. TOJs were also completed at rest. Theoretically, TOJ accuracy should be optimal in the audiovisual condition (Ernst & Bulthoff, 2004). And if engaging in a rapid goal-directed action specifically influences audiovisual processing, only audiovisual TOJ accuracy should decline between the rest and movement conditions. The results showed greater accuracy for audiovisual than auditory TOJs only at rest while TOJ accuracy did not differ across the sensory conditions after flinging movements. Further, contrasting TOJ accuracy between the resting and movement conditions only revealed a decrease in performance for audiovisual TOJs. Overall, unisensory processing appeared unaffected during rapid limb movements while audiovisual processing was specifically hindered. Acknowledgments: Natural Sciences and Engineering Research Council of Canada (NSERC), Canada Foundation for Innovation (CFI), Ontario Research Fund (ORF), and University of Toronto Graduate Student Bursary
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".