Performance in a within-modality temporal order judgment task reveals suboptimal multisensory integration following stimuli presentation at peak limb velocity
Bibliographic record
Abstract
Auditory and visual cues can be integrated in a statistically optimal fashion (e.g., Ernst & Bülthoff, 2004). However, the weighting of auditory and visual information for multisensory integration appears to change as a function of limb velocity (Tremblay & Nguyen, 2010; Loria, de Grosbois & Tremblay, submitted). The current study was designed to assess whether audiovisual information is optimally integrated at peak limb velocity. Participants (N = 13) were required to "fling" their limb through the centre of a virtual target (i.e., right index finger to reach peak velocity as it intersected the target). Piezo-LED devices were affixed on both sides of the virtual target and provided two auditory, visual, or audiovisual cues when the participant reached their peak limb velocity, or while the participant remained stationary. After each trial, participants completed a within-modality temporal order judgment task (TOJ), reporting which side of the virtual target the first sensory cue was presented. When analyzing response accuracy at rest, it was found that participants were more accurate in judging the order of the events (i.e., TOJ) in the auditory and audiovisual condition relative to the visual condition. Also, performance in the audiovisual condition was significantly less accurate when the sensory cues were presented at peak limb velocity compared to at rest. Overall, the results from this experiment in conjunction with those reported previously (e.g., Loria et al., submitted; Tremblay & Nguyen, 2010) suggest that the central nervous system integrates sensory information in a manner that may not always be optimal.Acknowledgments: Natural Sciences and Engineering Research Council, Ontario Research Fund, Canada 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".