Does sensory context influence audiovisual perception during goal-directed actions?
Bibliographic record
Abstract
How sensory information is processed during goal directed reaches is affected by the modality of the target (Sober and Sabes, 2005). For example, when planning reaches to visual targets, visual information contributes more to the motor plan than proprioception. In contrast, proprioceptive information is weighted more for movements toward proprioceptive targets (e.g., body positions). These target modality-dependent weighting mechanisms influence movement errors, kinematics, and the cortical treatment of sensory information (Sarlegna and Sainburg 2007, Blouin et al., 2014); however, it is presently unknown if these processes have an effect on multisensory perception during action. Twelve participants performed reaching movements to a 30 cm target defined by either a light emitting diode (i.e., visual target) or the index fingertip of the non-reaching hand (i.e., proprioceptive target). Audiovisual stimuli known to induce illusions (Shams et al., 2000; Andersen et al., 2004) were presented at 0 ms, 100 ms, and 200 ms relative to movement onset. After each trial, participants reported the number of visual events they perceived. Similar to previous studies, for the visual target, it was found that participant's perception of visual events was better if the illusion was presented at 0 ms and 100 ms compared to the 200 ms. These time-points corresponded to high velocity portions of the limb movement (see: Tremblay and Nguyen 2010). In contrast, there was no significant modulation of audiovisual perception for reaches performed to proprioceptive targets. These results support the idea that context dependent multisensory processes influence audiovisual perception during action.Acknowledgments: NSERC, CAMPUS FRANCE
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".