Audiovisual Integration During Joint Action: No Effects for Motion Discrimination and Temporal Order Judgment Tasks
Bibliographic record
Abstract
In daily life, humans constantly process information from multiple sensory modalities (e.g., visual and auditory). Information across sensory modalities may (or may not) be combined to form the perception of a single event via the process of multisensory integration. Recent research has suggested that performing a spatial crossmodal congruency task jointly with a partner affects multisensory integration. To date, it has not been investigated whether multisensory integration in other crossmodal tasks is also affected by performing a task jointly. To address this point, we investigated whether joint task performance also affects perceptual judgments in a crossmodal motion discrimination task and a temporal order judgment task. In both tasks, pairs of participants were presented with auditory and visual stimuli that might or might not be perceived as belonging to a single event. Each participant in a pair was required to respond to stimuli from one sensory modality only (e.g., visual stimuli only). Participants performed both individual and joint conditions. Replicating earlier multisensory integration effects, we found that participants' perceptual judgments were significantly affected by stimuli in the other modality for both tasks. However, we did not find that performing a task jointly modulated these crossmodal effects. Taking this together with earlier findings, we suggest that joint task performance affects crossmodal results in a manner dependent on how these effects are quantified (i.e., via responses time or accuracy) and the specific task demands (i.e., whether tasks require processing stimuli in terms of location, motion, or timing).
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".