The impact of joint attention on the sound-induced flash illusions
Bibliographic record
Abstract
Humans coordinate their focus of attention with others, either by gaze following or prior agreement. Though the effects of joint attention on perceptual and cognitive processing tend to be examined in purely visual environments, they should also show in multisensory settings. According to a prevalent hypothesis, joint attention enhances visual information encoding and processing, over and above individual attention. If two individuals jointly attend to the visual components of an audiovisual event, this should affect the weighing of visual information during multisensory integration. We tested this prediction in this preregistered study, using the well-documented sound-induced flash illusions, where the integration of an incongruent number of visual flashes and auditory beeps results in a single flash being seen as two (fission illusion) and two flashes as one (fusion illusion). Participants were asked to count flashes either alone or together, and expected to be less prone to both fission and fusion illusions when they jointly attended to the visual targets. However, illusions were as frequent when people attended to the flashes alone or with someone else, even though they responded faster during joint attention. Our results reveal the limitations of the theory that joint attention enhances visual processing as it does not affect temporal audiovisual integration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".