Multisensory Continuous Psychophysics: Heading Perception is Faster but Not More Precise When Both Sound and Visual Cues are Present
Bibliographic record
Abstract
Heading perception is an inherently multisensory phenomenon that can involve, among others, visual and auditory cues. Like in many other tasks, the presence of multisensory over unisensory cues is expected to lead to both higher precision in responses and lower reaction times – findings that are fairly well established in the trial-based tasks that are typical of this area of study. Here, we used a novel paradigm from vision science - continuous psychophysics - to investigate whether such enhancements of multisensory heading perception were found. We immersed 25 participants in a virtual environment in which they either experienced unisensory visual or auditory information consistent with self-motion that continuously changed direction, or consistent visual and auditory information at the same time. They were asked to continuously align a joystick with their direction of motion. Contrary to our expectations, we did not find any differences in precision between the three (auditory, visual, and visuo-auditory) conditions. However, we did find that participants reacted faster to changes in the stimulus in the visuo-auditory condition than in either of the unisensory conditions. While this discrepancy between our results and what has generally been reported in the literature (e.g., Ernst & Banks, 2002) might be the consequence of a speed-accuracy trade-off (Drugowitsch et al., 2014), it underlines the importance of testing long-established findings using novel paradigms in new, diversified contexts. References: Drugowitsch et al. (2014) eLife 3, e03005 Ernst and Banks (2002) Nature 415, 429-33
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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 | 0.002 |
| 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) | 0.004 | 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".