Crossmodal associations modulate multisensory spatial integration
Bibliographic record
Abstract
According to the Bayesian framework of multisensory integration, audiovisual stimuli associated with a stronger prior belief that they share a common cause (i.e., causal prior) are predicted to result in a greater degree of perceptual binding and therefore greater audiovisual integration. In the present psychophysical study, we systematically manipulated the causal prior while keeping sensory evidence constant. We paired auditory and visual stimuli during an association phase to be spatiotemporally either congruent or incongruent, with the goal of driving the causal prior in opposite directions for different audiovisual pairs. Following this association phase, every pairwise combination of the auditory and visual stimuli was tested in a typical ventriloquism-effect (VE) paradigm. The size of the VE (i.e., the shift of auditory localization towards the spatially discrepant visual stimulus) indicated the degree of multisensory integration. Results showed that exposure to an audiovisual pairing as spatiotemporally congruent compared to incongruent resulted in a larger subsequent VE (Experiment 1). This effect was further confirmed in a second VE paradigm, where the congruent and the incongruent visual stimuli flanked the auditory stimulus, and a VE in the direction of the congruent visual stimulus was shown (Experiment 2). Since the unisensory reliabilities for the auditory or visual components did not change after the association phase, the observed effects are likely due to changes in multisensory binding by association learning. As suggested by Bayesian theories of multisensory processing, our findings support the existence of crossmodal causal priors that are flexibly shaped by experience in a changing world.
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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 | 0.003 |
| 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 | 0.002 |
| 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".