Tanguay, A. R. | Search bioRxiv

bioRxiv2025

Did you see the sound? A Bayesian Perspective on Crossmodal Perception in Low Vision

Multisensory integration is often assumed to increase when visual input is degraded, yet it re-mains unclear whether low vision enhances susceptibility to cross-modal illusions or whether such effects depend on local variations in visual reliability. We tested low vision and sighted control participants on the Double Flash Illusion across 24 visual-field locations while sepa-rately measuring flash-detection accuracy. Both groups showed the expected auditory-driven increase in perceived flash numerosity, but only sighted controls reliably experienced the clas-sic "double-flash" percept. Illusion strength did not vary with eccentricity; instead, it was strongly predicted by local flash detection accuracy, indicating that sound-induced percepts depend on the availability of a reliable visual signal. Bayesian Causal Inference modeling revealed substantially weaker and more variable fits for low vision observers, with poorer fits associated with reduced visual sensitivity. Although model parameters did not differ significantly between groups, the similarity in estimated visual noise likely reflects model limitations rather than true equivalence in sensory precision. Together, these findings show that low vision does not globally amplify audiovisual interactions; rather, auditory enhance-ment depends on local visual reliability, and degraded vision leads to weaker alignment with Bayesian-optimal predictions.

neuroscience↗

bioRxiv2024

Filling-in of the Blindspot is Multisensory

We asked three questions about multisensory perception across the physiological blind spot: (1) Does audiovisual integration persist without bottom-up visual input? (2) Does the brain adjust its sensory uncertainties and priors accordingly? (3) Are the underlying causal-inference computations preserved? Participants judged flashes and beeps in an audiovisual illusion presented across the blind spot or a matched control location. Responses were fit with a Bayesian Causal Inference (BCI) model, estimating sensory noise, numerosity priors, and causal-inference priors under multiple decision strategies evaluated using BIC. Illusions were robust at both locations, indicating preserved integration. Model fits showed higher visual uncertainty and broader prior expectations at the blind spot, while auditory precision and the causal prior remained stable. Thus, the computational architecture of causal inference is maintained, but its parameters flexibly adapt to local sensory reliability. These findings demonstrate that perceptual inference remains intact even in regions without retinal input, achieved by adjusting internal uncertainty rather than altering core multisensory computations.

neuroscience↗