Discriminating Audiovisual Speed: Optimal Integration of Speed Defaults to Probability Summation When Component Reliabilities Diverge

Abstract

We investigated audiovisual speed perception to test the maximum-likelihood-estimation (MLE) model of multisensory integration. According to MLE, audiovisual speed perception will be based on a weighted average of visual and auditory speed estimates, with each component weighted by its inverse variance, a statistically optimal combination that produces a fused estimate with minimised variance and thereby affords maximal discrimination. We use virtual auditory space to create ecologically valid auditory motion, together with visual apparent motion around an array of 63 LEDs. To degrade the usual dominance of vision over audition, we added positional jitter to the motion sequences, and also measured peripheral trajectories. Both factors degraded visual speed discrimination, while auditory speed perception was unaffected by trajectory location. In the bimodal conditions, a speed conflict was introduced (48° versus 60° s−1) and two measures were taken: perceived audiovisual speed, and the precision (variability) of audiovisual speed discrimination. These measures showed only a weak tendency to follow MLE predictions. However, splitting the data into two groups based on whether the unimodal component weights were similar or disparate revealed interesting findings: similarly weighted components were integrated in a manner closely matching MLE predictions, while dissimilarity weighted components (greater than 3: 1 difference) were integrated according to probability-summation predictions. These results suggest that different multisensory integration strategies may be implemented depending on relative component reliabilities, with MLE integration vetoed when component weights are highly disparate.