Bayesian-based integration of multisensory naturalistic perithreshold stimuli

Most studies exploring multisensory integration have used clearly perceivable stimuli. According to the principle of inverse effectiveness, the added neural and behavioral benefit of integrating clear stimuli is reduced in comparison to stimuli with degraded and less salient unisensory information. Traditionally, speed and accuracy measures have been analyzed separately with few studies merging these to gain an understanding of speed-accuracy trade-offs in multisensory integration. In two separate experiments, we assessed multisensory integration of naturalistic audio-visual objects consisting of individually-tailored perithreshold dynamic visual and auditory stimuli, presented within a multiple-choice task, using a Bayesian Hierarchical Drift Diffusion Model that combines response time and accuracy. For both experiments, unisensory stimuli were degraded to reach a 75% identification accuracy level for all individuals and stimuli to promote multisensory binding. In Experiment 1, we subsequently presented uni- and their respective bimodal stimuli followed by a 5-alternative-forced-choice task. In Experiment 2, we controlled for low-level integration and attentional differences. Both experiments demonstrated significant superadditive multisensory integration of bimodal perithreshold dynamic information. We present evidence that the use of degraded sensory stimuli may provide a link between previous findings of inverse effectiveness on a single neuron level and overt behavior. We further suggest that a combined measure of accuracy and reaction time may be a more valid and holistic approach of studying multisensory integration and propose the application of drift diffusion models for studying behavioral correlates as well as brain-behavior relationships of multisensory integration.