Integration of redundant signals in dynamic multisensory contexts: the principles and computational mechanisms

One benefit of multisensory integration is the acceleration of responses in a perceptual decision task, known as the redundant signals effect (RSE). Based on probability summation, two basic principles have been proposed to explain such benefit: the "principle of congruent effectiveness", which suggests maximal RSE when unisensory performances are comparable, and the "variability rule", which proposes that RSE increases when unisensory performances are more variable. Yet, it remains unclear whether these principles extend to dynamic multisensory contexts and how they manifest in the model architectures proposed to explain RSE. To address these questions, we evaluated RSE in a multisensory context featuring transient onsets and offsets using a change detection task. Our results showed that both principles predicted the rank of the RSE in this dynamic setting, with the principle of congruent effectiveness emerging as the dominant one. Model comparison identified two best-performing model architectures that implement distinct crossmodal interactions (enhanced evidence accumulation rates or lowered decision criterion) in the perceptual process when two unimodal signals race towards their decision criterion. Moreover, the key predictors derived from the principles effectively modulate the crossmodal interactions in the two models. Together, these findings demonstrate that the principles underlying RSE generalize to dynamic multisensory contexts and operate via distinct computational mechanisms, shedding light on how perceptual systems flexibly integrate multisensory cues to optimize decision-making.