The relationship between multisensory associative learning and multisensory integration

Abstract Our perception of the world around us is inherently multisensory, and integrating sensory information from multiple modalities leads to more precise and efficient perception and behaviour. Determining which sensory information from different modalities should be perceptually bound is a key component of multisensory integration. To accomplish this feat, our sensory systems rely on both low-level stimulus features, as well as multisensory associations learned throughout development based on the statistics of our environment. The present study explored the relationship between multisensory associative learning and multisensory integration using encephalography (EEG) and behavioural measures. Sixty-one participants completed a three-phase study. First, participants were exposed to novel pairings audiovisual shape-tone pairings with frequent and infrequent stimulus pairings and complete a target detection task. EEG recordings of the mismatch negativity (MMN) and P3 were calculated as neural indices of multisensory associative learning. Next, the same learned stimulus pairs presented in audiovisual as well as unisensory auditory and visual modalities while both early (<120 ms) and late neural indices of multisensory integration were recorded. Finally, participants completed an analogous behavioural speeded-response task, with behavioural indices of multisensory gain calculated using the race model. Significant relationships were found in fronto-central and occipital areas between neural measures of associative learning and both early and late indices of multisensory integration in frontal and centro-parietal areas, respectively. Participants who showed stronger indices of associative learning also exhibited stronger indices of multisensory integration of the stimuli they learned to associate. Furthermore, a significant relationship was found between neural index of early multisensory integration and behavioural indices of multisensory gain. These results provide insight into the neural underpinnings of how higher-order processes such as associative learning guide multisensory integration.