Multisensory integration (MI) is defined as the neural process by which unisensory signals are combined to form a new product that is significantly different from the responses evoked by the modality-specific component stimuli. In recent years, MI research has seen exponential growth in the number of empirical and theoretical studies. This study presents a selective overview of formal modeling approaches to MI. Emphasis is on models and measures for behavioral paradigms, such as localization, judgment of temporal order or simultaneity, and reaction times, but some concepts for the modeling of single-cell spike rates are treated as well. We identify a number of essential concepts underlying most model classes, such as Bayesian causal inference, probability summation, coactivation, and time window of integration. Quantitative indexes for measuring and comparing the strength of MI across different paradigms are also discussed. Whereas progress over the last years is remarkable, we point out some strengths and weaknesses of the modeling approaches and discuss some obstacles toward a unified theory of MI.
Keywords: Bayesian causal inference; crossmodal recalibration; diffusion model; probability summation; race model; time window of integration.
© 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.