A Maximum-Likelihood Approach to Modeling Multisensory Enhancement

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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H. Colonius, A. Diederich


Multisensory response enhancement (MRE) is the augmentation of the response of a neuron to sensory input of one modality by si(cid:173) multaneous input from another modality. The maximum likelihood (ML) model presented here modifies the Bayesian model for MRE (Anastasio et al.) by incorporating a decision strategy to maximize the number of correct decisions. Thus the ML model can also deal with the important tasks of stimulus discrimination and identifi(cid:173) cation in the presence of incongruent visual and auditory cues. It accounts for the inverse effectiveness observed in neurophysiolog(cid:173) ical recording data, and it predicts a functional relation between uni- and bimodal levels of discriminability that is testable both in neurophysiological and behavioral experiments.