An ordinal model of the Mcgurk illusion

Audiovisual information is integrated in speech perception. One manifestation of this is the McGurk illusion in which watching the articulating face alters the auditory phonetic percept. Understanding this phenomenon fully requires a computational model with predictive power. Here, we describe an ordinal model, in which the response categories are ordered cyclically, that can account for the McGurk illusion. We compare this model to the Fuzzy Logical Model of Perception (FLMP), which is not an ordinal model, based on an original data set. While the FLMP fitted the data better than the ordinal model it also employed 30 free parameters where the ordinal model needed only 14. Testing the predictive power of the models using a form of cross-validation we found that, although both models performed rather poorly, the ordinal model performed better than the FLMP. Based on these findings we suggest that ordinal models generally have greater predictive power because they are constrained by a priori information about the adjacency of phonetic categories.