The Bayesian way to relate rhythm perception and production

Measurements of the perception and production of simple rhythmic patterns have been shown not to be in line in some cases. In this study it is demonstrated that a Bayesian approach provides a new way of understanding this difference, by formalizing the perceptual competition between mental representations and assuming possible nonuniform a priori probabilities of the rhythmic categories. Thus we can relate the two kinds of information and predict perception data from production data. In this approach, the contrast between rhythm perception and production data, taken from different studies in the literature, was shown almost to disappear, assembling independent prior probabilities from counts of patterns in corpora of musical scores, or from a theoretical measure of rhythmic complexity. The success of this Bayesian formalization may be interpreted as an optimal adaptation of our perceptual system to the environment in which the produced rhythms occur.