  This paper presents a computational model of concept learning using Bayesian
inference for a grammatically structured hypothesis space, and test the model
on multisensory (visual and haptics) recognition of 3D objects. The study is
performed on a set of artificially generated 3D objects known as fribbles,
which are complex, multipart objects with categorical structures. The goal of
this work is to develop a working multisensory representational model that
integrates major themes on concepts and concepts learning from the cognitive
science literature. The model combines the representational power of a
probabilistic generative grammar with the inferential power of Bayesian
induction.
