A Concept Learning Approach to Multisensory Object Perception
This work addresses multisensory object recognition for cognitive science and AI, but it is incremental as it builds on existing Bayesian and grammatical approaches.
The paper tackles the problem of multisensory object perception by developing a Bayesian concept learning model with a grammatically structured hypothesis space, tested on artificially generated 3D objects called fribbles, resulting in a working multisensory representational model that integrates cognitive science themes.
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.