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arXiv · 1409.6745

A Concept Learning Approach to Multisensory Object Perception

Ifeoma Nwogu·Goker Erdogan·Ilker Yildirim·Robert Jacobs
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Abstract

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.

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Ifeoma Nwogu, Goker Erdogan, Ilker Yildirim, Robert Jacobs. 2014-09-23. A Concept Learning Approach to Multisensory Object Perception. https://arxiv.org/abs/1409.6745

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