CMU talk: The Structure and Acquisition of Semantic Knowledge

The Structure and Acquisition of Semantic Knowledge

Charles Kemp
Department of Brain & Cognitive Sciences
Massachusetts Institute of Technology

Thursday, February 22, 2007

Humans regularly make inferences that go beyond the data they have seen. Two questions immediately arise: what is the knowledge that supports these inferences, and how is this knowledge acquired? I will present a hierarchical Bayesian approach to inductive reasoning that addresses both questions. When making inferences about the distribution of a novel property, people draw on rich semantic knowledge that can often be captured using structured representations of the relationships between the entities in a domain. For instance, given that gazelles have T4 cells and carry E. spirus bacteria, taxonomic relations are useful for predicting which other animals are likely to have T4 cells, but predator-prey relations are more useful when reasoning about the distribution of E. spirus bacteria. I will show that our formal framework provides close quantitative fits to human inferences about several kinds of properties when supplied with appropriate knowledge representations for each task. Different inductive tasks often draw on different kinds of knowledge which are best captured by qualitatively different kinds of representations. For instance, anatomical features of biological species are best captured by a taxonomic tree, political views are best captured by a linear spectrum, and friendship relations are best captured by a set of discrete cliques. Our hierarchical framework helps to explain how humans can discover the best kind of representation for a given inductive context.