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My work focuses on learning and cognitive development. I am particularly interested
in high-level cognition, including problems such as categorization, commonsense
reasoning and language acquisition.  I approach these problems by building
computational models and testing them against behavioral data.  Many of these
models rely on probabilistic inference and draw on recent ideas from statistics,
machine learning, and artificial intelligence.

A continuing line of work explores how probabilistic models of reasoning can be
combined with classic approaches to knowledge representation. Real-world
inferences often draw on sophisticated background knowledge: structured
representations can capture some of this knowledge, and probabilistic models can
explain how this knowledge guides inductive reasoning.  I have developed
probabilistic models of commonsense reasoning that draw on the knowledge
embedded in structured representations, including ontologies, causal networks, tree
structures, and logical theories. I have also developed learning algorithms that help
to explain how these structured representations are acquired.

Much of this work is carried out within a hierarchical Bayesian framework. Learning
of any kind must rely on inductive constraints, and I have developed hierarchical
models that help to explain how some of these constraints are acquired. Two of
these models explore how constraints that guide word learning and causal reasoning
can be acquired and used. A third model explores how humans discover which kind
of representation is best for a given domain: for instance, how children discover that
social networks are often organized into cliques, that comparative relations such
as "longer than'' or "better than'' are transitive, and that category labels can be
organized into hierarchies.

last updated 7/11/08 CK/tc

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