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Bayesian models of human learning

Dr. Joshua Tenenbaum

Tuesday, October 07, 2003, 01:00pm - 02:00pm

Department of Brain and Cognitive Sciences - Massachusetts Institute of Technology

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October 7, 2003 at 1:00 p.m.

Psychology Room 101, Busch Campus

Dr. Joshua Tenenbaum

Department of Brain and Cognitive Sciences - Massachusetts Institute of Technology

Bayesian models of human learning and reasoning

How can people learn the meaning of a new word from just a few
examples?  What makes a set of examples more or less representative of
a concept?  What makes two objects seem more or less similar?  Why are
some generalizations apparently based on all-or-none rules while
others appear to be based on gradients of similarity?  How do we infer
the existence of hidden causal properties or novel causal laws?  I
will describe an approach to explaining these aspects of everyday
induction in terms of rational statistical inference.  In our Bayesian
models, learning and reasoning are explained in terms of probability
computations over a hypothesis space of possible concepts, word
meanings, or generalizations.  The structure of the learner's
hypothesis spaces reflects their domain-specific prior knowledge,
while the nature of the probability computations depends on
domain-general statistical principles.  The hypotheses can be thought
of as either potential rules for abstraction or potential features for
similarity, with the shape of the learner's posterior probability
distribution determining whether generalization appears more
rule-based or similarity-based.  Bayesian models thus offer an
alternative to classical accounts of learning and reasoning that rest
on a single route to knowledge -- e.g., domain-general statistics or
domain-specific constraints -- or a single representational paradigm
-- e.g., abstract rules or exemplar similarity.  This talk will illustrate
the Bayesian approach to modeling learning and reasoning on a range of
behavioral case studies, and contrast its explanations with those of
more traditional process models.

Dr. Joshua Tenenbaum