Bayesian Models of Human Learning and Inference

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 or probability computations over a hypothesis space of possible concepts, word meanings, or generalizations. The structure of the learner's computations depends on domain-general statistical principles. The hypotheses can be thought of as either 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.