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Abstract:
on two fundamentally distinct modes of representation: rules
and similarity-to-exemplars. Through a combination of experiments
and formal analysis, I show how a Bayesian framework offers a
unifying account of both rule-based and similarity-based
generalization. Bayes explains the specific workings of these two
modes -- which rules are abstracted, how similarity is measured --
as well as why generalization appears rule-based or
similarity-based in different situations. I conclude that the
distinction between rules and similarity in concept learning may be
useful at the level of heuristic algorithms but is not
computationally fundamental.
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