title: Why formal learning theory matters for cognitive science.
abstract: This article reviews a number of different areas in the foundations of formal learning theory. After outlining the general framework for formal models of learning, the Bayesian approach to learning is summarized. This leads to a discussion of Solomonoff's Universal Prior Distribution for Bayesian learning. Gold's model of identification in the limit is also outlined. We next discuss a number of aspects of learning theory raised in contributed papers, related to both computational and representational complexity. The article concludes with a description of how semi-supervised learning can be applied to the study of cognitive learning models. Throughout this overview, the specific points raised by our contributing authors are connected to the models and methods under review.
authors: Sean
authors: S
authors: Fulop
authors: Nick
authors: N
authors: Chater
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019336:Cognitive Science / Q000379:methods*; D006801:Humans; D007257:Information Theory; D007804:Language Development; D007858:Learning; D008037:Linguistics; D011337:Probability Learning; D011582:Psychological Theory