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date: 22 February 2020

Abstract and Keywords

Inductive inferences that take us from observed data to underdetermined hypotheses are required to solve many cognitive problems, including learning categories, causal relationships, and languages. Bayesian inference provides a unifying framework for understanding how people make these inductive inferences, indicating how prior expectations should be combined with data. We introduce the Bayesian approach and discuss how it relates to other approaches such as the “heuristics and biases” research program. We then highlight some of the contributions that have been made by analyzing human cognition from the perspective of Bayesian inference, including connecting symbolic representations with statistical learning, identifying the inductive biases that guide human judgments, and forming connections to other disciplines.

Keywords: bayesian inference, rational models, inductive inference, learning

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