HD 6625

Moral Learning: Computation, Rationality, and Development

Course Description

Course information provided by the Courses of Study 2019-2020.

Over the last decade, computational approaches to causal learning and belief revision flourished. Bayesian approaches to learning in particular have been productive and influential. In this seminar, we will explore some of this recent work in learning theory and consider its implications for moral learning and moral development. The course is divided into three parts: Part one includes an overview of Bayesian learning theories of cognitive development. Part two covers current theoretical debates in moral psychology. In part three we will evaluate the state of evidence on moral learning in children and adults within the framework of learning theory. Topics include: prosociality, fairness, rule following, parochialism, conformity, and cultural influences.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: Graduate Standing in HD, PSYCH, or COGST. Recommended prerequisite: advanced coursework in cognitive science, developmental science, or moral psychology.

Distribution Category (CA-HE, KCM-HE, SBA-HE)

• Students will learn about moral learning from a cognitive and computational perspective.
• Students will learn to critically evaluate empirical work in cognitive science.
• Students will lead discussions and work independently on analytical writing and oral presentation skills.

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