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date: 26 January 2021

Abstract and Keywords

This chapter describes the relation between the imagination and causal cognition, particularly with relevance to recent developments in computational theories of human learning. According to the "probabilistic models" view of human learning, our ability to imagine possible worlds and engage in counterfactual reasoning is closely tied to our ability to think causally. Indeed, the purpose and distinguishing feature of causal knowledge is that it allows one to generate counterfactual inferences. The chapter begins with a brief description of the "probabilistic models" framework of causality and Bayesian learning, and reviews empirical work in that framework, which shows that adults and children use causal knowledge to generate counterfactuals. It also outlines a theoretical argument that suggests that the imagination is central to the process of causal understanding and planning and offers evidence that Bayesian learning also implicates the imaginative process. It concludes with a discussion of how this computational method may be applied to the study of the imagination, more classically construed.

Keywords: Bayes nets, causal reasoning, counterfactuals, fictional cognition, imagination

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