%A Asakura,Nobuhiko
%A Inui,Toshio
%D 2016
%J Frontiers in Psychology
%C
%F
%G English
%K false belief,Theory-theory,simulation theory,Bayesian network,internal model
%Q
%R 10.3389/fpsyg.2016.02019
%W
%L
%N 2019
%M
%P
%7
%8 2016-December-27
%9 Hypothesis and Theory
%+ Nobuhiko Asakura,Department of Psychology, Otemon Gakuin University,Osaka, Japan,n-asakura@otemon.ac.jp
%#
%! A Bayesian Framework for False Belief Reasoning
%*
%<
%T A Bayesian Framework for False Belief Reasoning in Children: A Rational Integration of Theory-Theory and Simulation Theory
%U https://www.frontiersin.org/article/10.3389/fpsyg.2016.02019
%V 7
%0 JOURNAL ARTICLE
%@ 1664-1078
%X Two apparently contrasting theories have been proposed to account for the development of children's theory of mind (ToM): theory-theory and simulation theory. We present a Bayesian framework that rationally integrates both theories for false belief reasoning. This framework exploits two internal models for predicting the belief states of others: one of self and one of others. These internal models are responsible for simulation-based and theory-based reasoning, respectively. The framework further takes into account empirical studies of a developmental ToM scale (e.g., Wellman and Liu, 2004): developmental progressions of various mental state understandings leading up to false belief understanding. By representing the internal models and their interactions as a causal Bayesian network, we formalize the model of children's false belief reasoning as probabilistic computations on the Bayesian network. This model probabilistically weighs and combines the two internal models and predicts children's false belief ability as a multiplicative effect of their early-developed abilities to understand the mental concepts of diverse beliefs and knowledge access. Specifically, the model predicts that children's proportion of correct responses on a false belief task can be closely approximated as the product of their proportions correct on the diverse belief and knowledge access tasks. To validate this prediction, we illustrate that our model provides good fits to a variety of ToM scale data for preschool children. We discuss the implications and extensions of our model for a deeper understanding of developmental progressions of children's ToM abilities.