Abstract
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.
Highlights
Inferring and understanding other people’s mental states such as desires, beliefs, and intentions is crucial for our successful social interactions
Our Bayesian formulation closely follows their work; the differences are that we take into account the idea of simulation theory and that we focus on the unexpected-contents task to model false belief reasoning
We have formalized a Bayesian model of false belief reasoning that incorporates the internal models of self and others for belief formation
Summary
Inferring and understanding other people’s mental states such as desires, beliefs, and intentions is crucial for our successful social interactions. The ToM scale consists of tasks to assess children’s understanding of multiple mental state concepts It reflects extant findings of children’s ToM such that they develop an understanding of diverse desires (people can have different desires for the same thing) before developing that of diverse beliefs (people can have different opinions and beliefs about the same situation); they develop understandings of diverse beliefs and knowledge access (others can have different perspectives that prevent them from having access to the true real-world information) before developing that of false beliefs. From a constructivism point of view, such a sequential progression of ToM suggests that an understanding of false beliefs should emerge under the developed understandings of the mental concepts such as diverse desires, diverse beliefs, and knowledge access Taking into account this view, we formalize a model of false belief reasoning based on a Bayesian network (Pearl, 2000; Spirtes et al, 2001) that represents causal relationships among the relevant mental concepts of others and one’s own. We further demonstrate that our model provides a good fit to the existing ToM scale data
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