Cognitive development - Wikipedia
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Developing without concepts. Behavioral and Brain Sciences, 33, Madole, K. Correct answers for these tasks require children to appreciate that an agent can have a false belief that contradicts the reality with which they are faced. Hence, success on false belief tasks is taken as indicating that ToM has become mature enough to function as an inference engine for reasoning about other people's beliefs, as distinct from one's own. Many studies have revealed that children come to understand other people's false beliefs at around 4 or 5 years of age; in addition, such a developmental transition appears to occur gradually e.
Two main theories have been proposed for explaining the process of ToM development: theory-theory Gopnik and Wellman, , and simulation theory Gordon, ; Gallese and Goldman, Theory-theory assumes that ToM ability rests on a set of rules, or literally theories, about how the minds of others work. It thus claims that children learn and become able to use such theories to predict and explain others' mental states and their behavior. In contrast, simulation theory argues that ToM ability does not require theorizing the minds of others.
Instead, it claims that children come to use their own minds as a simulation model to mimic and understand the minds of others. These theories have long been regarded as contrasting conceptualizations of ToM. In recent years, however, a number of researchers have advocated hybrid theories that incorporate the essences of both theory and simulation Nichols and Stich, ; Saxe, ; Goldman, ; Mitchell et al.
Notably, Mitchell et al. Recent neuroimaging findings further support such hybrid approaches, demonstrating mixed evidence for the neural mechanisms of ToM responsible for either theory-based or simulation-based reasoning Apperly, ; Mahy et al. In spite of a large body of empirical findings and recent theoretical advances in ToM research, relatively few studies have proposed computational models of ToM understanding, particularly false belief understanding O'Laughlin and Thagard, ; Goodman et al.
Moreover, none deal with mixed reasoning strategies based on theory-theory and simulation theory. Therefore, it still remains unclear whether and how children can, in principle, combine both strategies into a coherent theory of mind. In this study, we present a computational model that integrates theory-based and simulation-based strategies for false belief reasoning.
Our model builds on a Bayesian framework and thus provides a rational account of children's ToM.
It also makes testable predictions about children's performance on false belief tasks, allowing a quantitative comparison with existing behavioral data. We argue that a developmental ToM scale Wellman and Liu, is of particular relevance for any computational model of false beliefs. 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.
This kind of developmental sequence has been confirmed for preschool children with diverse cultural backgrounds Wellman and Liu, ; Peterson et al.
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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, ; Spirtes et al. In so doing, we show that this Bayesian network in effect provides a natural way to integrate theory-based and simulation-based strategies. We further demonstrate that our model provides a good fit to the existing ToM scale data.
A Bayesian network is a graphical model that provides a compact representation of the joint probability distribution for a set of random variables Pearl, ; Spirtes et al. Its graph structure represents the causal probabilistic relationship among the variables, specifies a particular factorization of the joint probability distribution, and enables efficient computation of probability distributions of the unobserved variables, given the observed ones.
Bayesian networks have been used in a wide range of fields and applications, such as computer science, engineering, statistics, medical diagnosis, and bioinformatics. Recently, they have found application in various areas of psychology, such as visual perception Kersten et al. Notably, Gopnik and Wellman have argued that Bayesian networks can be used for formalizing a theory-theory of cognitive development. In the following, we use this Bayesian network formalism to elaborate a model of false belief reasoning in children.
From a theory-theory perspective, Goodman et al. Our Bayesian formulation closely follows their work; the differences are that we additionally take into account the idea of simulation theory and that we focus on the unexpected-contents task to model false belief reasoning. The latter is motivated by the fact that this type of task was commonly used across the ToM scale studies listed above, but has not been the subject of formal analysis. The extension of our model to the change-of-location task will be discussed later.
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We note that the unexpected-contents task is divided into two stages. According to Wellman and Liu , in the first stage, a child sees a familiar, closed Band-Aid box that holds inside a plastic pig toy. From the appearance of the Band-Aid box, the child first expects Band-Aids inside.