The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2-to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.The input that reaches children from the world is concrete, particular, and limited. Yet, adults have abstract, coherent, and largely veridical representations of the world. The great epistemological question of cognitive development is how human beings get from one place to the other: How do children learn so much about the world so quickly and effortlessly? In the past 30 years, cognitive developmentalists have demonstrated that there are systematic changes in children's knowledge of the world. However, psychologists know much less about the representations that underlie that knowledge and the learning mechanisms that underlie changes in that knowledge.In this article, we outline one type of representation and several related types of learning mechanisms that may play a particularly important role in cognitive development. The representations are of the causal structure of the world, and the learning mechanisms involve a particularly powerful type of causal inference. Causal knowledge is important for several reasons. Knowing about causal structure permits us to make wide-ranging predictions about future events. Even more important, knowing about causal structure allows us to intervene in the world to bring about new eventsoften events that are far removed from the interventions themselves.

Although there is substantial evidence that 30-month-old children can reason about other people's desires, little is known about the developmental antecedents of this ability. A food-request procedure was devised to explore this understanding in 14-and 18-month-olds. Children observed an experimenter expressing disgust as she tasted 1 type of food and happiness as she tasted another type of food. They were then required to predict which food the experimenter would subsequently desire. The 14-month-olds responded egocentrically, offering whichever food they themselves preferred. However, 18-month-olds correctly inferred that the experimenter wanted the food associated with her prior positive affect. They were able to make this inference even when the experimenter's desires differed from their own. These data constitute the first empirical evidence that 18-month-olds are able to engage in some form of desire reasoning. Children not only inferred that another person held a desire, but also recognized how desires are related to emotions and understood something about the subjectivity of these desires.

This research concerns the development of children's understanding of representational change and its relation to other cognitive developments. Children were shown deceptive objects, and the true nature of the objects was then revealed. Children were then asked what they thought the object was when they first saw it, testing their understanding of representational change; what another child would think the object was, testing their understanding of false belief; and what the object looked like and really was, testing their understanding of the appearance-reality distinction. Most 3-year-olds answered the representational change question incorrectly. Most 5-year-olds did not make this error. Children's performance on the representational change question was poorer than their performance on the false-belief question. There were correlations between performance on all 3 tasks. Apparently children begin to be able to consider alternative representations of the same object at about age 4.

Words, Thoughts, and Theories articulates and defends the "theory theory" of cognitive and semantic development, the idea that infants and young children, like scientists, learn about the world by forming and revising theories, a view of the origins of knowledge and meaning that has broad implications for cognitive science. Gopnik and Meltzoff interweave philosophical arguments and empirical data from their own and other's research. Both the philosophy and the psychology, the arguments and the data, address the same fundamental epistemological question: How do we come to understand the world around us? Recently, the theory theory has led to much interesting research. However, this is the first book to look at the theory in extensive detail and to systematically contrast it with other theories. It is also the first to apply the theory to infancy and early childhood, to use the theory to provide a framework for understanding semantic development, and to demonstrate that language acquisition influences theory change in children.The authors show that children just beginning to talk are engaged in profound restructurings of several domains of knowledge. These restructurings are similar to theory changes in science, and they influence children's early semantic development, since children's cognitive concerns shape and motivate their use of very early words. But, in addition, children pay attention to the language they hear around them and this too reshapes their cognition, and causes them to reorganize their theories. Bradford Books imprint

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.