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 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

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this article. We also thank Denise Hatton, Tisha Baldwin, Joshua Nathan, Helen Sullivan, and Julia Wenzlaff for collecting data. Some of the stimulus materials used in Experiments 1 and 2 are adapted from the stimulus materials used in Rehder and Hastie (1997) and we thank them for inspiring many of the features and objects used in these studies. This project was supported by a National Science Foundation Grant (NSF-SBR 9515085) and a National Institute of Mental Health Grant (RO1 MH57737) given to Woo-kyoung Ahn, a National Science Foundation Graduate Fellowship to Nancy Kim, and a National Institute of Mental Health Postdoctoral Fellowship (MH10888-01A1) to Mary Lassaline

Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.

this paper, we describe a framework for goal-driven learning and its relationship to prior and current theories from each of these perspectives.

This paper is designed to illustrate and consider the relations between three types of metarepresentational ability used in verbal comprehension: the ability to metarepresent attributed thoughts, the ability to metarepresent attributed utterances, and the ability to metarepresent abstract, non-attributed representations (e.g. sentence types, utterance types, propositions). Aspects of these abilities have been separately considered in the literatures on “theory of mind”, Gricean pragmatics and quotation. The aim of this paper is to show how the results of these separate strands of research might be integrated with an empirically plausible pragmatic theory. 1

conditioning, etc.). Piaget spelled out a list of "formal operations," such as modus ponens, the probability schema, etc., which he regarded as the fundamental deductive and inductive rule schemas necessary to understand the world. The cognitive revolution, from its earliest incarnation in the work of such theorists as George Miller and Herbert Simon, until nearly the end of the 20 century, essentially embraced Piaget's position of extreme formalism and content independence of inferential rules. Cognitive scientists' endorsement of the formalist, universalistic position was undoubtedly encouraged by the analogy between the human mind and the computer: brain = hardware, cognitive procedures = operating principles and factory-installed software (Block, 1995). This analogy both encouraged the universality assumption and discouraged any assumption that cognitive procedures might be alterable. The heuristics and biases movement of Kahneman and Tversky (1974) and their colleagues in social

The article discusses claims that conceptual structure is in some part metaphorical, as identified by verbal metaphors like LOVE IS A JOURNEY. Two main interpretations of this view are discussed. In the first, a target domain is not explicitly represented but is instead understood through reference to a different domain. For example, rather than a detailed concept of love per se, one could make reference to the concept of a journey. In the second interpretation, there is a separate representation of love, but the content of that representation is influenced by the metaphor such that the love concept takes on the same structure as the journey concept. It is argued that the first interpretation is not fully coherent. The second interpretation is a possible theory of mental representation, but the article raises a number of empirical and theoretical problems for it. It is concluded that many of the data cited as evidence for metaphoric representations can be accounted for by structural similarity between domains. 1.

This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing

The set of English modal verbs is widely recognized to communicate two broad clusters of meanings: epistemic and root modal meanings. A number of researchers have claimed that root meanings are acquired earlier than epistemic ones; this claim has subsequently been employed in the linguistics literature as an argument for the position that English modal verbs are polysemous (Sweetser, 1990). In this paper I offer an alternative explanation for the later emergence of epistemic interpretations by linking them to the development of the child's theory of mind (Wellman, 1990); if correct, this hypothesis might have important implications for the shape of the semantics of modal verbs.