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.

Motivated by computational analyses, we look at how teaching affects exploration and discovery. In Experiment 1, we investigated children’s exploratory play after an adult pedagogically demonstrated a function of a toy, after an interrupted pedagogical demonstration, after a naïve adult demonstrated the function, and at baseline. Preschoolers in the pedagogical condition focused almost exclusively on the target function; by contrast, children in the other conditions explored broadly. In Experiment 2, we show that children restrict their exploration both after direct instruction to themselves and after overhearing direct instruction given to another child; they do not show this constraint after observing direct instruction given to an adult or after observing a non-pedagogical intentional action. We discuss these findings as the result of rational inductive biases. In pedagogical contexts, a teacher’s failure to provide evidence for additional functions provides evidence for their absence; such contexts generalize from child to child (because children are likely to have comparable states of knowledge) but not from adult to child. Thus, pedagogy promotes efficient learning but at a cost: children are less likely to perform potentially irrelevant actions but also less likely to discover novel information.

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.

We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: From a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This "naïve utility calculus" lets both children and adults observe others' behavior and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent or an enemy. We review studies providing support for the naïve utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy. Commonsense PsychologyTheories of decision-making have been at the heart of psychology since the field's inception, but only recently has the field turned to the study of how humans -especially the youngest humansthink humans make decisions. When we watch someone make a choice, we explain it in terms of their goals, preferences, personalities, and moral beliefs. This capacity -our commonsense psychology -is the cognitive foundation of human society. It lets us share what we have and know, with those from whom we expect the same in return, and it guides how we evaluate those who deviate from our expectations.The representations and inferential power underlying commonsense psychology trace back to early childhood -before children begin kindergarten, and often even in infancy. Work on how children reason about other agents' goals [1][2][3][4][5][6][7][8], desires [9][10][11], beliefs [12][13][14][15][16][17][18], and pro-social behavior [19][20][21][22][23][24][25][26][27][28][29] has advanced our understanding of what in our commonsense psychology is at work in early infancy [30][31][32] and what develops [16][17][33][34][35]. Nonetheless, major theoretical questions remain unresolved. What computations underlie our commonsense psychology, and to what extent are they specific to the social domain? Are there a small number of general principles by which humans reason about and evaluate other agents, or do we instead learn a large number of special case rules and heuristics? To what extent is there continuity between the computations supporting commonsense psychology in infancy and later ages? Is children's social-cognitive development a progressive refinement of a computational system in place from birth, or are there fundamentally new computational principles coming into play?In this article we advance a hypothesis that offers answers to each of these questions, and provides a unifying framework in which to understand the diverse social-cognitive capacities we see even in young children. We propose that human beings, from early infancy, interpret others' intentional actions through the lens of a naïve utility calculus: that is, people assume that others choose actions to maximize utilities -the rewards they expect to obtain relative to the costs...

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