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.

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.

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine's activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3‐ and 4‐year‐olds were found. We suggest that children's causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four‐year‐olds, but not 3‐year‐olds were able to make this inference.

Three studies explored whether and when children could categorize objects on the basis of a novel underlying causal power. To test this we constructed a "blicket detector," a machine that lit up and played music when certain objects were placed on it. First, 2-, 3- and 4-year-old children saw that an object labeled as a "blicket" would set off the machine. In a categorization task, other objects were demonstrated on the machine. Some set it off and some did not. Children were asked to say which objects were "blickets." In an induction task, other objects were or were not labeled as "blickets." Children had to predict which objects would have the causal power to set off the machine. The causal power could conflict with perceptual properties of the object, such as color and shape. In an association task the object was associated with the machine's lighting up but did not cause it to light up. Even the youngest children sometimes used the causal power to determine the object's name rather than using its perceptual properties and sometimes used the object's name rather than its perceptual properties to predict the object's causal powers. Children rarely categorized the object on the basis of the associated event. Young children also sometimes made interesting memory errors-they incorrectly reported that objects with the same perceptual features had had the same causal power. These studies demonstrate that even very young children will easily and swiftly learn about a new causal power of an object and spontaneously use that information in classifying and naming the object.

Children's causal learning has been characterized as a rational process, in which children appropriately evaluate evidence from their observations and actions in light of their existing conceptual knowledge. We propose a similar framework for children's selective social learning, concentrating on information learned from others' testimony. We examine how children use their existing conceptual knowledge of the physical and social world to determine the reliability of testimony. We describe existing studies that offer both direct and indirect support for selective trust as rational inference and discuss how this framework may resolve some of the conflicting evidence surrounding cases of indiscriminate trust. Importantly, this framework emphasizes that children are active in selecting evidence (both social and experiential), rather than being passive recipients of knowledge, and motivates further studies that more systematically examine the process of learning from social information.

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Previous research has suggested that preschoolers possess a cognitive system that allows them to construct an abstract, coherent representation of causal relations among events. Such a system lets children reason retrospectively when they observe ambiguous data in a rational manner (e.g., D. M. Sobel, J. B. Tenenbaum, & A. Gopnik, 2004). However, there is little evidence that demonstrates whether younger children possess similar inferential abilities. In Experiment 1, the authors extended previous findings with older children to examine 19- and 24-month-olds' causal inferences. Twenty-four-month-olds' inferences were similar to those of preschoolers, but younger children lacked the ability to make retrospective causal inferences, perhaps because of performance limitations. In Experiment 2, the authors designed an eye-tracking paradigm to test younger participants that eliminated various manual search demands. Eight-month-olds' anticipatory eye movements, in response to retrospective data, revealed inferences similar to those of 24-month-olds in Experiment 1 and preschoolers in previous research. These data are discussed in terms of associative reasoning and causal inference.

The current study uses reach tracking to investigate how cognitive control is implemented during online performance of the Stroop task (Experiment 1) and the Eriksen flanker task (Experiment 2). We demonstrate that two of the measures afforded by reach tracking, initiation time and reach curvature, capture distinct patterns of effects that have been linked to dissociable processes underlying cognitive control in electrophysiology and functional neuroimaging research. Our results suggest that initiation time reflects a response threshold adjustment process involving the inhibition of motor output, while reach curvature reflects the degree of co-activation between response alternatives registered by a monitoring process over the course of a trial. In addition to shedding new light on fundamental questions concerning how these processes contribute to the cognitive control of behavior, these results present a framework for future research to investigate how these processes function across different tasks, develop across the lifespan, and differ among individuals.