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.

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...

The ability to make inductive inferences from sparse data is a critical aspect of human learning. However, the properties observed in a sample of evidence depend not only on the true extension of those properties but also on the process by which evidence is sampled. Because neither the property extension nor the sampling process is directly observable, the learner's ability to make accurate generalizations depends on what is known or can be inferred about both variables. In particular, different inferences are licensed if samples are drawn randomly from the whole population (weak sampling) than if they are drawn only from the property's extension (strong sampling). Given a few positive examples of a concept, only strong sampling supports flexible inferences about how far to generalize as a function of the size and composition of the sample. Here we present a Bayesian model of the joint dependence between observed evidence, the sampling process, and the property extension and test the model behaviorally with human infants (mean age: 15 months). Across five experiments, we show that in the absence of behavioral cues to the sampling process, infants make inferences consistent with the use of strong sampling; given explicit cues to weak or strong sampling, they constrain their inferences accordingly. Finally, consistent with quantitative predictions of the model, we provide suggestive evidence that infants' inferences are graded with respect to the strength of the evidence they observe.Bayesian model | cognitive development | exploratory play

Thinking about other people's thoughts recruits a specific group of brain regions, including the temporo-parietal junctions (TPJ), precuneus (PC), and medial prefrontal cortex (MPFC). The same brain regions were recruited when children (N=20, 5-11 years) and adults (N=8) listened to descriptions of characters' mental states, compared to descriptions of physical events. Between ages 5 and 11 years, responses in the bilateral TPJ became increasingly specific to stories describing mental states as opposed to people's appearance and social relationships. Functional activity in the right TPJ was related to children's performance on a high level theory of mind task. These findings provide insights into the origin of neural mechanisms of theory of mind, and how behavioral and neural changes can be related in development.

Humans explain and predict other agents' behavior using mental state concepts, such as beliefs and desires. Computational and developmental evidence suggest that such inferences are enabled by a principle of rational action: the expectation that agents act efficiently, within situational constraints, to achieve their goals. Here we propose that the expectation of rational action is instantiated by a naïve utility calculus sensitive to both agent-constant and agent-specific aspects of costs and rewards associated with actions. In four experiments, we show that, given an agent's choices, children (range: 5-6 year olds; N=96) can infer unobservable aspects of costs (differences in agents' competence) from information about subjective differences in rewards (differences in agents' preferences) and vice versa. Moreover, children can design informative experiments on both objects and agents to infer unobservable constraints on agents' actions.