Why the Child's Theory of Mind Really Is a Theory

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73444/1/j.1468-0017.1992.tb00202.x.pd

Bayes and Blickets: Effects of Knowledge on Causal Induction in Children and Adults

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account.Mitsubishi Electronic Research LaboratoriesUnited States. Air Force Office of Sponsored ResearchMassachusetts Institute of Technology. Paul E. Newton ChairJames S. McDonnell Foundatio

Just do it? Investigating the gap between prediction and action in toddlers' causal inferences

Adults’ causal representations integrate information about predictive relations and the possibility of effective intervention; if one event reliably predicts another, adults can represent the possibility that acting to bring about the first event might generate the second. Here we show that although toddlers (mean age: 24 months) readily learn predictive relationships between physically connected events, they do not spontaneously initiate one event to try to generate the second (although older children, mean age: 47 months, do; Experiments 1 and 2). Toddlers succeed only when the events are initiated by a dispositional agent (Experiment 3), when the events involve direct contact between objects (Experiment 4), or when the events are described using causal language (Experiment 5). This suggests that causal language may help children extend their initial causal representations beyond agent-initiated and direct contact events.James S. McDonnell Foundation (Causal Learning Collaborative)American Psychological FoundationTempleton Foundatio

Sensitive perception of a person’s direction of walking by 4-year-old children.

Watch any crowded intersection, and you will see how adept people are at reading the subtle movements of one another. While adults can readily discriminate small differences in the direction of a moving person, it is unclear if this sensitivity is in place early in development. Here, we present evidence that 4-year-old children are sensitive to small differences in a person's direction of walking (ϳ7°) far beyond what has been previously shown. This sensitivity only occurred for perception of an upright walker, consistent with the recruitment of high-level visual areas. Even at 4 years of age, children's sensitivity approached that of adults'. This suggests that the sophisticated mechanisms adults use to perceive a person's direction of movement are in place and developing early in childhood. Although the neural mechanisms for perceiving biological motion develop slowly, they are refined enough by age 4 to support subtle perceptual judgments of heading. These judgments may be useful for predicting a person's future location or even their intentions and goals

Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference

a b s t r a c t People can behave in a way that is consistent with Bayesian models of cognition, despite the fact that performing exact Bayesian inference is computationally challenging. What algorithms could people be using to make this possible? We show that a simple sequential algorithm ''Win-Stay, Lose-Sample'', inspired by the Win-Stay, Lose-Shift (WSLS) principle, can be used to approximate Bayesian inference. We investigate the behavior of adults and preschoolers on two causal learning tasks to test whether people might use a similar algorithm. These studies use a ''mini-microgenetic method'', investigating how people sequentially update their beliefs as they encounter new evidence. Experiment 1 investigates a deterministic causal learning scenario and Experiments 2 and 3 examine how people make inferences in a stochastic scenario. The behavior of adults and preschoolers in these experiments is consistent with our Bayesian version of the WSLS principle. This algorithm provides both a practical method for performing Bayesian inference and a new way to understand people's judgments

Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood

How was the evolution of our unique biological life history related to distinctive human developments in cognition and culture? We suggest that the extended human childhood and adolescence allows a balance between exploration and exploitation, between wider and narrower hypothesis search, and between innovation and imitation in cultural learning. In particular, different developmental periods may be associated with different learning strategies. This relation between biology and culture was probably coevolutionary and bidirectional: life-history changes allowed changes in learning, which in turn both allowed and rewarded extended life histories. In two studies, we test how easily people learn an unusual physical or social causal relation from a pattern of evidence. We track the development of this ability from early childhood through adolescence and adulthood. In the physical domain, preschoolers, counterintuitively, perform better than school-aged children, who in turn perform better than adolescents and adults. As they grow older learners are less flexible: they are less likely to adopt an initially unfamiliar hypothesis that is consistent with new evidence. Instead, learners prefer a familiar hypothesis that is less consistent with the evidence. In the social domain, both preschoolers and adolescents are actually the most flexible learners, adopting an unusual hypothesis more easily than either 6-y-olds or adults. There may be important developmental transitions in flexibility at the entry into middle childhood and in adolescence, which differ across domains

The Child as Econometrician:A Rational Model of Preference Understanding in Children

Recent work has shown that young children can learn about preferences by observing the choices and emotional reactions of other people, but there is no unified account of how this learning occurs. We show that a rational model, built on ideas from economics and computer science, explains the behavior of children in several experiments, and offers new predictions as well. First, we demonstrate that when children use statistical information to learn about preferences, their inferences match the predictions of a simple econometric model. Next, we show that this same model can explain children's ability to learn that other people have preferences similar to or different from their own and use that knowledge to reason about the desirability of hidden objects. Finally, we use the model to explain a developmental shift in preference understanding