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DOI: 10.1126/Science.1223416 , 1623 (2012); 337 Science Alison Gopnik Scientific Thinking in Young Children: Theoretical Advances, Empirical Research, and Policy Implications Alison Gopnik Science 337, 1623 (2012); DOI: 10.1126/science.1223416 This copy is for your personal, non-commercial use only. If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following the guidelines here. The following resources related to this article are available online at www.sciencemag.org (this information is current as of October 13, 2012 ): Updated information and services, including high-resolution figures, can be found in the online version of this article at: on October 13, 2012 http://www.sciencemag.org/content/337/6102/1623.full.html Supporting Online Material can be found at: http://www.sciencemag.org/content/suppl/2012/09/26/337.6102.1623.DC1.html A list of selected additional articles on the Science Web sites related to this article can be found at: http://www.sciencemag.org/content/337/6102/1623.full.html#related This article cites 29 articles, 8 of which can be accessed free: www.sciencemag.org http://www.sciencemag.org/content/337/6102/1623.full.html#ref-list-1 This article appears in the following subject collections: Psychology http://www.sciencemag.org/cgi/collection/psychology Downloaded from Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2012 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS. REVIEW tween stimuli. Associative learning appears to be very different from the hypothesis testing and ex- perimentation of science. Scientific Thinking in Young Children: In the past 10 years, however, theoretical and empirical research has begun to show that chil- dren’s learning mechanisms do indeed resem- Theoretical Advances, Empirical ble the basic inductive processes of science. We now have a more precise and formal theory of Research, and Policy Implications children’s learning mechanisms, derived from ideas about probabilistic models and Bayesian Alison Gopnik learning methods that originated in computer sci- ence, statistics, and philosophy of science. New theoretical ideas and empirical research show that very young children’s learning and thinking are strikingly similar to much learning and thinking in science. Preschoolers test hypotheses Probabilistic Models against data and make causal inferences; they learn from statistics and informal experimentation, Philosophy of science, artificial intelligence, and from watching and listening to others. The mathematical framework of probabilistic models and developmental psychology all face the and Bayesian inference can describe this learning in precise ways. These discoveries have same fundamental dilemma. As adults, we seem implications for early childhood education and policy. In particular, they suggest both that early to have highly structured, abstract, coherent knowl- childhood experience is extremely important and that the trend toward more structured and edge of the world around us. This knowledge academic early childhood programs is misguided. allows us to make wide-ranging predictions and inferences. But we also seem to learn that highly hirty years ago, the idea that 2-year-olds sophisticated knowledge when they respond structured knowledge from the contingent, con- think like scientists would have seemed to focused questions about real-life examples crete, probabilistic evidence of our senses. How Tabsurd. Jean Piaget, the great pioneer of (2–4). can this be? Traditionally, philosophers and psy- cognitive development, claimed that preschoolers’ This research showed that young children’s chologists have responded to this dilemma in “ ” thinking was just the opposite of scientific think- knowledge is structurally similar to scientific the- two ways. Nativists have argued that this ab- on October 13, 2012 ing. Preschoolers were irrational, illogical, “pre- ories, but not necessarily that children learn like stract structure must be in place innately be- causal,” and limited to the here and now (1). scientists. It could be that much of this knowl- cause it could not possibly be learned. “Empiricists” These ideas informed both education and policy. edge is innate rather than learned—evolutionarily have argued that this abstract structure is illu- These claims have turned out to be wrong. determined rather than inferred from experience. sory; in reality there are only specific learned as- Several waves of empirical work have shown Moreover, until quite recently, there were few the- sociations between particular pieces of evidence. that even infants and very young children have oretical accounts that could encompass learning The probabilistic models approach [e.g., (6–9)] intuitive theories of the world around them. mechanisms in both childhood and science, or addresses this dilemma in a new way. Imagine More recently, mathematical models of learning empirical results that showed those mechanisms that there is some real structure in the world—a have been developed. Empirical research in- were similar. In fact, the predominant theories spatial configuration, a grammar, or a network www.sciencemag.org formed by those models shows that early learning of learning emphasized complex associations be- of causal relationships. That structure gives rise is also remarkably similar to scientific induction (Fig. 1). During the 1980s and 1990s, researchers dis- covered that very young children have abstract, structured, coherent, causal representations of the world around them—representations that are sim- ilar to scientific theories. They use those repre- Downloaded from sentations to make wide-ranging new predictions. These representations appear to be in place even in infancy, but it is particularly clear that pre- schoolers have intuitive theories of the physical, biological, psychological, and social world (2–4). New methods led to this first revolution in our understanding of development. The advent of video recording, and striking experimental ingenuity, led to a flood of results that showed sophisticated knowledge in even the youngest infants. By studying what babies looked at, reached for, or imitated, researchers could show that even infants understand both physical ob- jects and other people [e.g., (3, 5)]. Piaget tried to assess preschool children’sknowledgebyask- ing them open-ended questions about hypothetical scenarios. But preschoolers show much more Department of Psychology, University of California, Berkeley, Fig. 1. Child’s play is science. [“Playing Doctors” by Frederick Daniel Hardy (1827–1911); image: CA 94720, USA. E-mail: [email protected] Stapleton Collection/Corbis] www.sciencemag.org SCIENCE VOL 337 28 SEPTEMBER 2012 1623 REVIEW to some patterns of evidence rather than others— edge that we see in scientific and intuitive the- higher-order causal structure—for example, the a particular set of visual images, or spoken sen- ories. In particular, causal knowledge is central to general framework principles that prevail in a tences, or statistical contingencies between events. both kinds of theories. Causal graphical models scientific paradigm and that shape particular caus- That spatial or grammatical or causal structure or “Bayes nets,” developed in the philosophy of al hypotheses (14). There has also been work on can be represented mathematically by a genera- science and computer science, provide a partic- an even more general “probabilistic logic” that tive model, such as a map or tree structure or a ularly powerful and successful account of causal can encode a much wider range of relationships, graphical network. The representation is a hypoth- knowledge and learning (6, 12, 13). Algorithms including spatial and logical as well as causal esis about what the actual structure is like. This that use Bayes nets allow computers to actually ones. At least in principle, this logic allows a hypothesis can be precisely described in formal do some kinds of science, such as discovering the wide range of generative models to be learned ways. The representation is generative, which causal structure of weather systems, gene expres- from probabilistic data (15). means that it will allow you to mathematically sion, or brain function from data. Unlike traditional nativism, the probabilistic compute the patterns of evidence that follow from Most recently, this work has been expanded models approach gives us a way to actually infer that structure and then make new inferences ac- to allow for formal representations of more abstract abstract hierarchical structure from data, at least cordingly: A particular map will let you predict how to reach a location by a new route; a par- ticular grammatical tree will let you predict wheth- er a new sentence will be acceptable; a particular causal graph will let you predict whether a new event will be followed by other events. If the hypothesis is correct, then these predictions will turn out to be right. These generative models, then, can describe representations of the world and explain how those representations allow us to make a wide range of new inferences. Critically, the systematic link between structure and evidence in these mod- els also allows you to
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