Alison Gopnik - US grants

Earlier studies have found that 15-21-month-old children often develop particular non-linguistic problem-solving abilities at about the same time that they develop words that embody the concepts to which those abilities are relevant. Later in life, in school, for example, the words children know clearly influence the kinds of problems they can solve. This research will explore whether language can influence cognition in the same fashion even at this very early age? It turns out that different languages emphasize some types of concepts more clearly and saliently than others. For instance, Korean and Japanese place less emphasis on nouns than English does and more on verbs. Children learning Korean and Japanese typically learn verb morphology, the way in which verbs change their form to reflect tense, person, and the like, earlier than English-speakers, but they use fewer nouns. This learning clearly reflects the frequency of these kinds of words in the language they hear. Does this difference in linguistic input also affect the development of non-linguistic cognitive abilities? The research will involve testing Korean- and Japanese-speaking children longitudinally and cross-sectionally and will compare their linguistic and cognitive development to that of English speakers. The children will receive two types of tasks. The first kind, tool-use tasks, measure children's ability to understand actions and the relations between means and ends; these concepts typically are embodied by verbs. The second is sorting tasks, which measure children's ability to understand object categories; these concepts are typically embodied by nouns. The children's early words will also be recorded. If there is a tight correlation between the development of linguistic abilities and non-linguistic problem-solving abilities at this early age, then non-linguistic abilities related to verbs, such as tool use, will develop earlier in the Korean and Japanese speakers, while abilities related to nouns, such as object sorting, will develop earlier in the English speakers. This research potentially has great relevance to early-childhood education. Children are increasingly entering school from environments in which a language other than English is the predominant if not exclusive medium of communication. The results of this line of research should help preschools to deal with these normal children whose pattern of developed abilities would not be the same as that of children from monolingual English-speaking environments.

In the first few years of life, children learn a great deal about the causal structure of the world around them; they learn how some events make other events happen. This knowledge is reflected in children's everyday "theories" of the world. How does this learning take place? This research program will investigate the underlying mechanisms for causal learning in children. In particular, the research will explore how children use information about contingencies between events, such as the fact that one event always follows another event, to draw conclusions about causal relationships. It will investigate how children learn about inhibitory causes, interactive causes and hidden, unobserved causes. This work is informed by recent work in computer science and statistics that shows how computers can make accurate causal inferences. This work should have significant broader impact for educational practice. Causal relationships play a crucial role in scientific knowledge. If we understand children's basic natural causal learning mechanisms, we can use this understanding to help teach science more effectively. Similarly, this research has impact for studies of developmental disabilities such as autism and mental retardation. There is reason to think that children with these syndromes may have particular difficulty with causal learning, and understanding natural causal learning may help us understand and remedy these difficulties.

In the course of development, children change their beliefs, moving from a less to more accurate picture of the world. How do they do this when there are apparently an infinite variety of beliefs from which to choose? And how can we reconcile children's cognitive progress with the apparent irrationality of many of their explanations and predictions? In computer science, probabilistic models have provided a powerful framework for characterizing beliefs, and can tell us when beliefs are justified by the evidence. But they face similar questions: how can one actually get from less warranted beliefs to more accurate ones given a vast space of possibilities? This project brings these threads together, suggesting a possible solution to both challenges. The solution is based on the idea that children may form their beliefs by randomly sampling from a probability distribution of possible hypotheses, testing those sampled hypotheses, and then moving on to sample new possibilities. This "Sampling Hypothesis" provides a natural bridge between understanding how children actually do learn and reason and how computers can be designed to learn and reason optimally. These experiments will provide an important first step in exploring the Sampling Hypothesis: how do evidence and prior beliefs shape the samples of possible beliefs that children generate and evaluate, and how do developmental changes lead to differences in the samples of possible beliefs generated and evaluated.

A relatively immediate contribution of this work will be to connect state-of-the-art methods from machine learning and data analysis in computer science and statistics with accounts of belief acquisition in developmental psychology and educational psychology. In the longer run, the proposed projects have the potential to inform education, early intervention programs, and the study of cognitive deficits; by precisely characterizing how learning should proceed in typically developing children, this project can illustrate when and how developmental limitations impact learning and suggest a framework of ways of helping children with such disorders. The research also supports an ambitious training plan for post-doctoral and graduate student researchers, requiring the development of a nuanced understanding of both computational approaches and developmental experiments.

Psychological research has traditionally been conducted using laboratory experiments, bringing a small number of people into a research laboratory and asking them to complete a task. But the existence--and increasing availability--of online datasets on human behavior and new technologies for data collection suggests a different approach might be possible: mining large databases for clues about how people reason, learn, and interact. Dr. Griffiths, Dr. Gopnik, and Dr. Keltner will establish a research center at the University of California, Berkeley to explore the potential of this data-intensive approach to psychological science. The research center will work with a network of researchers across the country and companies developing technologies for collection of behavioral data to establish pilot projects in cognitive psychology, developmental psychology, and social psychology. These pilot projects will include examining what online databases reveal about human reasoning, how mobile devices can be used to study how children learn, and whether interactions on social networking websites can answer questions about human emotion.

"Big data" research is currently dominated by computer scientists and statisticians, but the questions that are often the focus of this research "understanding human behavior" have traditionally been the domain of psychologists. The new research center for data-intensive psychological science will bring these groups together by establishing collaborations between researchers and developing a curriculum for training students to work at the intersection of these disciplines. The results will be potentially transformative for psychological research, taking it out of the laboratory and into the world. By asking questions that are motivated by decades of psychological theory, data-intensive psychological research will provide new insights into analyzing large behavioral datasets that are potentially relevant to any research project or commercial enterprise that relies upon this kind of data.

How do young children learn so much about the world so quickly and accurately? And how can they learn so much when, at the same time, they often seem so irrational and unpredictable? The research in this proposal will help answer these questions by bringing together ideas from computer science with research on very young children. The basic idea is that young children learn in some of the same ways as the most powerful machine-learning programs. Both the children and the computers explore a wide range of more and less likely possibilities. Moreover, children may sometimes actually explore more widely than adults and so be smarter or at least more open-minded learners. Some of their apparently irrational play, like their wide-ranging pretend play, may really reflect powerful learning methods.

This work should have significant broader impact for educational practice. If we understand children's basic natural rational learning mechanisms we can use those mechanisms to help teach science more effectively. In particular, there are significant practical questions about how we can leverage children's spontaneous play to help them learn. Similarly, this research has impact for studies of developmental disabilities such as autism and mental retardation. There is reason to think that children with these syndromes may have particular difficulty with the kind of learning about possibilities that is facilitated by pretend play, and understanding that learning may help us understand and remedy these difficulties.

One way children make sense of the social world is by forming mental representations of categories of people, such as "girls" and "boys" or "children" and "adults." Forming such categories is a useful way to summarize information and to support novel inferences. For instance, if you learn that a person belongs to the category "child," you can infer that the person is young. This work investigates what children's social categories look like, and how their categories affect the way they explain and reason about aspects of the social world. In particular, the experiments focus on whether and when children are able to appreciate that members of a social category can be associated with a property not only because of shared intrinsic characteristics or preferences, but also as a consequence of the larger social structure in which the category members are situated. For instance, girls could be associated with pink clothing because they have an intrinsic preference for pink, or instead because they are embedded in a social structure that increases the probability that they will select pink, perhaps due to external constraints such as availability and social acceptability. Reasoning about social categories in this way requires "structural thinking." The proposed work will chart the development and consequences of structural thinking from preschool through early childhood.

Understanding the nature and development of social categories is important for a variety of reasons. At a theoretical level, structural thinking challenges dominant approaches to the representation of social categories, and therefore opens up new theoretical possibilities. At a practical level, understanding how children learn and reason about social categories is crucial for developing effective ways to mitigate the effects of harmful stereotypes, which could negatively affect not only a child's interactions with others, but also how the child thinks about him- or herself. More generally, structural thinking plays an important role in our ability to reason effectively about most complex systems: people embedded in social structures, individual species operating within ecosystems, or cities operating within state and federal guidelines, to name just a few. Understanding when and how structural thinking emerges can inform the development of educational efforts in childhood and support better decision-making in adulthood.