My research explores how young children come to know about the world around them. The work is informed by the "theory theory" -- the idea that children develop and change intuitive theories of the world in much the way that scientists do. Most recently, we have been concentrating on young children's causal knowledge and causal learning across domains, including physical, biological and psychological knowledge. In collaboration with computer scientists, we are using the Bayes Net formalism to help explain how children are able to learn causal structure from patterns of data, and we have demonstrated that young children have much more powerful causal learning mechanisms than was previously supposed.
Post Doctoral Students
I am a postdoctoral researcher in Professor Alison Gopnik's Cognitive Development Lab and Professor Tania Lombrozo's Concepts and Cognition Lab. I study explanation and causal reasoning in adults and across development. I approach explanation both as a process and a product: I investigate cognitive consequences of engaging in explanation (process) and of producing different types of explanation, such as formal, causal-mechanistic, teleological, structural, mathematical, etc., of different levels of complexity (product). My research on causal reasoning examines the role of stability, or robustness of causal relationships across varying background circumstances. In my current research projects, I explore how explanation and causal reasoning contribute to learning, inductive inference, and decision-making, and how this relationship varies with context, domain-specific experience and development. nadyavasilyeva.weebly.com
I am a Postdoctoral Fellow in Alison Gopnik's Cognitive Development Lab at the University of California, Berkeley and a frequent collaborator with Kalina Christoff's Cognitive Neuroscience of Thought Lab at the University of British Columbia. I received my PhD in Philosophy from the University of Toronto, where I developed one of the first philosophical theories of mind-wandering. My theory focuses on the dynamics of thought: in particular, why the wandering mind drifts from one topic to another.
My postdoctoral research extends this project in two related directions, which lie at the intersection of philosophy, developmental psychology, and cognitive neuroscience. My research with Professor Gopnik focuses on the similarities between adult mind-wandering and how children's thinking and imagination. I am studying whether mind-wandering can induce child-like thinking in adults, improving performance in situations where children learn more flexibly than adults.
My research focuses on how children develop an understanding of every day causal events in the world, both social and physical. I am interested in the interaction between innate constraints on conceptual development and cultural learning processes. I have been working on a series of studies that compare Berkeley children to children in China. These studies explore the development of causal attributions for social and physical events and the development of beliefs about free will. Future work will explore the relationship between culture, learning, and early education curriculum in Peru and the U.S.
My research interests, broadly defined, are informed by two closely related questions about conceptual development: 1) how do children acquire abstract knowledge, such as complex causal relations, and 2) once children acquire this knowledge how does it interact with their existing beliefs to revise their theories about the world and facilitate the acquisition of new concepts? My work thus far explores abstract knowledge from the perspective of categorical relational learning and word learning (e.g., number word learning and adjectival word learning). Future work, however, will continue to expand upon these questions by examining other kinds of abstract knowledge including causal reasoning.
Hi, my name is Shaun, and I am interested in learning more about the development of social cognition. As a second year graduate student in Alison Gopnik's lab, I study how young children reason about other people’s actions, thoughts, and emotions. I would also like to investigate how these reasoning abilities change over time to allow children, adolescents, and adults to navigate increasingly complex social interactions such as communication, pedagogy, and joint action.
I’m interested in how children and adults generate and flexibly update their hypotheses, especially in light of evidence that prompts larger conceptual revision. We often reason about causes and effects without considering the “background” in which these relations are embedded—i.e., the rules, norms, and larger causal structures or systems that govern them. How does “foregrounding” such information impact our causal reasoning, counterfactual thinking, and explanatory judgments? I’m interested in how these and broader questions about mental representations of causality and modality may feature in topics such as science education, creative innovation, and paradigm shifts in scientific thought.
I'm interested in how learners (child, adult, or machine) actively and passively select information from a data-dense environment. In particular, I'm interested in how information processing is flexibly constrained by (1) a learner's current conceptual framework and inductive biases, and (2) the intrinsic reward of acquiring and processing information of a certain degree of complexity. Using both behavioral and computational methods, I aim to explore how these processes interact with a dynamic environment to incite the development and revision of rich systems of knowledge.
I’m interested in learning and problem solving in adults and children. Specifically, I plan to research the strategies that we use to gain information about the world and to integrate that information into our existing knowledge. I am also interested in creative cognition, and would like to explore how children and adults generate creative solutions to difficult problems, as well as how this relates to other methods of problem solving. I also work in Tania Lombrozo’s Concepts and Cognition Lab, where I study the mechanism by which the act of explaining facilitates generalization on the basis of causally relevant properties.
I received my doctorate in Philosophy with a Cognitive Science minor from Rutgers University in 2009. Since then, I have spent two years doing postdoctoral work in Machine Learning followed by three years of research in Developmental Psychology. My work explores how humans and machines come to have mental symbols that mean/represent categories and kinds in the world. At present I am particularly interested in the idea that young children acquire category representations by positing the existence of hidden properties as causal explanations for observed collections of features in objects. This has led me to explore the more general cognitive process of positing novel entities as causal explanations of observable phenomena.
I also run an outreach project, the Paradox Lab (paradoxlab.org), aimed at teaching critical thinking to children, through the 501(c)3 non-profit organization, NeuroLinx.
Hi! I'm Sarah, the Lab Manager for Dr. Gopnik's Cognitive Development Lab. I graduated from UNC Chapel Hill and moved soon thereafter to Berkeley. I hope to investigate how different pedagogical styles and tactics may promote effective learning for children with learning disabilities. I am especially interested in how different pedagogical structures affect later outcomes in development for these children. Please direct any inquiries to email@example.com
I’m a postdoctoral researcher in the Meaning, Culture, and Cognition Lab at Radboud University in Nijmegen, The Netherlands. I completed my PhD in psychology at UC Berkeley, studying universals and variation in spatial language and cognition. My current research focuses on the nature of category systems across languages: how these semantic structures vary, evolve, and influence thought. I’m collaborating with the Gopnik lab to study the roles of language and culture in children’s early reasoning about causes and relations.
How do young children learn so much about the world, and so efficiently? My research investigates how children actively seek information in their physical and social environments as evidence to test and dynamically revise their hypotheses and theories over time. My program explores the development of active learning across the life span, analyzing the effectiveness of children’s information search and hypothesis testing strategies, such as question asking and selective exploration, and identifying potential sources of developmental change. In particular, my work inaugurates the developmental investigation of “ecological learning”, defined as the ability to flexibly and dynamically select those active learning strategies that maximize learning efficiency in different learning environments. Finally, I am interested in developing an approach to classroom learning that leverages children’s active learning strategies and theory-building abilities and harnesses them to inform education policy.
My research explores how children learn and reason about the causal structure of the world. In particular, I am interested in how even very young learners are able to acquire abstract representations that extend beyond their observations, simply by thinking. How is "learning by thinking" possible? What does this phenomenon tell us about the nature of early mental representations and how they change? To begin to answer these questions, my work to date has focused on a suite of activities that impose top-down constraints on human inference, focusing on phenomena that are characteristic of learning in early childhood. My current research includes learning by analogy, by explanation, and by engagement in imaginary worlds. My work is interdisciplinary, combining perspectives in psychology, philosophy, education, and computational theory. See elclab.ucsd.edu
Dr. Walker was co-advised by Drs. Gopnik and Lombrozo, and is now a faculty member in the Department of Psychology at the University of California, San Diego, and PI of the Early Learning & Cognition Lab.
Humans and animals acquire spatial and causal knowledge about the world by making inferences from sparse amounts of data to construct hypotheses. They can then use these hypotheses to inform their future actions. My studies investigate both the kinds of data that humans and animals use and what hypotheses they construct in the form of spatial strategies and planned causal interventions. I conduct these investigations using diverse methodologies including comparative work with animals, computational modeling, and studies involving multiple adults interacting with very young children. Current webpage here.
Dr. Waismeyer was co-advised by Drs. Gopnik and Jacobs, and is now a postdoctoral scholar working with Dr. Andy Meltzoff at the University of Washington.
My research bridges two research traditions: Cognitive Development and Computational Modeling. By bridging these methods, I hope to understand the structure of children's early causal beliefs, how evidence and prior beliefs interact to affect children's learning, the developmental processes that influence children's belief revision, and the role of social factors (such as learning from others) in guiding learning. Current webpage here.
Dr. Bonawitz worked as a post-doctoral fellow with Drs. Gopnik and Griffiths, and is now a faculty member at the Rutgers University - Newark.
I am an Assistant Professor in the Psychology Department at the University of Toronto. My research explores the development and origins of social cognition – how children, adults and animals understand and learn from others’ behavior. I am currently especially interested in the relationship between social learning and causal reasoning, and how social information can be combined with direct observation when making judgments about the causal nature of the world. I use computational models to better understand human social reasoning, and in order to ultimately develop intelligent computer programs with some of these same social learning abilities. Prior to coming to Toronto, I was a Senior Research Fellow in the University of St. Andrews Psychology department, where my research was funded by an ESRC Future Research Leaders grant.
I completed my PhD in 2013 in the UC Berkeley Psychology department, where I worked in Alison Gopnik’s Cognitive Development lab and Tom Griffiths’ Computational Cognitive Science lab. Along with my PhD, I completed a master’s degree in the UC Berkeley Statistics department, focusing on applications of probability theory and statistical computing.
Prior to starting my PhD at UC Berkeley, I was a Complexity Scientist at Icosystem Corporation, where I helped develop software for creating postal routes, modeling the spread of cell phone viruses, visualizing online information, and suggesting good baby names, among other things. Before that, I completed a Masters degree at the MIT Media Lab, working on social learning in interactive animated characters.
In my non-academic life, my dog and I trained as a wilderness search and rescue team with the California Rescue Dog Association and the Alameda County Sheriff’s Department Search and Rescue Unit. More recently, I have been a Bright Club participant, performing academic stand up comedy in St. Andrews, Edinburgh and Dundee. Current website here.
Dr. Buchsbaum was co-advised by Drs. Gopnik and Griffiths, and is now a faculty member at the University of Toronto.