CSAIL Forum with Alison Gopnik

Please join us for the CSAIL Forum with Alison Gopnik

CSAIL Forum hosted by Daniela Rus
Speaker: Alison Gopnik
Distinguished Professor of Psychology, Affiliate Professor of Philosophy, and Berkeley Artificial Intelligence Research Group, UC Berkeley
Title: Empowerment Gain as Causal Learning, Causal Learning as Empowerment Gain: A bridge between Bayesian causal hypothesis testing and reinforcement learning
Date/time: Tuesday 12:00-1:00 EDT, November 4, 2025 
Venue: Live stream via Zoom: Registration required
Bio: 
Alison Gopnik is a leader in cognitive science, particularly the study of learning and development. She was a founder of the field of “theory of mind”, an originator of the “theory theory” of cognitive development, and the first to apply Bayesian models to children’s learning. She has received the APS Lifetime Achievement, Cattell, and William James Awards, the SRCD Lifetime Achievement Award, the APA Distinguished Scientific Contributions Award, the Bradford Washburn and Carl Sagan Awards for Science Communication, and the Rumelhart Prize for Theoretical Foundations of Cognitive Science. She is a member of the National Academy of Sciences and the American Academy of Arts and Sciences and a Cognitive Science Society, American Association for the Advancement of Science, and Guggenheim Fellow. She was 2022-23 President of the Association for Psychological Science. She has six grandchildren. 

She is the author of over 160 journal articles and several books including the bestselling and critically acclaimed popular books The Scientist in the Crib, 1999, The Philosophical Baby, 2009, and The Gardener and the Carpenter, 2016. She has written widely about cognitive science and psychology for The Wall Street Journal, The New York Times, The Economist, and The Atlantic, among others. Her TED talk has been viewed more than 5.6 million times. She has frequently appeared on TV, radio, and podcasts including “The Charlie Rose Show”, “The Colbert Report”, and “The Ezra Klein Show”. 

Abstract
Learning about the causal structure of the world is a fundamental problem for human cognition, and causal knowledge is central to both intuitive and scientific world models. However, causal models and especially causal learning have proved to be difficult for standard Large Models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. These approaches also face challenges when it comes to learning however. In parallel, in the very different tradition of reinforcement learning, researchers have developed the idea of an intrinsic reward signal called “empowerment”. An agent is rewarded for maximizing the mutual information between its actions and their outcomes, regardless of the external reward value of those outcomes. In other words, the agent is rewarded if variation in an action systematically leads to parallel variation in an outcome so that variation in the action predicts variation in the outcome. Empowerment, then has two dimensions , it involves both controllability and variability. The result is an agent that has maximal control over the maximal part of its environment. “Empowerment” may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines.  If an agent learns an accurate causal model of the world they will necessarily increase their empowerment, and, vice versa, increasing empowerment will lead to a more accurate (if implicit) causal model of the world. Empowerment may also explain distinctive empirical features of children’s causal learning, as well as providing a more tractable computational account of how that learning is possible.