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Bayesian Theory of Mind: Modeling Human Reasoning About Beliefs, Desires, Goals, and Social Relations

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Bayesian Theory of Mind: Modeling Human Reasoning About Beliefs, Desires, Goals, and Social Relations Bayesian Theory of Mind: Modeling Human Reasoning about Beliefs, Desires, Goals, and Social Relations by b MASSACH 'Ff iNST1TUTE Chris L. Baker B.S., University of Washington (2004) Submitted to the Department of Brain and Cognitive Sciences in partial fulfillment of the requirements for the degree of ARCHIVES Doctor of Philosophy in Cognitive Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2012 © 2012 Massachusetts Institute of Technology. All rights reserved. A uthor .......................... ........ ....................... partment of Brain and Cognitive Sciences May 8, 2012 Certified by........ Joshua B. Tenenbaum, PhD Professor of Cognitive Science and Computation Thesis Supervisor 71~-1,17<7 V Accepted by. .. .... ....... Earl K. Miller, PhD Picower Professor of Neuroscience and Director, Department of Brain & Cognitive Sciences Graduate Program 2 Bayesian Theory of Mind: Modeling Human Reasoning about Beliefs, Desires, Goals, and Social Relations by Chris L. Baker Submitted to the Department of Brain and Cognitive Sciences on May 8, 2012, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract This thesis proposes a computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict others' actions, given their mental states, but also to do the reverse: attribute mental states - beliefs, desires, intentions, knowledge, goals, preferences, emotions, and other thoughts - to explain others' behavior. The goal of this thesis is to provide a formal account of the knowledge and mechanisms that support these judgments. The thesis will argue for three central claims about human ToM. First, ToM is constructed around probabilistic, causal models of how agents' beliefs, desires and goals interact with their situation and perspective (which can differ from our own) to produce behavior. Second, the core content of ToM can be formalized using context-specific models of approximately rational plan- ning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). Third, hypotheses about the structure and content of ToM can be tested through a combination of computational modeling and behavioral experiments. An experimental paradigm for eliciting fine-grained ToM judgments will be proposed, based on comparing human inferences about the mental states and behavior of agents moving within simple two-dimensional scenarios with the inferences predicted by computational models. Three sets of experiments will be presented, investigating models of human goal inference (Chapter 2), joint belief-desire inference (Chapter 3), and inference of interactively-defined goals, such as chasing and fleeing (Chapter 4). BToM, as well as a selection of prominent alternative pro- posals from the social perception literature will be evaluated by their quantitative fit to behavioral data. Across the present experiments, the high accuracy of BToM, and its performance relative to alternative models, will demonstrate the difficulty of capturing human social judgments, and the success of BToM in meeting this challenge. 3 Thesis Supervisor: Joshua B. Tenenbaum, PhD Title: Professor of Cognitive Science and Computation 4 To my parents: Esther, Mike, Kathi, and Jess. 5 6 Acknowledgments "You should not forget the feeling of gratitude. It is rare to meet one another and to practice what is rare to practice." - Zen Master D6gen, Regulationsfor the Auxiliary Cloud Hall I came to MIT to work with Josh Tenenbaum on the precise topic of this thesis: computational modeling of theory of mind. This was a somewhat risky proposition - a novel research direction for the lab, and for the field in general, but Josh convinced me that we would succeed. It is a testament to his penetrating vision, and to his skills as an advisor, how consistent this thesis is with some of our earliest discussions. Since then, I've realized that this was no fluke - Josh's students have continued to embark on ambitious research expeditions, with impressive results every time. Josh really is that smart. Rebecca Saxe played an integral role in this research program from day one. During my first semester, she and Josh organized the "Computational Mindreading Group" (other key members included Whitman Richards, Konrad Kbrding, and Kobi Gal). Her expertise on theory of mind research guided us toward the prior work, open questions, and theoretical approaches that continue to inspire my research today. As a collaborator, Rebecca's input on all aspects of my research has been essential. Rebecca will always set a high standard for my future contributions to the field. Leslie Kaelbling and Nancy Kanwisher made vital contributions as advisory committee mem- bers. Leslie played a particularly important role in encouraging and providing suggestions on the material in Chapter 3. Nancy ensured that my results were evaluated rigorously, while providing great enthusiasm and kindness throughout. Leslie and Nancy are inspiring scientists and outstand- ing role models for an aspiring academic. Chapters 2 & 3 of this thesis are based on papers with Rebecca Saxe and Josh Tenenbaum, and Chapter 4 is based on a paper with Noah Goodman and Josh Tenenbaum. Noah Goodman has been a close collaborator on several other papers and projects, and always an excellent source of ideas and inspiration. 7 My additional fantastic coauthors include: Liz Baraff Bonawitz, Vikash Mansinghka, Alison Gopnik, Henry Wellman, Laura Schulz, Pat Shafto, Russell Warner, Tomer Ullman, Owen Macin- doe, Owain Evans, Julian Jara-Ettinger, Hilary Richardson, Kiley Hamlin, Jacob Feldman, and Peter Pantelis. Our collaborations have genuinely enhanced my thinking and benefitted every as- pect of this thesis. Many members and affiliates of the CoCoSci and Saxe labs provided feedback and friendship: Konrad Kdrding, Tom Griffiths, Charles Kemp, Tevye Krynski, Amy Perfors, Lauren Schmidt, Kobi Gal, Brian Milch, Virginia Savova, Dan Roy, Mike Frank, Ed Vul, David Wingate, Peter Battaglia, Liane Young, Marina Bedny, Emile Bruneau, Frank Jakel, Ruslan Salakhutdinov, Yarden Katz, Steve Piantadosi, Hyowon Gweon, John McCoy, Tim O'Donnell, Brenden Lake, Jessica Hamrick, Cameron Freer, Andreas Stuhlmtiller, Leon Bergen, Nick Dufour, Katherine Heller, Abe Friesen, Mina Cikara, Ben Deen, Jen White, Tao Gao, Josh Hartshorne, Jon Scholz, Max Siegel, Jon Malmaud and Eyal Dechter. I have also enjoyed working with a number of talented undergrad- uate researchers in the CoCoSci lab: Matthew Ng, Kevin Wang, Lindsay Johnson, Jessica Su and Sahar Hakim-Hashemi. Professor Rajesh Rao, my undergraduate research advisor at the University of Washington was an exceptional mentor, providing my first exposure to computational neuroscience, machine learning, and social robotics. In Raj's lab, I worked closely with (then) PhD students Aaron Shon and David Grimes, who taught me an incalculable amount, and inspired me to follow in their footsteps by pursuing my PhD. Their work on Bayesian imitation learning has continued to have an important influence on my own research. I owe special thanks to the hosts of several talks during 2011-2012 which helped sharpen the ideas in this thesis: Tamim Asfour, Chris Geib, Robert Goldman, Henry Kautz, Rineke Verbrugge, Jacob Feldman, and Camille Morvan. Thanks as well to the hosts and attendees of the MIT BCS CogLunch series and the MIT CSAIL ML-Tea series for tolerating several of my talks. I am grateful to the MIT Leventhal Presidential Graduate Fellowship, the Department of Home- land Security Graduate Fellowship, and the National Science Foundation Graduate Fellowship for their generous funding. Additional funding was provided by AFOSR MURI contract FA9550-05- 8 1-0321, the James S. McDonnell Foundation Causal Learning Collaborative Initiative, and ARO MURI grant W91 lNF-08-1-0242. Through activities and meetings relating to these and several other grants, I have been fortunate to interact with many interesting colleagues who have had a lasting influence on my work: Tina Eliassi-Rad, David Roberts, Whitman Richards, Scott Atran, Brian Stankiewicz, Tamar Kushnir, Pedro Domingos, Tom Dietterich, Drew Bagnell, and Brian Ziebart. The Department of Brain and Cognitive Sciences has been an amazing place to do my PhD. The friendships I've made at MIT - too many to list here - will last long after I've left. Special thanks to Denise Heintze, the BCS academic administrator, for administering my graduation. Thank you to the Cambridge surf crew: Devin McCombie (Deuce), James Evans (3stroke), Dariusz Golda (Sarge), Alec Robertson (Buoy), Sebastian Sovero (Cbass), and Dan Sura (Prof. de las Olas) for teaching me to shred! Thank you to Boardman house: Bren Galvez-Moretti, Matt Chua, Kyle Steinfeld, Dennis Ott, Slobodan Radoman, Ben Scott, Vivek Freitas, and Veronika and Dod Bernstein for being awesome. This thesis is dedicated to my parents, but my entire family has always been exceptionally supportive of my education. Thank you to all my grandparents, siblings, aunts, uncles, and cousins who have helped me along the way. Caroline Runyan has been my closest companion, supporter, friend, helper, and colleague for most my time at MIT. Her talent as a scientist is a constant inspiration. It has been
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