Psych225 / CS322

Triangulating Intelligence: Melding Neuroscience, Psychology, and AI

Winter 2022, Tue 1:30--4:30pm, Littlefield 103

Description

This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas.

Recommended prerequisites: PSYCH 1, PSYCH 24/SYMSYS 1/CS 24, CS 221, CS 231N.

Announcement: Winter 2022 students, please join Piazza for discussion. All future announcements will be made through Piazza. We will also have polls on Piazza to help people know each other and find teammates.

Staff

Instructor

Instructor

Instructor

CA

Office Hours: The schedule and Zoom links or locations are available on Canvas.

Hyo: Wednesday 11:30am--12pm
Jiajun: Friday 1:30pm--2pm
Dan: Thursday 1:00pm--1:30pm
Yunzhi: Tuesday 11:30am--1pm, Thursday 10:30am-12pm

Schedule*

Date	Lecture	Instructor	Deliverables
01/04/2022	Background reading No class
Building Machines That Learn and Think Like People Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman Behavioral and brain sciences 40, 2017 The Scientist as Child Alison Gopnik Philosophy of Science 63(4), 1996, Pages 485-514 Science and Core Knowledge Susan Carey and Elizabeth Spelke Philosophy of Science 63(4), 1996, Pages 515-533 Introductory Essay: What does infant perception tell us about theories of perception? Eleanor J. Gibson Journal of Experimental Psychology: Human Perception and Performance 13(4), 1987, Pages 515-523 The Goldilocks Effect: Human Infants Allocate Attention to Visual Sequences That Are Neither Too Simple Nor Too Complex Celeste Kidd, Steven T. Piantadosi, Richard N. Aslin PLoS ONE 7(5), 2012, Page E36399
01/11/2022	Introduction Lecture	Hyo Gweon, Jiajun Wu, Dan Yamins
01/18/2022	Perception Discussion		Short review Due 01/21 11:59pm
Newborn chickens generate invariant object representations at the onset of visual object experience Justin N. Wood PNAS 110(34), 2013, Pages 14000-14005 Receptive fields, binocular interaction and functional architecture in the cat's visual cortex D.H.Hubel and T.N.Wiesel The Journal of physiology 160(1), 1962, Pages 106-154.2 ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton NeurIPS 2012 Performance-optimized hierarchical models predict neural responses in higher visual cortex Daniel L. K. Yamins, Ha Hong, Charles F. Cadieu, Ethan A. Solomon, Darren Seibert, and James J. DiCarlo PNAS 111(23), 2014, Pages 8619-8624 [Optional] How Does the Brain Solve Visual Object Recognition? James J.DiCarlo, Davide Zoccolan, Nicole C.Rust Neuron 73(3), 2012, Pages 415-434 [Optional] Human uncertainty makes classification more robust Joshua C. Peterson, Ruairidh M. Battleday, Thomas L. Griffiths, Olga Russakovsky ICCV 2019 [Optional] Principles of Object Perception Elizabeth S. Spelke Cognitive Science 14(1), 1990, Pages 29-56
01/25/2022	Perception Discussion		Short review Due 01/24 11:59pm
Origins of knowledge Elizabeth S. Spelke, Karen Breinlinger, Janet Macomber, Kristen Jacobson Psychological Review, 99(4), 1992, Pages 605–632 The Code for Facial Identity in the Primate Brain Le Chang and Doris Y. Tsao Cell 169(6), 2017, Pages 1013-1028.e14 Probing Physics Knowledge Using Tools from Developmental Psychology Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-chun Hung, Matt Botvinick arXiv preprint arXiv:1804.01128 (2018) [Optional] Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte PLOS Computational Biology 10(11), 2014 [Optional] Learning Physical Graph Representations from Visual Scenes Daniel M. Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, Daniel L.K. Yamins NeurIPS 2020 [Optional] Object-Centric Learning with Slot Attention Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf NeurIPS 2020 [Optional] Infants' Knowledge About Occlusion and Containment Events: A Surprising Discrepancy Susan J. Hespos and Renée Baillargeon Psychological Science 12(2), 2001, Pages 141-147
02/01/2022	Physics and Causality Discussion		Short review Due 01/31 11:59pm
Simulation as an engine of physical scene understanding Peter W. Battaglia, Jessica B. Hamrick, and Joshua B. Tenenbaum PNAS 110(45), 2013, Pages 18327-18332 Functional neuroanatomy of intuitive physical inference Jason Fischer, John G. Mikhael, Joshua B. Tenenbaum, and Nancy Kanwisher PNAS 113(34), 2016, Pages E5072-E5081 Flexible Neural Representation for Physics Prediction Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins NeurIPS 2018 [Optional] Learning Physical Intuition of Block Towers by Example Adam Lerer, Sam Gross, Rob Fergus ICML 2016 [Optional] Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects Rajesh P. N. Rao and Dana H. Ballard Nature Neuroscience 2, 1999, Pages 79–87 [Optional] Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning William Lotter, Gabriel Kreiman, David Cox ICLR 2017
02/08/2022	Physics and Causality Discussion		Short review Due 02/07 11:59pm
Physion: Evaluating Physical Prediction from Vision in Humans and Machines Daniel M. Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiau-Yu Fish Tung, R.T. Pramod, Cameron Holdaway, Sirui Tao, Kevin Smith, Fan-Yun Sun, Li Fei-Fei, Nancy Kanwisher, Joshua B. Tenenbaum, Daniel L.K. Yamins, Judith E. Fan NeurIPS 2021 Origins of the concepts cause, cost, and goal in prereaching infants Shari Liu, Neon B. Brooks, and Elizabeth S. Spelke PNAS 116(36), 2019, Pages 17747-17752 The role of mental simulation in primate physical inference abilities Rishi Rajalingham, Aida Piccato, Mehrdad Jazayeri PrePrint, 2021 [Optional] PHYRE: A New Benchmark for Physical Reasoning Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick NeurIPS 2019 [Optional] Observing the unexpected enhances infants’ learning and exploration Aimee E. Stahl and Lisa Feigenson Science 348(6230), 2015, Pages 91-94
02/15/2022	Interaction and Social Learning Discussion		Short review Due 02/14 11:50pm
Ten-month-old infants infer the value of goals from the costs of actions Shari Liu, Tomer D. Ullman, Joshua B. Tenenbaumand, Elizabeth S. Spelke Science, 358(6366), 2017, Pages 1038–1041 Neuronal Prediction of Opponent’s Behavior during Cooperative Social Interchange in Primates Keren Haroush, Ziv M.Williams Cell, 160(6), 2015, Pages 1046-1048 Emergent Tool Use From Multi-Agent Autocurricula Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch ICLR 2020 [Optional] Teleological reasoning in infancy: the naı̈ve theory of rational action György Gergely, Gergely Csibra Trends in Cognitive Sciences, 7(7), 2003, Pages 287–292 [Optional] The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology Julian Jara-Ettinger, Hyowon Gweon, Laura E. Schulz, Joshua B. Tenenbaum Trends in Cognitive Sciences 20(10), 2016, Pages 785 [Optional] Comprehensive Longitudinal Study Challenges the Existence of Neonatal Imitation in Humans Janine Oostenbroek, Thomas Suddendorf, Mark Nielsen, Jonathan Redshaw, Siobhan Kennedy-Costantini, Jacqueline Davis, Sally Clark, Virginia Slaughter Current Biology, 26(10), 2016, Pages R412-R414
02/22/2022	Interaction and Social Learning Discussion		Short review Due 02/21 11:59pm
Machine Theory of Mind Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S.M. Ali Eslami, Matthew Botvinick ICML 2018 Bayesian change-point analysis reveals developmental change in a classic theory of mind task Sara T. Baker, Alan M. Leslie, C.R. Gallistel, Bruce M. Hood Cognitive Psychology 91, 2016, Pages 124-149 Single-neuronal predictions of others’ beliefs in humans Mohsen Jamali, Benjamin L. Grannan, Evelina Fedorenko, Rebecca Saxe, Raymundo Báez-Mendoza, Ziv M. Williams Nature 591, 2021, Pages 610–614 [Optional] Rational quantitative attribution of beliefs, desires and percepts in human mentalizing Chris L. Baker, Julian Jara-Ettinger, Rebecca Saxe, Joshua B. Tenenbaum Nature Human Behaviour 1, Article number: 0064, 2017 [Optional] Infants consider both the sample and the sampling process in inductive generalization Hyowon Gweon, Joshua B. Tenenbaum, and Laura E. Schulz PNAS, 107(20), 2010, Pages 9066–9071 [Optional] Chapter 20 - Developmental Cognitive Neuroscience of Theory of Mind H. Gweon, R. Saxe Neural Circuit Development and Function in the Brain, Academic Press, 2013, Pages 367-377 [Optional] Emergence of Pragmatics from Referential Game between Theory of Mind Agents Luyao Yuan, Zipeng Fu, Jingyue Shen, Lu Xu, Junhong Shen, Song-Chun Zhu NeurIPS 2019
03/01/2022	Project presentations
03/08/2022	Project presentations