Research Recap

Recording https://www.bilibili.com/video/BV14i4y1e7Lc/?vd_source=8b926cc5cb9e7d8fb85957e534d96e47 Speaker Michael J. Frank Bio Michael J. Frank is Edgar L Marston Professor of Cognitive, Linguistic & Psychological Sciences at Brown University. He directs the Center for Computational Brain Science within the Carney Institute for Brain Science. He received his PhD in Neuroscience and Psychology in 2004 at the University of Colorado, following undergraduate and master’s degrees in electrical engineering. Frank’s work focuses primarily on theoretical models of frontostriatal circuits and their modulation by dopamine, especially their cognitive functions and implications for neurological and psychiatric disorders. The models are tested and refined with experiments across species, neural recording methods, and neuromodulation. Honors include the Troland Research Award from the National Academy of Sciences (2021), Kavli Fellow (2016), the Cognitive Neuroscience Society Young Investigator Award (2011), and the Janet T Spence Award for early career transformative contributions (Association for Psychological Science, 2010). Dr Frank is a senior editor for eLife. ...

Recording https://www.bilibili.com/video/BV1vg4y1f7ey/?spm_id_from=333.999.0.0&vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Robert C. Wilson Bio Bob Wilson is an Associate Professor of Psychology and Director of the Cognitive Science Program at the University of Arizona. Bob is interested in the computational neuroscience of decision-making, studying all kinds of choices from simple perceptual decisions to judgments about phishing emails. Outside of the lab, Bob enjoys raising chickens and learning the piano. Abstract Many decisions involve a trade-off between exploring unknown options for information and exploiting known options for a more certain payoff. In this talk, I will present evidence that people use two strategies to solve these explore-exploit dilemmas: directed exploration, driven by information, and random exploitation, driven by noise. These two strategies appear to rely on dissociable cognitive and neural processes, but I will show that they can arise from a single model based on mental simulation. This model accounts for the effects of uncertainty, time horizon, and the informativeness of feedback on directed and random exploitation as well as more recent findings suggesting that random exploitation is truly random. I will end with a discussion of our future work on real-world decisions.

Recording https://www.bilibili.com/video/BV1ye411d7Ty/?spm_id_from=333.999.0.0&vd_source=8b926cc5cb9e7d8fb85957e534d96e47 Speaker Weiji Ma Bio Wei Ji Ma is Professor of Neural Science and Psychology at New York University. He received his Ph.D. in Theoretical Physics from the University of Groningen, the Netherlands, in 2001. He switched to computational neuroscience and computational cognitive science, doing postdocs at Caltech and the University of Rochester. He was Assistant Professor of Neuroscience at Baylor College of Medicine from 2008 to 2013 before he joined New York University. His lab uses behavioral experiments, computational models, and (through collaboration) neural measures to investigate how people make perceptual and cognitive decisions. Ma has worked on topics in visual decision-making, neural coding and computation, multisensory perception, working memory, metacognition, complex planning, procrastination, and mindsets. He is the lead author of the textbook Bayesian models of Perception and Action, published by MIT Press in 2023. ...

Recording https://www.bilibili.com/video/BV1ZM411S7d7/ Speaker Charley Wu Bio Charley Wu is a cognitive scientist who is interested in the specific shortcuts and cognitive algorithms that people use to make inference tractable. Using online and virtual reality experiments, He employs computational models to predict and understand human behavior. These models allow us to understand the strategies and approximations that allow people to do so much with so little. Originally trained in Philosophy at the University of British Columbia, He pivoted to cognitive science via a M.Sc. from the University of Vienna and a PhD in Psychology from Humboldt University of Berlin, while based at the Max Planck Institute for Human Development. Prior to joining the University of Tübingen, he was a postdoc at Harvard University working with Fiery Cushman and Sam Gershman. ...

Recording https://www.bilibili.com/video/BV1QN4y1C77n/?vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Seongmin A. Park Bio Seongmin A. Park is a researcher at the Institute of Cognitive Science Marc Jeannerod, CNRS (UMR5229), where he focuses on human learning and decision-making. He received his Ph.D. from the Korea Advanced Institute of Science and Technology and completed his postdoctoral training at CNRS with Dr. Jean-Claude Dreher and at UC Davis with Dr. Erie Boorm Abstract Generalizing past experiences to new situations is a hallmark of human intelligence, but it remains challenging for many AI systems. One proposed mechanism for achieving this behavioral flexibility is through the construction of an “internal model” or “cognitive map” - a structural knowledge representation that indicates the relationships between discrete entities learned from different events. However, we have yet to fully understand how the brain constructs low-dimensional representations from everyday experiences and leverages its cognitive map to promote generalization and flexible decision-making. In this talk, I will present research shedding light on these questions from human neuroimaging and neural network modeling. My findings suggest the brain organizes relationships between discrete entities into a graphical structure embedded in Euclidean space. Moreover, I will discuss how the geometry of the cognitive map interacts with changing task goals to facilitate flexible decision-making. Finally, I will provide evidence that the brain generalizes previously learned abstract knowledge structures to solve novel problems, akin to finding unexplored shortcuts during spatial navigation. By incorporating insights into the neural representation of cognitive maps into computational frameworks like reinforcement learning, my work indicates we can develop a deeper understanding of complex human cognition not fully accounted for by standard models. Uncovering the mechanisms underlying the brain’s remarkable behavioral flexibility has implications for advancing both cognitive science and artificial intelligence.

Recording https://www.bilibili.com/video/BV1G34y1g7Q6/?spm_id_from=333.999.0.0&vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Matt Nassar Bio Matt Nassar is an Assistant Professor in the Department of Neuroscience at Brown University. He received his BA at Colgate University and his Doctorate from the University of Pennsylvania. He completed post-doctoral training at the University of Pennsylvania and Brown University before joining the faculty at Brown. His research examines how the brain prioritizes, segregates, and combines information collected in complex environments and how this process differs across individuals, pathologies, and over-healthy aging. For example, why and how do people prioritize sensory information arriving at certain times or locations? How does this prioritization differ across individuals and change across healthy aging How does the internal state of the brain affect ongoing cognition and sensory processing? What functions might these dynamic fluctuations serve in the real world? ...

Recording https://www.bilibili.com/video/BV1P94y1a7Kf/?spm_id_from=333.999.0.0&vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Mark Ho Bio Mark Ho is an Assistant Professor in the Computer Science Department at Stevens Institute of Technology. Previously, he was a faculty fellow at NYU and a postdoc at Princeton and UC Berkeley. He received his Ph.D in Cognitive Science and M.S. in Computer Science from Brown University. His research combines approaches from cognitive science, social psychology, and computer science to study the computational principles underlying human problem solving and social cognition. ...

Recording https://www.bilibili.com/video/BV1Rh4y1a7iq/?vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Haoxue Fan Bio Haoxue Fan is a fifth-year graduate student at Harvard working with Dr. Elizabeth Phelps. She is interested in how emotion interacts with decision-making under uncertainty, including cognitive processes such as exploration, information seeking, and planning. To answer these questions, she uses a combination of computational modeling, physiological measurements, and behavioral experiments. She is originally from Shanghai in China, and is always eager to know where is the best coffee and boba tea place in town :) ...

Recording https://www.bilibili.com/video/BV1sh4y1A7eu/ Speaker Stefano Palminteri Bio I am a Research Director (equivalent of Full Professor) and head of the Human Reinforcement Learning team, which is part of the Laboratoire de Neurosciences Cognitives et Computationelles. My goal is understanding how humans learn to make decisions at the behavioral, computational and neural levels. I am mainly (but not only!) interested in situations when decisions are based on past experience (a.k.a. reinforcement learning). In the last few years I mainly worked on two computational hypotheses, “relative value” and “learning bias”, concerning human reinforcement learning. In addition to extending these two frameworks, new lines of research in my team investigate social learning , the experience/description gap and, more recently, the intersection between cognitive science and artificial intelligence. I also enjoy questioning the epistemological and methodological foundations of decision-making, neuroeconomics and cognitive science research. ...

Recording https://www.bilibili.com/video/BV1Sj411C77e/?vd_source=e9626f9767e6e22ece9d765f34ba01c5 Speaker Zhaoyu Zuo Bio Zhaoyu Zuo obtained a master’s degree in pattern recognition and intelligent systems from the University of Science and Technology of China and is now a research assistant at Shanghai Jiao Tong University. He is concerned with the nature of human intelligence and the mechanisms that lead to behavior and mental disorders. He uses brain-inspired models to study the cognitive computational mechanisms behind complex decisions, particularly the cooperative relationship between memory systems and reinforcement learning. ...