coggroup January 2023

coggroup@mx1.psych.mcgill.ca

1 participants
2 discussions

[Coggroup] Dec 15: "Intuitive Physical Reasoning and Mental Simulation" (Todd Gureckis, NYU)
by Stevan R. Harnad, Dr. 29 Oct '23

29 Oct '23

cog Intuitive Physical Reasoning and Mental Simulation Todd Gureckis Psychology, NYU UQÀM ISC DIC CRIA Séminaire en informatique cognitive/Cognitive Informatics Seminar Thursday, 10:30 am December 15, 2022 Zoom: https://uqam.zoom.us/j/88481835073 ( videos of past seminars: https://youtu.be/XePaBMc_HFg ) Abstract: The ability to reason about the physics of our world (e.g., what arrangements of objects are stable, how things will fall or move under a force) is central to human intelligence. One influential hypothesis is that this capacity stems from the ability to perform “mental simulations” of physical events (in effect, playing a mental “movie” of the future evolution of a scene according to the laws of physics). In this talk, I’ll try to pin down several core commitments of the mental simulation approach that must be present for the general theory to be viable. I then will describe experiments we conducted recently trying to test these commitments. Along the way, we stumbled into several curious and novel errors and biases in human physical reasoning ability that we believe represent limits to the universality of contemporary simulation theories. If there is time, I will discuss a related project considering how efficient or optimal people are when they “experiment” in the physical world in order to learn the covert properties of objects such as mass or attractive/repulsive forces like magnetism. Todd M. Gureckis, Professor of Psychology, New York University, studies how people actively explore their world in order to learn, including everyday reasoning capacities for the physical and social world. His research combines methods of computational modeling, developmental psychology, cognitive neuroscience, and online data collection. He is the founder and a lead developer of the psiTurk<https://psiturk.org/> package, a tool for facilitating online experiments used in hundreds of research labs. His work has been recognized by the NSF CAREER award, the Presidential Early Career Award (PECASE) from the Office of Science and Technology Policy at the White House, the James S. McDonnell Foundation Scholar award, and several paper and conferences awards with his students including the Marr Prize from the Cognitive Science Society, the Clifford T. Morgan Prize from the Psychonomic Society. He has variously served an Associate Editor for Cognitive Science, Topics in Cognitive Science, and Computational Brain and Behavior. References https://gureckislab.org/<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgureckisl…> : https://gureckislab.org/papers/#/ref/ludwin2021limits<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgureckisl…> https://gureckislab.org/papers/#/ref/ludwinpeery2020broken<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgureckisl…> https://gureckislab.org/papers/#/ref/bramley2018intuitive<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgureckisl…>

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[Coggroup] Karl Friston (UCL): "Active inference and artificial curiosity" 10:30 am Thurs Dec 8 ZOOM
by Stevan R. Harnad, Dr. 10 Jan '23

10 Jan '23

Active inference and artificial curiosity Karl J. Friston Director, Wellcome Centre for Human Neuroimaging Institute of Neurology, UCL London 10:30 am Thursday, December 8 Zoom: https://uqam.zoom.us/j/88481835073 Cognitive Informatics Seminar Séminaire en informatique cognitive UQÀM ISC DIC CRIA Abstract: This talk offers a formal account of insight and learning in terms of active (Bayesian) inference. It deals with the dual problem of inferring states of the world and learning its statistical structure. In contrast to current trends in machine learning (e.g., deep learning), we focus on how agents learn from a small number of ambiguous outcomes to form insight. I will use simulations of abstract rule-learning and approximate Bayesian inference to show that minimising (expected) free energy leads to active sampling of novel contingencies. This epistemic, curiosity-directed behaviour closes `explanatory gaps' in knowledge about the causal structure of the world, thereby reducing ignorance, in addition to resolving uncertainty about states of the known world. We then move from inference to model selection or structure learning to show how abductive processes emerge when agents test plausible hypotheses about symmetries in their generative models of the world. The ensuing Bayesian model reduction evokes mechanisms associated with sleep and has all the hallmarks of aha moments. [A person with his hand on his chin Description automatically generated with medium confidence] Karl Friston, theoretical neuroscientist and authority on brain imaging, invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). Mathematical contributions include variational Laplacian procedures and generalized filtering for hierarchical Bayesian model inversion. Friston currently works on models of functional integration in the human brain and the principles that underlie neuronal interactions. His main contribution to theoretical neurobiology is a free-energy principle for action and perception (active inference). A repository of active inference papers: GitHub - BerenMillidge/FEP_Active_Inference_Papers: A repository for major/influential FEP and active inference papers.<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.co…> Theoretical lecture on the physics behind active inference: I am therefore I think by Karl Friston - YouTube<https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.youtu…>

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coggroup January 2023