Abstract:
Face processing plays a central role in human life. Understanding how the brain represents and processes faces is not only important for basic science, but improve the design of machine learning and artificial intelligence systems that socially interact with humans, or mediate social interactions among humans, In this talk, I will give an overview of our recent work modeling human face processing. We leverage a variety of computer vision and machine learning techniques to arrive at an efficient and neurally plausible representation of faces, and develop a statistical modeling framework to investigate cognitive and social aspects of human face processing.
Bio:
Dr. Yu uses mathematically rigorous and algorithmically diverse tools to understand the nature of representation and computation that give rise to intelligent behavior in humans (and other animals), with particular regard to the challenges posed by inferential and predictive uncertainty. Using diverse machine learning and statistical tools, such as Bayesian statistical modeling, control theory, reinforcement learning, and information theory, Dr. Yu and colleagues develop theoretical frameworks and mathematical models to explain disparate aspects of cognition: perception, attention, decision-making, learning, cognitive control, active sensing, economic choice, and social interactions. Dr. Yu's lab mainly specializes in theoretical modeling, but also utilizes various experimental methods, e.g. behavior, eye-tracking, fMRI, to help develop and validate the theoretical concepts. In addition, Dr. Yu collaborates with a number of experimentalists on a range of topics related to human and animal cognition, including neural and psychiatric impairments.