The Foundations & Frontiers Speaker Series brings leading cognitive scientists to U-M to present a special pair of presentations on the same day. The first presentation serves as an introduction to an important theoretical idea or method in the field -- the Foundations. The second presentation concerns the application of that idea or method to an innovative topic, thus exploring the Frontiers of the field in a way that highlights the significance of the theoretical idea.
Monday, April 3, 2023
East Hall 4448
Dr. Yu conducts research at the intersection of natural and artificial intelligence. Her group uses mathematically rigorous and algorithmically diverse tools to understand the nature of representation and computations that give rise to intelligent behavior, with particular regard to the challenges posed by inferential uncertainty and the opportunities afforded by volitional control. Using diverse machine learning and statistical tools, e.g. Bayesian statistical modeling, control theory, reinforcement learning, and information theory, theoretical frameworks and mathematical models are developed to explain disparate aspects of cognition importation for intelligence: perception, attention, decision-making, learning, cognitive control, active sensing, economic behavior, and social interactions. After completing her bachelor’s at MIT, PhD at UCL Gatsby Computational Neuroscience Unit, and postdoc training at Princeton University, Dr. Yu was faculty at UCSD Cognitive Science for 14 years before joining TU Darmstadt in Germany as an Alexander von Humboldt AI Professor in late 2022. With her interdisciplinary expertise and interest, Dr. Yu’s arrival strengthens and complements research at the Centre for Cognitive Science at TU Darmstadt and at hessian.AI, the Centre for Artificial Intelligence of Hesse based in Darmstadt. Her new group currently has positions open for postdocs, PhD students, master’s students, research interns, and visiting scholars.
3:00-3:30 pm Foundations Presentation
3:30-3:45 pm Q & A
—15 minute break—
4:00-4:50 pm Frontiers Presentation
4:50-5:20 pm Q & A
"Computational Modeling of Human Face Processing"
Face processing plays a central role in everyday human life. We investigate the nature of face representation and processing in the brain, by adapting and developing appropriate machine learning and computer vision methods. In the Foundations portion of the lecture, I will be giving a review of the technical methods utilised in our modeling work to model the psychological face space, including the relevant computer vision and deep neural network models, as well as more classical methods such as multi-dimensional scaling. I will also introduce a novel way of doing regression developed in our lab, and describe its theoretical guarantees and implications for scientific data analysis and interpretation.
In the Frontiers portion of the lecture, I will describe our work using the Active Appearance Model, which has recently been shown to have latent features encoded by face processing neurons in the primate brain, to show that human social trait perception has both a linear component and a quadratic component, with the latter specifically related to the statistical typicality of a face defined as log likelihood of the face under the population face distribution. I will relate this typicality element to the coding cost of maximally efficient neural representation, and describe its implications for learning and exploration. In the cognitive domain, we examine how attentional modulation affects face representation and perception. In the social domain, we examine how facial processing affects social perception and judgment, with implications for gender and racial biases. In the psychiatric domain, we examine how depression and anxiety in an individual interact with face perception and face-based decision making.
Previous Years Speakers
Fall 2022 Schedule
Winter 2021 Schedule
Fall 2020 Schedule
For more information about the Foundations & Frontiers Speaker Series, please contact: