Presented By: Weinberg Institute for Cognitive Science
Foundations and Frontiers Speaker Series
Computational Modeling of Human Face Processing
Presentation Abstract
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, including novel methods for combining image-computable machine vision algorithms with more classical psychological methods, such as multidimensional scaling, that take human similarity judgments into account.
In the Frontiers portion of the lecture, I will describe our work using a neurobiologically supported and psychologically informed face representation, to computational model and quantify human perception and judgment of faces, including a computational explanations of the provenance of certain racial and gender biases. Furthermore, we will examine some implications of these findings for the broader domains of psychiatry, social psychology, economics, and political science.
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Schedule
2:00-2:30 pm Foundations Presentation
2:30-2:40 pm Q & A
—10 minute break—
2:50-3:20 pm Frontiers Presentation
3:20-3:30 pm Q & A
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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.
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, including novel methods for combining image-computable machine vision algorithms with more classical psychological methods, such as multidimensional scaling, that take human similarity judgments into account.
In the Frontiers portion of the lecture, I will describe our work using a neurobiologically supported and psychologically informed face representation, to computational model and quantify human perception and judgment of faces, including a computational explanations of the provenance of certain racial and gender biases. Furthermore, we will examine some implications of these findings for the broader domains of psychiatry, social psychology, economics, and political science.
------
Schedule
2:00-2:30 pm Foundations Presentation
2:30-2:40 pm Q & A
—10 minute break—
2:50-3:20 pm Frontiers Presentation
3:20-3:30 pm Q & A
-----
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.
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