RI: Small: CompCog: Leveraging Deep Neural Networks for Understanding Human Cognition

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The last few years have seen significant breakthroughs in artificial intelligence and machine learning, resulting in systems that approach or even exceed human performance in interpreting pictures and words. This project explores the implications of these breakthroughs for understanding how the human mind works. Focusing on artificial neural networks, a key technology behind many recent breakthroughs that is capable of discovering novel representations for complex stimuli, the project has two goals. First, assessing the degree of correspondence between human and machine learning by examining whether the pictures or words that are similar in the representations discovered by neural network models are also judged to be similar by people. Second, developing methods for increasing this correspondence, with the goal of being able to use neural network representations to generate good predictions about how people learn and form categories using real images or text.This research project will answer basic scientific questions about how the representations discovered by contemporary neural networks relate to human cognition. It will then explore what architectures and training regimes produce representations with these properties. In addition, the project will address the methodological question of how one can modify these representations to produce better alignment with human cognition. Answering this question will lead to powerful new tools for making models of human behavior in naturalistic contexts, leveraging the latest results in machine learning to broaden the scope of experimental research in cognitive science. By building stronger links between human and machine learning, this project will have implications for both fields. Even if current neural network systems turn out to differ significantly from human learning, they provide state-of-the-art representations for images and text that can be used as a starting point for developing better accounts of human representations. By discovering the ways in which the representations learned by artificial neural networks differ from those of humans, one can identify new algorithms and training methods that will result in a closer alignment. Since human beings remain the best examples available of systems that can solve certain problems, such an alignment offers a path toward expanding the capacities of current artificial intelligence systems and making them more interpretable by people, which is critical in settings that require human-machine interaction.