CAREER: Connecting Human and Machine Learning through Probabilistic Models of Cognition

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

People are able to learn new concepts much faster than computers, often requiring only a handful of examples where a computer might require hundreds. This remarkable ability is partly the consequence of extensive experience with the world, resulting in strong prior knowledge about the kinds of objects that are likely to form categories. This research project bridges the gap between human and machine learning by developing probabilistic models of human category learning, connecting psychological data with the latest theories from computer science and statistics. These mathematical and computational models are used to explore how people learn categories so quickly, to capture the effects of prior knowledge on categorization, and to build a catalogue of human concepts that can be used to test psychological theories and to train machine learning systems. In each case, the research combines the ideas, methods, and sources of data used in psychology and computer science, using hierarchical Bayesian models and Markov chain Monte Carlo algorithms to model human cognition, laboratory experiments to test these models, and large databases as a source of statistical information that guides model predictions. This research program is integrated with an educational plan that incorporates undergraduate and graduate teaching and mentoring, development of a textbook on probabilistic models of cognition, tutorials and workshops aimed at increasing contact between the computer science and psychology communities, and outreach through talks and a website.