homepage: | http://cocosci.berkeley.edu/tom/ |
search externally: | Google Scholar, Springer, CiteSeer, Microsoft Academic Search, Scirus , DBlife |
Description
I am interested in developing mathematical models of higher level cognition, and understanding the formal principles that underlie our ability to solve the computational problems we face in everyday life. My current focus is on inductive problems, such as probabilistic reasoning, learning causal relationships, acquiring and using language, and inferring the structure of categories. I try to analyze these aspects of human cognition by comparing human behavior to optimal or "rational" solutions to the underlying computational problems. For inductive problems, this usually means exploring how ideas from artificial intelligence, machine learning, and statistics (particularly Bayesian statistics) connect to human cognition. Some specific questions and representative publications appear on my departmental webpage. These interests sometimes lead me into other areas of research: I have recently been exploring some ideas in nonparametric Bayesian statistics and formal models of cultural evolution.
Lectures:
lecture Inferring structure from data as author at Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010, 9676 views |
lecture Monte Carlo and the mind as author at Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010, 7735 views |
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tutorial Cognitive science for machine learning 3: Models and theories in cognitive science as author at Cognitive Science and Machine Learning Summer School (MLSS), Sardinia 2010, 6036 views |