People solve challenging computational problems every day, making predictions about future events, learning new causal relationships, or discovering how objects should be divided into categories. My research investigates how this is possible, first identifying the nature of the underlying computational problems, and then examining whether we can explain aspects of human behavior as the result of approximating optimal solutions to those problems. Since many of the problems people face in everyday life are problems of induction, requiring inferences from limited data to underconstrained hypotheses, these optimal solutions draw on methods developed in statistics, machine learning, and artificial intelligence research. Exploring how these methods relate to human cognition provides connections between these fields and cognitive science, as well as a way to turn insights obtained from studying people into new formal techniques.
Griffiths, T. L. (2015). Manifesto for a new (computational) cognitive revolution. Cognition, 135, 21-23. View
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7, 217-229. View
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) How to grow a mind: Statistics, structure, and abstraction. Science, 331, 1279-1285. View
Griffiths, T. L., Kalish, M. L., & Lewandowsky, S. (2008). Theoretical and experimental evidence for the impact of inductive biases on cultural evolution. Philosophical Transactions of the Royal Society, 363,3503-3514. View