Tania Lombrozo

Tania Lombrozo is the Arthur W. Marks ’19 Professor of Psychology at Princeton University. Her research investigates the human mind, asking how and why we think and believe the way we do. As a cognitive scientist, she takes an interdisciplinary approach, tackling these big questions with the tools of both psychology and philosophy.

At Princeton, Dr. Lombrozo directs the Concepts & Cognition Lab, where one line of research focuses on why children and adults are so driven to explain the social and physical world around them. What drives our curiosity? When and how does it make us better learners, and how might it lead us astray? Another line of research focuses on the nature of belief: Why do people believe what they do? Are people ever justified in forming beliefs that go beyond the evidence? Are scientific beliefs and religious beliefs merely beliefs about different topics, or do they differ in more fundamental ways? How do people understand the relationship between science and religion?

Prof. Lombrozo is the recipient of numerous honors for her scientific contributions, including awards from the American Psychological Association, the Association for Psychological Science, and the National Science Foundation, among others. She holds a B.S. in Symbolic Systems and a B.A. in Philosophy from Stanford University, as well as a Ph.D. in Psychology from Harvard University. Before joining the faculty at Princeton, she was a professor of psychology at the University of California, Berkeley.

Tom Griffiths

Tom Griffiths is the Henry R. Luce Professor of Information Technology, Consciousness and Culture in the Departments of Psychology and Computer Science at Princeton University. His research explores connections between human and machine learning, using ideas from statistics and artificial intelligence to understand how people solve the challenging computational problems they encounter in everyday life.

Tom completed his PhD in Psychology at Stanford University in 2005, and taught at Brown University and the University of California, Berkeley before moving to Princeton. He has made contributions to both machine learning and cognitive science across a wide range of topics, including how computers use language and make inferences about human behavior, how people learn causal relationships and concepts, the similarities and differences between human and machine learning, and predicting and understanding human decisions.

His current work is focused on improving our definition of rational decision-making and on the ways that big data is transforming behavioral science.  He has received awards for his research from organizations including the American Psychological Association, the National Academy of Sciences, and the Guggenheim Foundation, and is co-author of the book Algorithms to Live By, introducing ideas from computer science and cognitive science to a general audience.