josh
Josh Tenenbaum

Professor
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology

Email: jbt AT mit DOT edu
Phone: 617-452-2010 (office), 617-253-8335 (fax)
Mail: Building 46-4015, 77 Massachusetts Avenue, Cambridge, MA 02139
Curriculum Vitae (as of January 2011)


Research interests

My colleagues and I in the Computational Cognitive Science group study one of the most basic and distinctively human aspects of cognition: the ability to learn so much about the world, rapidly and flexibly. Given just a few relevant experiences, even young children can infer the meaning of a new word, the hidden properties of an object or substance, or the existence of a new causal relation or social rule. These inferences go far beyond the data given: after seeing three or four examples of "horses", a two-year-old will confidently judge whether any new entity is a horse or not, and she will be mostly correct, except for the occasional donkey or camel.

We want to understand these everyday inductive leaps in computational terms. What is the underlying logic that supports reliable generalization from so little data? What are its cognitive and neural mechanisms, and how can we build more powerful learning machines based on the same principles?

These questions demand a multidisciplinary approach. Our group's research combines computational models (drawing chiefly on Bayesian statistics, probabilistic generative models, and probabilistic programming) with behavioral experiments in adults and children. Our models make strong quantitative predictions about behavior, but more importantly, they attempt to explain why cognition works, by viewing it as an approximation to ideal statistical inference given the structure of natural tasks and environments.

While our core interests are in human learning and reasoning, we also work actively in machine learning and artificial intelligence. These two programs are inseparable: bringing machine-learning algorithms closer to the capacities of human learning should lead to more powerful AI systems as well as more powerful theoretical paradigms for understanding human cognition.

Current research in our group explores the computational basis of many aspects of human cognition: learning concepts, judging similarity, inferring causal connections, forming perceptual representations, learning word meanings and syntactic principles in natural language, noticing coincidences and predicting the future, inferring the mental states of other people, and constructing intuitive theories of core domains, such as intuitive physics, psychology, biology, or social structure.

The Computational Cognitive Science Group

Special issue of Trends in Cognitive Science, July 2006 (Vol. 10, Issue 7), on "Probabilistic Models of Cognition".

Graduate Summer School in Probabilistic Models of Cognition, July 9-27, Institute for Pure and Applied Mathematics (UCLA).


Courses

Spring 2011: 9.915 Special Topics in Computational Cognitive Science: Probabilistic Programming
Fall 2009: 9.66/9.914/6.804 Computational Cognitive Science
Spring 2009: 9.012 Cognitive Science
Fall 2008: 9.916/Harvard Psych 2180 Computational Models and Cognitive Development
Winter 2006: 9.94 The Cognitive Science of Intuitive Theories


Representative reading and talks


Papers (listed chronologically - see below for listing by topic)

Most recent

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999 and before


Papers (by topic - this list is somewhat out of date)

Nonlinear dimensionality reduction

Separating style and content

Concept learning and generalization

Learning and Representing Word Meanings

Learning and similarity

Probabilistic reasoning

Causal learning and inference

Theory of mind


Lab mascots
Abigail "Avishka" Lea Tenenbaum