Tom Griffiths, Lab Director

(webpage)

Frederick Eberhardt

My research focuses on formal methods of causal learning using experimental interventions. This work builds on results and insights from philosophical accounts of causation, the theory of graphical models and experimental design, and decision theory. More generally, I am interested in the connection between probability and causation, and learning and decision theory. I have also done some historical work on the philosopher Hans Reichenbach. For more details, please see my homepage.

Florencia Reali

(webpage) My research combines behavioral experiments and probabilistic models to study various aspects of language learning and processing. I am also interested in exploring some theoretical aspects of language evolution, including the interaction between cultural transmission, biological adaptation and individual learning.

Joe Austerweil

(webpage) My research explores the interconnection between human and statistical solutions to inductive problems. In particular, I am interested in using statistical models to garner insight to how the human mind solves problems that plague philosophers and computer scientists. By looking at the assumptions behind these computational models, we better understand the prior assumptions people use to make surprisingly accurate inferences in the underconstrained problems of everyday life. Additionally, I explore infusing these assumptions into state-of-the-art machine learning techniques. I hope to improve their performance on everyday tasks (where people, with surprisingly less data, easily outperform them).

Kevin Canini

(webpage) I'm interested in statistical machine learning and (probabilistic) models of human cognition. My long-term goal is to build intelligent computer programs that are inspired by human neurobiology and psychology.

Chris Lucas

I'm interested in how and to what extent the abstract knowledge that constrains human induction is acquired. My current research focuses on causal induction, but I'm also interested in categorization, language, and the neural machinery behind human induction.

Lei Shi

I'm interested in using Bayesian methods to model human cognitive processes. My current project is making connections between exemplar models and Monte Carlo sampling methods. When I have time, I also wonder how Bayes' rule is implemented in the brain.

Joseph Jay Williams

I'm interested in causal reasoning, and explanation, from the perspective of computational modelling, verbal theories, and empirical research. For example, I'm curious about what knowledge people rely on to draw causal inferences, why people have a tendency to infer causal structure when it's not there, and the mechanisms by which seeking and generating explanations allow us to learn. I'm also interested in machine learning and artificial intelligence, and I like thinking about how what we know in cognitive science research can give us interesting insight into the activities that people naturally engage in on a day to day basis.

Jing Xu

I'm interested in understanding the connection between people's lower level behavior, such as movement control, and people's higher level cognition, such as cognitive switching, category learning and memory. Particularly, at this interface, how the prior knowledge/constraints in people's mind influence people's behavior. I'm using computational and mathematical approaches to model behavioral and neural empirical data.

Naomi Feldman

My interests are in speech perception and language acquisition. I'm using computational and behavioral methods to look at how people organize speech sounds into categories and how those categories affect their ability to perceive differences between sounds.

Sharon Goldwater

My research interests include language acquisition, computational linguistics, phonology, and morphology.

Adam Sanborn

I am interested in how perceptual categories are built and structured and how people use these categories to make decisions. Using Bayesian methods and behavioral experiments, I am developing methods for efficiently learning about natural categories, as well as exploring rational models of categorization and intuitive dynamics.

Frank Wood

My research effort is directed towards both contributing models and algorithms to the field of statistical machine learning and figuring out how the brain works.