Research in our laboratory investigates how the human brain builds internal models of the world and uses them to guide complex decisions. These models capture how the environment is structured and how it changes, allowing us to go beyond stored stimulus–response associations, infer unobservable states, predict future events, and generalize from past experience.

 

We study this primarily through social decision-making, in which the brain must infer others’ beliefs, intentions, and strategies while learning the underlying structure of the environment. We also examine more fundamental questions about how constructing such internal models is constrained by the way the brain abstracts and organizes information into structured representations, and how these representations enable efficient, but sometimes biased, inference and generalization. 

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Our goal is to develop a quantitative, mechanistic understanding of how inferences and choices are made by reverse-engineering  human decisions recorded in controlled experiments. We use computational models of behavior to decompose decision-making processes into interacting component operations and test whether and how they are implemented in the brain. This research incorporates methods from social and cognitive neuroscience, game theory, machine learning, economics, and, where appropriate, social-network analysis and linguistics.

 

To date, we have used this interdisciplinary approach to pursue four complementary research themes:

(i) inferences in repeated decisions,

(ii) inferences in novel decisions,

(iii) computational and neurocognitive basis of abstract representations that shape inference, and

(iv) applying these neurocomputational insights to real-world problems.