The human mind is constantly engaged in a process of making sense of the external world by focusing on what is relevant from a myriad of sensory stimuli.

• How do people learn to organize thoughts in a way that supports adaptive behavior?

• How does such learning shape the affective response to different experiences?

• And can this process be leveraged to design effective mental health interventions?

To address these questions, we build computational models that can generate behavior in real-time, and test these models based on how well they match data from different modalities (e.g. choice, eye-tracking, virtual reality). We draw from reinforcement learning and Bayesian inference to uncover the algorithms that give rise to behavior in naturalistic environments. And we are collaborating with neuroscientists to understand how these algorithms are implemented in the brain.

Check out our publications to learn more.