Towards naturalistic reinforcement learning in health and disease
Adaptive decision-making relies on our ability to organize experience into useful representations of the environment. This ability is critical in the real world: each person’s experience is dynamic and continuous, and no two situations we encounter are exactly the same. In this talk, I will first show that attention and memory contribute to inferring a set of features of the environment relevant for learning and decision-making (i.e. a “state representation”). I will then present results from ongoing work attempting to understand how such inference can take place in naturalistic environments. One line of work leverages virtual reality in combination with eye-tracking to study what features of naturalistic scenes guide goal-directed search. A second study examines the role of language in providing a prior for which features are relevant for decision-making. And a third thread focuses on how mood biases attention to different features of a decision. I will conclude with a discussion of the potential of naturalistic reinforcement learning as a model of mental health dynamics.