People learn more from their experiences than just how to behave in different situations. They also learn to organize experiences into internal representations that facilitate flexible behavior, in domains ranging from simple decision-making to goal-directed action in naturalistic, richly structured environments. In this talk, I will show that such representation learning relies on selective attention to constrain the dimensionality of environments that humans learn from. Computational modeling revealed that attention is in turn guided by memory-augmented probabilistic inference over what features of the environment are relevant for the task at hand. Moreover, how people deploy selective attention in service of representation learning is an important source of variation in human decision-making. In the final part of the talk, I will discuss ongoing work leveraging virtual reality in combination with eye-tracking to study representation learning in naturalistic settings.

Zoom link: https://berkeley.zoom.us/j/92628652633