Princeton Neuroscience Institute and Department of Psychology
While much is known about reinforcement learning in simple scenarios in which a single stimulus is associated with a rewarding outcome, less is understood about how humans and animals learn in multidimensional settings in which stimulus attributes relevant for reward are not known in advance. In this talk, I will present data suggesting that reinforcement learning in complex environments relies on selective attention to uncover those aspects of the environment that are predictive of reward. I will then show how the interaction of reinforcement learning and attention changes with aging. In a multidimensional choice task, behavior of both young and older adults was explained well by a reinforcement learning model that uses selective attention to constrain learning. However, the model suggested that older adults restricted their learning to fewer features, employing more focused attention than younger adults. Furthermore, this difference in strategy predicted age-related deficits in accuracy. I will discuss these results suggesting that a narrower filter of attention may reflect an adaptation to the reduced capabilities of the reinforcement learning system.