Recent decision-making work has focused on a distinction between a habitual, model-free neural system that is motivated toward actions that lead directly to reward, and a more computationally demanding goal-directed, model-based system that is motivated toward actions that improve one’s future state. In this paper we examine how aging affects motivation toward reward-based versus state-based decision-making. Participants performed tasks in which one type of option provided larger immediate rewards, but the alternative type of option led to larger rewards on future trials, or improvements in state. We predicted that older adults would show a reduced preference for choices that led to improvements in state and a greater preference for choices that maximized immediate reward. We also predicted that fits from a HYBRID reinforcement-learning model would indicate greater model-based strategy use in younger than in older adults. In line with these predictions, older adults selected the options that maximized reward more often than younger adults in three of the four tasks and modeling results suggested reduced model-based strategy-use. In the task where older adults showed similar behavior to younger adults our model-fitting results suggested that this was due to the utilization of a win-stay-lose-shift heuristic rather than a more complex model-based strategy. Additionally, within older adults we found that model-based strategy use was positively correlated with memory measures from our neuropsychological test battery. We suggest that this shift from state-based to reward-based motivation may be due to age related declines in the neural structures needed for more computationally demanding model-based decision-making.
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