State-based versus reward-based motivation in younger and older adults
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 article, 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 did 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.
KeywordsAging Computational modeling Decision-making Motivation Reward
This research was funded by NIA grant AG043425 to D.A.W. and W.T.M. and NIDA grant DA032457 to W.T.M. We thank Anna Anthony for help with data collection. Correspondence should be addressed to W. Todd Maddox (Maddox@psy.utexas.edu) or Darrell A. Worthy (firstname.lastname@example.org).
- Eppinger, B., Walter, M., Heekeren, H. R., & Li, S.-C. (2013). Of goals and habits: age-related and individual differences in goal-directed decision-making. Frontiers in Neuroscience, 7.Google Scholar
- Fridlund, A., & Delis, D. C. (1987). CVLT research edition administration and scoring software. New York: The Psychological Corporation.Google Scholar
- Gershman, S. J., Markman, A. B., & Otto, A. R. (2012). Retrospective revaluation in sequential decision making: A tale of two systems.Google Scholar
- Heaton, R. K. (1981). A manual for the Wisconsin card sorting test: Western Psycological Services.Google Scholar
- Lezak, M. (1995). Neuropsychological testing. Oxford: University Press.Google Scholar
- Maddox, W. T., Gorlick, M. A., Worthy, D. A., & Beevers, C. G. (2012). Depressive symptoms enhance loss-minimization, but attenuate gain-maximization in history-dependent decision-making. Cognition.Google Scholar
- Schwartz, B. (2009). The paradox of choice: HarperCollins.Google Scholar
- Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction (Vol. 1): Cambridge Univ Press.Google Scholar
- Wechsler, D. (1997). WAIS-III, Wechsler Adult Intelligence Scale: Administration and Scoring Manual: Psychological Corporation.Google Scholar
- Worthy, D. A., & Maddox, W. T. (2012). Age-based differences in strategy use in choice tasks. Frontiers in Neuroscience, 5.Google Scholar