Validating Habitual and Goal-Directed Decision-Making Performance Online in Healthy Older Adults

Abstract

Everyday decision-making is supported by a dual-system of control comprised of parallel goal-directed and habitual systems. Over the past decade, the two-stage Markov decision task has become popularized for its ability to dissociate between goal-directed and habitual decision-making. While a handful of studies have implemented decision-making tasks online, only one study has validated the task by comparing in-person and web-based performance on the two-stage task in children and young adults. To date, no study has validated the dissociation of goal-directed and habitual behaviors in older adults online. Here, we implemented and validated a web-based version of the two-stage Markov task using parameter simulation and recovery and compared behavioral results from online and in-person participation on the two-stage task in both young and healthy older adults. We found no differences in estimated free parameters between online and in-person participation on the two-stage task. Further, we replicate previous findings that young adults are more goal-directed than older adults both in-person and online. Overall, this work demonstrates that the implementation and use of the two-stage Markov decision task for remote participation is feasible in the older adult demographic, which would allow for the study of decision-making with larger and more diverse samples.

Keywords: aging; decision-making; goal-directed; habitual; older adults; online; reinforcement learning; validating.

FIGURE 1

Two-stage Markov Decision Task. Participants were given a choice between one of two start states on each trial, a forest and a desert. One location more commonly (70%) led to one of the second-stage states (the blue and purple cartoons), and rarely (30%) led to the other. Each second-stage state was associated with slowly changing reward probabilities.

FIGURE 2

Stay-switch plots of simulated behavior. Graphs depicting purely model-based (goal-directed), purely model-free (habitual), and hybrid behaviors. Purely model-based behavior is predicted by an interaction between reward and transition, whereas purely model-free behavior is predicted solely by reinforcement history. Hybrid behavior represents a mix of model-based and model-free behavior.

FIGURE 3

Stay-switch plots of behavior. Both in-person (n = 12) and online (n = 30) young adult groups exhibited hybrid behavior, that is, a mix of model-based and model-free behavior. In both in-person (n = 11) and online (n = 30) older adult groups, behavior was more characteristic of habitual performance, with a strong effect of reward but an attenuated effect of transition.

FIGURE 4

Estimated free parameters by group. We performed 2-way ANOVAs assessing main effects of age group (young adults vs. older adults) and participation medium (in-person vs. online) for each parameter. There were no main effects of participation medium, but we found main effects of age for weight (w) and β. Bolded lines represent significant main effects, brackets represent significant pairwise comparisons (**p < 0.001, *p < 0.05).