Boosting model-based control in older adults

Abstract

<p>The tension between habits and plans is reflected in everyday decision-making. Habits are computationally cheap, but fail to flexibly adapt to changes in the environment. Planning is a flexible decision-making strategy, but requires greater resources. Arbitration between habits and plans has been formalized using reinforcement learning algorithms that distinguish between model-free control (habits) and model-based control (plans). Evidence about these two decision-making approaches suggests model-based control follows a developmental trajectory, emerging during adolescence, strengthening during young adulthood, and declining in older adulthood. The normative decline in planning (model-based control) presents the opportunity to develop interventions to increase flexible decision-making. Therefore, we asked if incentives could be used to increase model-based control in older adults. We expected older adults would fail to increase model-based control in response to incentives. This prediction was based upon prior research suggesting older adulthood is associated with deficits in representing and updating the expected value of rewards. Contrary to our expectations, in Experiment 1 we found that incentives could be used to boost model-based control in older adults sampled from an online population. We hypothesized this may be due to previous experience with the task (or with similar tasks). In Experiment 2, a naïve sample of older adults did not boost model-based control in response to incentives. These results suggest that incentives may be a useful intervention to increase model-based planning in older adulthood, but this may require extensive experience with the incentive structure.</p>

Cognitive resource limitations shift effort trade-offs across the lifespan

“…In this study, we argue that this balancing was reflected by sensitivity to the opportunity cost of time, whereby older adults engaged in cognitive effort when it was most rewarding when the average reward rate was high. In this way, our findings join a spate of recent work that suggests that people can adaptively change behaviour to overcome age-related cognitive limitations if properly incentivised to do so (Ferdinand & Czernochowski, 2018;Yee et al, 2019;Patzelt et al, 2019;Harsay et al, 2010;Gatzke-Kopp, 2018;Chevalier, 2015;Niebaum et al, 2019;Fischer et al, 2018). We extend this work by suggesting a potential computational mechanism that older adults (and possibly children) use to strategically adapt to cognitive control demands in their environment: the opportunity cost of time.…”

Resource‐rational approach to meta‐control problems across the lifespan

“…Although their responses slow down, they seem to do so to maintain accuracy, allowing them to engage in more cognitive control (Starns & Ratcliff, 2010; Yee et al, 2019). In contrast to cognitive control, motivational incentives do not seem to lead to increased MB decision‐making in older adults, even when engaging in this more complex strategy leads to better task outcomes (Bolenz et al, 2019; Patzelt et al, 2019). Thus, it seems that the reduced MB learning seen in older adults cannot be explained by a reduced willingness to engage in this strategy, but rather is due to age‐related limitations in the cognitive processes involved in MB decision‐making.…”