Abstract
In this study we investigated age-related and individual differences in habitual (model-free) and goal-directed (model-based) decision-making. Specifically, we were interested in three questions. First, does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms. To affect model-based behavior in this task we manipulated the distinctiveness of reward probabilities of choice options. The results show age-related deficits in model-based decision-making, which are particularly pronounced if unexpected reward indicates the need for a shift in decision strategy. In this situation younger adults explore the task structure, whereas older adults show perseverative behavior. Consistent with previous findings, these results indicate that older adults have deficits in the representation and updating of expected reward value. We also observed substantial individual differences in model-based behavior. In younger adults high WM capacity is associated with greater model-based behavior and this effect is further elevated when reward probabilities are more distinct. However, in older adults we found no effect of WM capacity. Moreover, age differences in model-based behavior remained statistically significant, even after controlling for WM capacity. Thus, factors other than decline in WM, such as deficits in the in the integration of expected reward value into strategic decisions may contribute to the observed impairments in model-based behavior in older adults.
Highlights
Many simple everyday decision-making tasks, such as which cereals to take for breakfast or which subway to take to work in the morning, can be solved via habitual decision mechanisms
Does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms
We had three specific research questions in mind: first, does aging affect the balance between habitual and goal-directed decision mechanisms? Second, are age differences in the interplay of these decision mechanisms related to age differences in working memory (WM) capacity? Third, can model-based choice behavior be affected by manipulating the distinctiveness of the reward value of different choice options? To address these questions we adapted a two-state Markov decision task (Daw et al, 2011; Wunderlich et al, 2012) in combination with computational reinforcement learning (RL) modeling
Summary
Many simple everyday decision-making tasks, such as which cereals to take for breakfast or which subway to take to work in the morning, can be solved via habitual decision mechanisms. In the current study we examined age and individual differences in the interplay between habitual and goal-directed decision-making. We had three specific research questions in mind: first, does aging affect the balance between habitual and goal-directed decision mechanisms? The dissociation between habitual and goal-directed mechanisms is at the core of many current theories of learning and decision-making (Daw et al, 2005; Balleine and O’Doherty, 2010; Kahneman, 2011). Habitual or model-free learning refers to the acquisition of behavior based on associations between actions and effects: actions that are followed by reward are more likely to reoccur (Thorndike, 1911). Model-free learning is a robust and computationally efficient mechanism It can come at the cost of being inflexible, especially in dynamically changing environments, which constrain the adaptive value of habitual responses (Doll et al, 2012). Results from electrophysiological studies in animals and neuroimaging work in humans show that these reward predictions errors seem to be coded in phasic changes of dopaminergic activity in the midbrain and ventral striatum (Schultz et al, 1997; Montague et al, 2004; D’Ardenne et al, 2008; Niv et al, 2012)
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