Abstract
SummaryHuman choice behavior often reflects a competition between inflexible computationally efficient control on the one hand and a slower more flexible system of control on the other. This distinction is well captured by model-free and model-based reinforcement learning algorithms. Here, studying human subjects, we show it is possible to shift the balance of control between these systems by disruption of right dorsolateral prefrontal cortex, such that participants manifest a dominance of the less optimal model-free control. In contrast, disruption of left dorsolateral prefrontal cortex impaired model-based performance only in those participants with low working memory capacity.
Highlights
Why is our behavior at times automatic and driven by habit and at other times deliberative and focused on a specific goal? most of us seamlessly switch between these modes of behavior, it has been suggested that a relative dominance of either habit-like or goal-directed modes of behavior underpin a range of disorders that span addictions (Everitt and Robbins, 2005) through to Parkinson’s disease (de Wit et al, 2011)
Human choice behavior often reflects a competition between inflexible computationally efficient control on the one hand and a slower more flexible system of control on the other. This distinction is well captured by model-free and model-based reinforcement learning algorithms
Studying human subjects, we show it is possible to shift the balance of control between these systems by disruption of right dorsolateral prefrontal cortex, such that participants manifest a dominance of the less optimal model-free control
Summary
Most of us seamlessly switch between these modes of behavior, it has been suggested that a relative dominance of either habit-like or goal-directed modes of behavior underpin a range of disorders that span addictions (Everitt and Robbins, 2005) through to Parkinson’s disease (de Wit et al, 2011). This renders understanding the parsing of control between these two modes of decision making a pressing issue. Model-based control, by contrast, dynamically computes optimal actions by forward planning, a process that is computationally demanding but allows for flexible, outcome-specific behavioral repertoires (Daw et al, 2005; Dayan and Niv, 2008; Otto et al, 2013; but see Gershman et al, 2012)
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