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The Effect of Incentives on Contributions from Model-based Learning Strategies: Individual Differences in Cognitive Traits and Neural Circuitry

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Decision making is an essential and indispensable element in everyday life. It is posited that parallel, distinct systems subserving deliberative, goal-directed control and automatic, habitual control underlie decision making. Computational accounts suggest that model-based and model-free learning strategies give rise to these two systems respectively. The model-based system is a prospective system that is capable of forming an internal model of the states and transitions in the environment and utilizes this internal model to direct future actions. In contrast, the model-free system is a retrospective system that learns action values through trial-and-error interactions with the environment; it lacks an internal model and relies on cached values from experience to direct future actions. There is considerable individual variation in the balance between model-based and model-free learning. A growing body of research is aiming to elucidate the neural substrates of these two systems and physiological and cognitive factors that influence the balance between them. Incentive motivation can affect various forms of cognitive processes and performance. However, much remains to be studied about the effect of reward incentives on the arbitration between model-based and model-free learning, as well as individual differences in neural signatures and cognitive traits that moderate the effect of reward on the balance between these two types of control strategies. Here, I present results from two studies that aim to address these questions with computational modeling, neuropsychological tests and neuroimaging, using a well-validated two-stage decision making task that assessed the balance between model-based and model-free systems. In Study 1, results showed that across all participants, the prospect of higher reward increased the balance towards model-based learning, and decreased exploration of alternative options during the decision-making task. Individual differences in processing speed affected the effect of reward in changing the influence from the model-free system. In Study 2, results replicated the finding that higher reward cues decreased exploratory choices. However, in contrast to Study 1, higher reward cues did not exert influence on the balance between model-based and model-free strategies. Analytical thinking style was associated with less dependence on model-free learning when facing high reward compared to low reward cues. Resting-state functional connectivity between frontal, temporal and striatal regions inform how different individuals may alter decision strategies in response to reward. Specifically, resting-state fMRI found that the effect of reward on decreasing exploratory decisions was associated with functional connectivity within left dorsolateral prefrontal cortex (dlPFC), between the right orbitofrontal cortex (OFC) and frontotemporal regions, as well as between frontal and striatal areas. The reward-induced change in the degree of model-based control was associated with the functional connectivity between the right dlPFC and right middle temporal gyrus (MTG). Finally, VBM analysis found a marginally significant relationship between the effect of reward on model-based control and gray matter volume in the right anterior insula. The studies provide evidence to show the role of incentive motivation in model-based learning and shed light on the individual differences in neural circuitry and cognitive dispositions that influence how they arbitrate between the two learning strategies in response to different reward incentives.

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