Computional interactions between model-free and model-based decision making in the human brain

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A basic tenet of adaptive behavior is that humans and animals seek to maximize reward and minimize punishment. It has been conjectured that the brain employs two complementary computational strategies to solve this task: a model-free repetition of previously successful behavior, and a more demanding model‐based approach to assess which future actions will lead to the most highly valued outcomes. Consistent with these strategies, neuroimaging studies during decision-making have revealed evidence for distinct value signals pertaining to planning and choice after extensive training in the brain. However, it remains to be seen whether both systems act in isolation or interact in learning and choice. Furthermore, the factors governing which system controls behavior are still unclear. In this proposal we outline a comprehensive series of investigations in humans to study interactions between model-free and model-based choice systems, and to uncover signals in the brain corresponding to the underlying computational processes. Specifically, we address the question of information transfer between systems, such as in accessing learnt cached values by a planning system or a model‐based teaching signal to instruct cached values. We will then investigate the factors and neural mechanisms that determine whether model‐free or model-based systems influence choice and to what degree. Particularly, we will study a putative role of relative certainties in both controllers’ predictions for behavioral control, and the role of working memory performance in both systems. Finally, we will explore the role of decision making in psychopathology by comparing model-free and model‐based choice performance and underlying neural systems between depressed subjects and healthy controls. We expect that the findings will greatly enhance our knowledge about how the human brain implements the computations underlying decision-making and may also help us understand and characterize decision-making deficits in psychiatric patients.