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The mesostriatal dopamine system is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventral striatum notably covarying with model-free prediction errors. However, latent learning and devaluation studies show that behavior also shows hallmarks of model-based planning, and the interaction between model-based and model-free values, prediction errors, and preferences is underexplored. We designed a multistep decision task in which model-based and model-free influences on human choice behavior could be distinguished. By showing that choices reflected both influences we could then test the purity of the ventral striatal BOLD signal as a model-free report. Contrary to expectations, the signal reflected both model-free and model-based predictions in proportions matching those that best explained choice behavior. These results challenge the notion of a separate model-free learner and suggest a more integrated computational architecture for high-level human decision-making.

Original publication

DOI

10.1016/j.neuron.2011.02.027

Type

Journal article

Journal

Neuron

Publication Date

03/2011

Volume

69

Pages

1204 - 1215

Addresses

Center for Neural Science and Department of Psychology, New York University, New York, NY 10012, USA. daw@cns.nyu.edu

Keywords

Basal Ganglia, Neurons, Humans, Dopamine, Magnetic Resonance Imaging, Brain Mapping, Logistic Models, Reinforcement (Psychology), Choice Behavior, Neuropsychological Tests, Models, Neurological, Adult, Female, Male