Model-based influences on humans' choices and striatal prediction errors

Nathaniel D. Daw, Samuel J. Gershman, Ben Seymour, Peter Dayan, Raymond J. Dolan

Research output: Contribution to journalArticlepeer-review

683 Scopus citations

Abstract

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 languageEnglish (US)
Pages (from-to)1204-1215
Number of pages12
JournalNeuron
Volume69
Issue number6
DOIs
StatePublished - Mar 24 2011

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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