Model-free and model-based reward prediction errors in EEG

Tom D. Sambrook, Ben Hardwick, Andy J. Wills, Jeremy Goslin

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
22 Downloads (Pure)


Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world’s structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain.
Original languageEnglish
Pages (from-to)162-171
Number of pages10
Early online date24 May 2018
Publication statusPublished - Sep 2018

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