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

NeuroImage
Thomas D SambrookJeremy Goslin

Abstract

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 a...Continue Reading

Citations

Aug 20, 2020·Cognitive, Affective & Behavioral Neuroscience·Franz WurmMarco Steinhauser
Aug 28, 2020·The Behavioral and Brain Sciences·George Ainslie
May 24, 2021·Cerebral Cortex·Harry J Stewardson, Thomas D Sambrook
May 27, 2021·Journal of Neurophysiology·Dimitrios J PalidisPaul L Gribble
Oct 9, 2021·Scientific Reports·Harry J Stewardson, Thomas D Sambrook

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