Summary:
Recent developments in reinforcement learning, cognitive control, and systems neuroscience highlight the complimentary roles in learning of valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by the magnitude of surprise. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). Here, we combine behavioral modeling and single-trial EEG regression revealing a sequence of valenced and non-valenced PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions revealed a spatio-tempo-spectral cascade from valenced RPE value represented by the feedback related negativity event-related potential (ERP) followed by non-valenced RPE magnitude and outcome probability effects indexed by subsequent P300 and late frontal positivity ERPs. The results show that learning is supported by a sequence of multiple PEs evident in the human EEG.
Authors:
Colin W. Hoy
Sheila C. Steiner
Robert T. Knight
Date: 2020
DOI: https://doi.org/10.1101/2020.10.20.347740