# Single-trial modeling separates multiple overlapping prediction errors during reward processing in human EEG

Article · Colin W. Hoy, Sheila C. Steiner, Robert Thomas Knight

- Archive id: doi:10.1038/s42003-021-02426-1
- Published: 2021-07-23
- Venue: Communications Biology
- DOI: https://doi.org/10.1038/s42003-021-02426-1
- PMID: 34302057
- Citations: 54
- Source URL: https://doi.org/10.1038/s42003-021-02426-1
- PDF: https://www.nature.com/articles/s42003-021-02426-1.pdf
- Provenance: openalex

## Abstract

Learning signals during reinforcement learning and cognitive control rely on valenced reward prediction errors (RPEs) and non-valenced salience prediction errors (PEs) driven by surprise magnitude. A core debate in reward learning focuses on whether valenced and non-valenced PEs can be isolated in the human electroencephalogram (EEG). We combine behavioral modeling and single-trial EEG regression to disentangle sequential PEs in an interval timing task dissociating outcome valence, magnitude, and probability. Multiple regression across temporal, spatial, and frequency dimensions characterized a spatio-tempo-spectral cascade from early valenced RPE value to non-valenced RPE magnitude, followed by outcome probability indexed by a late frontal positivity. Separating negative and positive outcomes revealed the valenced RPE value effect is an artifact of overlap between two non-valenced RPE magnitude responses: frontal theta feedback-related negativity on losses and posterior delta reward positivity on wins. These results reconcile longstanding debates on the sequence of components representing reward and salience PEs in the human EEG.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI Alignment & Safety](https://space-kitty.com/topics/alignment-safety)

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