Principal components analysis of reward prediction errors in a reinforcement learning task

Thomas D. Sambrook, Jeremy Goslin

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)
28 Downloads (Pure)


Models of reinforcement learning represent reward and punishment in terms of reward prediction errors (RPEs), quantitative signed terms describing the degree to which outcomes are better than expected (positive RPEs) or worse (negative RPEs). An electrophysiological component known as feedback related negativity (FRN) occurs at frontocentral sites 240-340 ms after feedback on whether a reward or punishment is obtained, and has been claimed to neurally encode an RPE. An outstanding question however, is whether the FRN is sensitive to the size of both positive RPEs and negative RPEs. Previous attempts to answer this question have examined the simple effects of RPE size for positive RPEs and negative RPEs separately. However, this methodology can be compromised by overlap from components coding for unsigned prediction error size, or "salience", which are sensitive to the absolute size of a prediction error but not its valence. In our study, positive and negative RPEs were parametrically modulated using both reward likelihood and magnitude, with principal components analysis used to separate out overlying components. This revealed a single RPE encoding component responsive to the size of positive RPEs, peaking at similar to 330ms, and occupying the delta frequency band. Other components responsive to unsigned prediction error size were shown, but no component sensitive to negative RPE size was found. (C) 2015 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)276-286
Number of pages11
Issue numberPart A
Early online date18 Jul 2015
Publication statusPublished - 1 Jan 2016

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