Format

Send to

Choose Destination
Neuroimage. 2016 Jan 1;124(Pt A):276-286. doi: 10.1016/j.neuroimage.2015.07.032. Epub 2015 Jul 18.

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

Author information

1
Cognition Institute, Department of Psychology, Plymouth University, Plymouth PL4 8AA, UK. Electronic address: tom.sambrook@plymouth.ac.uk.
2
Cognition Institute, Department of Psychology, Plymouth University, Plymouth PL4 8AA, UK.

Abstract

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 240-340ms 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 ~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.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center