RT Journal Article
SR Electronic
T1 Testing the Reward Prediction Error Hypothesis with an Axiomatic Model
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 13525
OP 13536
DO 10.1523/JNEUROSCI.1747-10.2010
VO 30
IS 40
A1 Robb B. Rutledge
A1 Mark Dean
A1 Andrew Caplin
A1 Paul W. Glimcher
YR 2010
UL http://www.jneurosci.org/content/30/40/13525.abstract
AB Neuroimaging studies typically identify neural activity correlated with the predictions of highly parameterized models, like the many reward prediction error (RPE) models used to study reinforcement learning. Identified brain areas might encode RPEs or, alternatively, only have activity correlated with RPE model predictions. Here, we use an alternate axiomatic approach rooted in economic theory to formally test the entire class of RPE models on neural data. We show that measurements of human neural activity from the striatum, medial prefrontal cortex, amygdala, and posterior cingulate cortex satisfy necessary and sufficient conditions for the entire class of RPE models. However, activity measured from the anterior insula falsifies the axiomatic model, and therefore no RPE model can account for measured activity. Further analysis suggests the anterior insula might instead encode something related to the salience of an outcome. As cognitive neuroscience matures and models proliferate, formal approaches of this kind that assess entire model classes rather than specific model exemplars may take on increased significance.