Testing the reward prediction error hypothesis with an axiomatic model

Robb B. Rutledge, Mark Dean, Andrew Caplin, Paul W. Glimcher

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)13525-13536
Number of pages12
JournalJournal of Neuroscience
Volume30
Issue number40
DOIs
StatePublished - Oct 6 2010

ASJC Scopus subject areas

  • Neuroscience(all)

Fingerprint Dive into the research topics of 'Testing the reward prediction error hypothesis with an axiomatic model'. Together they form a unique fingerprint.

Cite this