Cerebral Cortex Advance Access originally published online on September 6, 2006
Cerebral Cortex 2007 17(7):1570-1581; doi:10.1093/cercor/bhl068
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Error-Likelihood Prediction in the Medial Frontal Cortex: A Critical Evaluation
1 Department of Psychology, 2 Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands, 3 Department of Psychology, Vrije Universiteit, Amsterdam, The Netherlands, 4 F.C. Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands, 5 Nijmegen Institute for Cognition and Information, Radboud Universiteit, Nijmegen, The Netherlands, 6 Department of Psychology, Department of Psychiatry, and Center for Cognitive Neuroscience, University of Pennsylvania, Philadelphia, PA, USA, 7 Department of Psychology, Stony Brook University, Stony Brook, NY, USA
Address correspondence to Sander Nieuwenhuis, Department of Psychology, Cognitive Psychology Unit, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands. Email: snieuwenhuis{at}fsw.leidenuniv.nl.
A recent study has proposed that posterior regions of the medial frontal cortex (pMFC) learn to predict the likelihood of errors occurring in a given task context. A key prediction of the error-likelihood (EL) hypothesis is that the pMFC should exhibit enhanced activity to cues that are predictive of high compared with low error rates. We conducted 3 experiments, 2 using functional neuroimaging and 1 using event-related potentials, to test this prediction in human volunteers. The 3 experiments replicated previous research in showing clear evidence of increased pMFC activity associated with errors, conflict, negative feedback, and other aspects of task performance. However, none of the experiments yielded evidence for an effect of cue-signaled EL on pMFC activity or any indication that such an effect developed with learning. We conclude that although the EL hypothesis presents an elegant integrative account of pMFC function, it requires additional empirical support to remain tenable.
Key Words: ACC • dopamine • error processing • fMRI • reinforcement learning • stop signal
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