%A Stefanics,Gábor %A Kremláček,Jan %A Czigler,István %D 2014 %J Frontiers in Human Neuroscience %C %F %G English %K predictive coding,EEG,ERP,visual mismatch negativity,vMMN,Prediction error,repetition suppression,stimulus specific adaptation,refractoriness,Attention,Perceptual Learning,Bayesian Brain %Q %R 10.3389/fnhum.2014.00666 %W %L %M %P %7 %8 2014-September-16 %9 Review %+ Dr Gábor Stefanics,Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich,Zurich, Switzerland,stefanics@biomed.ee.ethz.ch %+ Dr Gábor Stefanics,Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich,ETH Zurich, Zurich, Switzerland,stefanics@biomed.ee.ethz.ch %# %! Measuring and interpreting visual mismatch negativity: A predictive coding view %* %< %T Visual mismatch negativity: a predictive coding view %U https://www.frontiersin.org/articles/10.3389/fnhum.2014.00666 %V 8 %0 JOURNAL ARTICLE %@ 1662-5161 %X An increasing number of studies investigate the visual mismatch negativity (vMMN) or use the vMMN as a tool to probe various aspects of human cognition. This paper reviews the theoretical underpinnings of vMMN in the light of methodological considerations and provides recommendations for measuring and interpreting the vMMN. The following key issues are discussed from the experimentalist's point of view in a predictive coding framework: (1) experimental protocols and procedures to control “refractoriness” effects; (2) methods to control attention; (3) vMMN and veridical perception.