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Front Hum Neurosci. 2014 Sep 16;8:666. doi: 10.3389/fnhum.2014.00666. eCollection 2014.

Visual mismatch negativity: a predictive coding view.

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Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich ETH Zurich, Zurich, Switzerland ; Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich Zurich, Switzerland.
Department of Pathological Physiology, Faculty of Medicine in Hradec Králové, Charles University in Prague Hradec Králové, Czech Republic.
Research Center for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences Budapest, Hungary.


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.


EEG; ERP; perceptual learning; prediction error; predictive coding; repetition suppression; stimulus specific adaptation; visual mismatch negativity

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