Our visual field contains much more information at every moment than we can attend and consciously process. How is the multitude of unattended events processed in the brain and selected for the further attentive evaluation? Current theories of visual change detection emphasize the importance of conscious attention to detect changes in the visual environment. However, an increasing body of studies shows that the human brain is capable of detecting even small visual changes if such changes violate non-conscious probabilistic expectations based on prior experiences. In other words, our brain automatically represents environmental statistical regularities.Since the discovery of the auditory mismatch negativity (MMN) event-related potential (ERP) component, the majority of research in the field has focused on auditory deviance detection. Such automatic change detection mechanisms operate in the visual modality too, as indicated by the visual mismatch negativity (vMMN) brain potential to rare changes. vMMN is typically elicited by stimuli with infrequent (deviant) features embedded in a stream of frequent (standard) stimuli, outside the focus of attention. Information about both simple and more complex characteristics of stimuli is rapidly processed and stored by the brain in the absence of conscious attention.In this research topic we aim to present vMMN as a prediction error signal and put it in context of the hierarchical predictive coding framework. Predictive coding theories account for phenomena such as MMN and repetition suppression, and place them in a broader context of a general theory of cortical responses (Friston, 2005(Friston, , 2010. Each paper in this Research Topic is a valuable contribution to the field of automatic visual change detection and deepens our understanding of the short term plasticity underlying predictive processes of visual perceptual learning.A wide range of vMMN studies has been presented in seventeen articles in this Research Topic. Twelve articles address roughly four general sub-themes including attention, language, face processing, and psychiatric disorders. Additionally, four articles focused on particular subjects such as the oblique effect, object formation, and development and time-frequency analysis of vMMN. Furthermore, a review paper presented vMMN in a hierarchical predictive coding framework.Four articles investigated the relationship between attention and vMMN. Kremláček et al. (2013) presented subjects with radial motion stimuli in the periphery of the visual field using an oddball paradigm and manipulated the attentional load by varying the difficulty of a central distractor tasks. They aimed to manipulate the amount of available attentional resources that might have been involuntarily captured by the vMMN-evoking stimuli presented in the periphery outside of the attentional focus. The distractor task had three difficulty levels: (1) a central fixation (easy), and a target number detection task with (2) one target number (moderate), and (3) three target numbers (diffi...