Visual Mismatch Negativity (vMMN): A Prediction Error Signal in the Visual Modality

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65

authors

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articles

Visual Mismatch Negativity (vMMN): A Prediction Error Signal in the Visual Modality

145.8K

views

65

authors

18

articles

Editorial

22 January 2015

Visual mismatch negativity (vMMN): a prediction error signal in the visual modality

Gábor Stefanics

,

Piia Astikainen

and

István Czigler

9,683 views

61 citations

Editors

3

Gabor Stefanics

University of Zurich

Piia Astikainen

University of Jyväskylä

István Czigler

Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hun-Ren

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Current theories of visual change detection emphasize the importance of conscious attention to detect unexpected changes in the visual environment. However, an increasing body of studies shows that the human brain is capable of detecting even small visual changes, especially if such changes violate non-conscious expectations based on repeating experiences. In other words, our brain automatically represents environmental regularities. Since the discovery of the mismatch negativity component, the majority of research in the field focused on auditory deviance detection, and its neural correlate, the MMN response. However, automatic predictive (i.e. change detection) mechanisms operate in the visual modality too. An indicator of the automatic change detection is the visual mismatch negativity (vMMN) component of the event-related potentials (ERPs). VMMN is typically elicited by stimuli with infrequent (deviant) features embedded in streams of identical (standard) stimuli. vMMN is elicited independently of attention by deviant color, orientation, movement, spatial frequency, contrast, and has shown its potential also as a clinically relevant brain measure. Abstract sequential regularities of visual stimuli and natural stimuli such as faces and hands have also been used to study automatic prediction error responses to unpredicted visual events. These studies confirmed that information about the content of both simple and more complex natural stimuli is rapidly processed and stored by the brain in the absence of conscious attention. vMMN, the visual counterpart of the auditory MMN comes to age. In this Research Topic, we aim to put vMMN in a broad context of prediction error signals and predictive brain mechanisms.

Current theories of visual change detection emphasize the importance of conscious attention to detect unexpected changes in the visual environment. However, an increasing body of studies shows that the human brain is capable of detecting even small visual changes, especially if such changes violate non-conscious expectations based on repeating experiences. In other words, our brain automatically represents environmental regularities. Since the discovery of the mismatch negativity component, the majority of research in the field focused on auditory deviance detection, and its neural correlate, the MMN response. However, automatic predictive (i.e. change detection) mechanisms operate in the visual modality too. An indicator of the automatic change detection is the visual mismatch negativity (vMMN) component of the event-related potentials (ERPs). VMMN is typically elicited by stimuli with infrequent (deviant) features embedded in streams of identical (standard) stimuli. vMMN is elicited independently of attention by deviant color, orientation, movement, spatial frequency, contrast, and has shown its potential also as a clinically relevant brain measure. Abstract sequential regularities of visual stimuli and natural stimuli such as faces and hands have also been used to study automatic prediction error responses to unpredicted visual events. These studies confirmed that information about the content of both simple and more complex natural stimuli is rapidly processed and stored by the brain in the absence of conscious attention. vMMN, the visual counterpart of the auditory MMN comes to age. In this Research Topic, we aim to put vMMN in a broad context of prediction error signals and predictive brain mechanisms.

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Editors

Gabor Stefanics

University of Zurich

Piia Astikainen

University of Jyväskylä

István Czigler

Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Hun-Ren

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145,782 Total views

103,786 Article views

31,055 Article downloads

10,941 Topic views

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Frontiers in Human Neuroscience

Brain Imaging and Stimulation, Cognitive Neuroscience, Brain Health and Clinical Neuroscience, Sensory Neuroscience, Speech and Language

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