Ccipital Deviance


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Occipital Deviance


Occipital Deviance

Author: Mark Keller

language: en

Publisher: Lulu.com

Release Date: 2003


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Seventeen stories to question your perception by; from the mind of a sixteen-year old. When you wake up in chains... When you're trapped in a sinking ship... When you're hearing voices from toys... When you're seeing people jumping from rooftops... Do think of me.

Multisensory Object Perception in the Primate Brain


Multisensory Object Perception in the Primate Brain

Author: Marcus Johannes Naumer

language: en

Publisher: Springer Science & Business Media

Release Date: 2010-07-03


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It should come as no surprise to those interested in sensory processes that its research history is among the longest and richest of the many systematic efforts to understand how our bodies function. The continuing obsession with sensory systems is as much a re?ection of the fundamental need to understand how we experience the physical world as it is to understand how we become who we are based on those very experiences. The senses function as both portal and teacher, and their individual and collective properties have fascinated scientists and philosophers for millennia. In this context, the attention directed toward specifying their properties on a sense-by-sense basis that dominated sensory research in the 20th century seems a prelude to our current preoccupation with how they function in concert. Nevertheless, it was the concentrated effort on the operational principles of in- vidual senses that provided the depth of understanding necessary to inform current efforts to reveal how they act cooperatively. We know that the information provided by any individual sensory modality is not always veridical, but is subject to a myriad of modality-speci?c distortions. Thus, the brain’s ability to compare across the senses and to integrate the information they provide is not only a way to examine the accuracy of any individual sensory channel but also a way to enhance the collective information they make available to the brain.

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


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

Author: Gabor Stefanics

language: en

Publisher: Frontiers Media SA

Release Date: 2015-06-04


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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 probabilistic expectations based on repeating experiences. In other words, our brain automatically represents statistical regularities of our visual environmental. 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. In this research topic we aim to present vMMN as a prediction error signal. Predictive coding theories account for phenomena such as mismatch negativity and repetition suppression, and place them in a broader context of a general theory of cortical responses. A wide range of vMMN studies has been presented 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. 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.