scholarly journals Cortical Dynamics of Perceptual Grouping and Segmentation: Crowding

2016 ◽  
Vol 16 (12) ◽  
pp. 1114 ◽  
Author(s):  
Gregory Francis ◽  
Mauro Manassi ◽  
Michael Herzog
2017 ◽  
Vol 17 (10) ◽  
pp. 366
Author(s):  
Gregory Francis ◽  
Alban Bornet ◽  
Adrien Doerig ◽  
Michael Herzog

2020 ◽  
Author(s):  
Daniel Herrera-Esposito ◽  
Ruben Coen-Cagli ◽  
Leonel Gomez-Sena

AbstractPeripheral vision comprises most of our visual field, and is essential in guiding visual behavior. Its characteristic capabilities and limitations, which distinguish it from foveal vision, have been explained by the most influential theory of peripheral vision as the product of representing the visual input using summary-statistics. Despite its success, this account may provide a limited understanding of peripheral vision, because it neglects processes of perceptual grouping and segmentation. To test this hypothesis, we studied how contextual modulation, namely the modulation of the perception of a stimulus by its surrounds, interacts with segmentation in human peripheral vision. We used naturalistic textures, which are directly related to summary-statistics representations. We show that segmentation cues affect contextual modulation, and that this is not captured by our implementation of the summary-statistics model. We then characterize the effects of different texture statistics on contextual modulation, providing guidance for extending the model, as well as for probing neural mechanisms of peripheral vision.


Author(s):  
Riitta Salmelin ◽  
Jan Kujala ◽  
Mia Liljeström

When seeking to uncover the brain correlates of language processing, timing and location are of the essence. Magnetoencephalography (MEG) offers them both, with the highest sensitivity to cortical activity. MEG has shown its worth in revealing cortical dynamics of reading, speech perception, and speech production in adults and children, in unimpaired language processing as well as developmental and acquired language disorders. The MEG signals, once recorded, provide an extensive selection of measures for examination of neural processing. Like all other neuroimaging tools, MEG has its own strengths and limitations of which the user should be aware in order to make the best possible use of this powerful method and to generate meaningful and reliable scientific data. This chapter reviews MEG methodology and how MEG has been used to study the cortical dynamics of language.


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