Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Information redundancy across spatial scales modulates early visual cortical processing

MPG-Autoren
/persons/resource/persons246803

Ten Oever,  Sanne
Maastricht University;
Language and Computation in Neural Systems, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Ergänzendes Material (frei zugänglich)
Zitation

Petras, K., Ten Oever, S., Dalal, S. S., & Goffaux, V. (2021). Information redundancy across spatial scales modulates early visual cortical processing. NeuroImage. Advance online publication. doi:10.1016/j.neuroimage.2021.118613.


Zitierlink: https://hdl.handle.net/21.11116/0000-0009-4252-0
Zusammenfassung
Visual images contain redundant information across spatial scales where low spatial frequency contrast is informative towards the location and likely content of high spatial frequency detail. Previous research suggests that the visual system makes use of those redundancies to facilitate efficient processing. In this framework, a fast, initial analysis of low-spatial frequency (LSF) information guides the slower and later processing of high spatial frequency (HSF) detail. Here, we used multivariate classification as well as time-frequency analysis of MEG responses to the viewing of intact and phase scrambled images of human faces to demonstrate that the availability of redundant LSF information, as found in broadband intact images, correlates with a reduction in HSF representational dominance in both early and higher-level visual areas as well as a reduction of gamma-band power in early visual cortex. Our results indicate that the cross spatial frequency information redundancy that can be found in all natural images might be a driving factor in the efficient integration of fine image details.