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Christopher Blair, Ryan Mruczek, Gideon Caplovitz; Decoding the neural representation of size using multivariate pattern analyses and high density electroencephalography. Journal of Vision 2015;15(12):579. doi: https://doi.org/10.1167/15.12.579.
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© ARVO (1962-2015); The Authors (2016-present)
In recent years a number of exciting fMRI studies have highlighted the role of primary visual cortex in the representation of the perceived size of objects. Several studies have now demonstrated that an object’s perceived size, independent of its retinal size, is correlated with functional and anatomical aspects of V1. These results have been surprising, given previous assumptions about the retinotopic nature of V1 processing. What remains fundamentally unknown is whether these representations of the perceived size of objects are originating in V1 and propagating in a feedforward manner to the rest of the visual system, or if representations of an object’s perceived size may first originate in higher level visual areas before feeding back to V1. Unfortunately, fMRI lacks the temporal resolution to directly address this question. Here we explore the viability of High Density EEG as a potential tool for resolving this question. EEG offers the necessary temporal resolution, and when combined with Dipole Source Localization, has the potential to identify neural correlates of perceived size throughout visual cortex . We demonstrate an important first step in determining whether EEG can reveal neural correlates of perceived size: the classification of retinal size with ms temporal resolution. Using a 256 channel High Density EEG system, event related potentials were evoked by either small or large circles presented within Ebbinghaus-type arrays. Multivariate pattern analyses performed on the electrode data could successfully distinguish between small and large target circles. This suggests that HD-EEG may indeed be a plausible approach for addressing questions related to the representation of perceived size.
Meeting abstract presented at VSS 2015
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