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Rosemary Le, Chen Gafni, Michal Ben-Shachar, Brian Wandell; Stimulus dependence of population receptive fields within the visual field maps and the visual word form area. Journal of Vision 2018;18(10):249. doi: 10.1167/18.10.249.
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© ARVO (1962-2015); The Authors (2016-present)
Background FMRI responses to text in the visual word form area (VWFA) are larger than responses to other stimulus types. Here we try to understand the origin of these differences by analyzing signals in the visual field maps (V1, V2, V3, hV4). While the magnitude of the fMRI signal may not differentiate words vs. nonwords in the visual field maps, models of the signal may differentiate these stimulus types. Specifically, population receptive field (pRF) measurements for words versus nonword stimuli may differ not only in the VWFA but also in the visual field maps. Methods We fit pRFs to three types of stimuli: words, false font, and checkerboards. The subject performed a task at fixation while a rectangular aperture containing the stimulus traversed the visual field. Other stimulus manipulations included word size, language (Hebrew vs. English), and aperture size. We compared pRF estimates for different stimulus types across the visual areas and in the VWFA. Results Compared to word stimuli, pRFs measured with false font stimuli only differ in the VWFA. Compared to word stimuli, pRFs measured with checkerboard stimuli differ in the visual field maps. This difference is small in V1 and increases along the visual hierarchy. Compared to English words, pRFs measured with Hebrew words differ in the VWFA and and slightly in V3v, but not in the earliest visual field maps (V1 or V2v). Aperture size and word size also affect pRF parameters. Conclusions Language and stimulus properties have effects not only within the VWFA but also in the visual field maps. PRF modeling captures differences not observed in the magnitude of the fMRI signal. These differences may be relevant to modeling proper word form recognition and reading ability.
Meeting abstract presented at VSS 2018
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