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Sarah Herald, Hua Yang, Bradley Duchaine; Contralateral bias persists in category-selective visual areas. Journal of Vision 2017;17(10):282. doi: https://doi.org/10.1167/17.10.282.
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fMRI studies in humans and single-unit work in macaques has suggested that visual recognition mechanisms show contralateral biases that are much weaker than those found in early visual cortex (Hemond et al., 2007). In almost all these studies, single stimuli were displayed peripherally to assess biases. In natural vision though, visual stimuli are present in both hemifields, and a recent ERP study found the N170 was driven exclusively by the contralateral stimulus when faces and houses were simultaneously presented to both hemifields (Towler & Eimer, 2015). To examine contralateral biases in category-selective visual areas using a more naturalistic display with fMRI, we first carried out a dynamic localizer using videos of faces, bodies, scenes, objects, and scrambled objects to identify category-selective areas. We then scanned participants while they viewed faces and houses simultaneously presented to the left and right visual hemifields. We found that face-selective and place-selective areas displayed large contralateral biases in which category-selective regions were primarily influenced by contralaterally-presented stimuli. For example, in the right OFA, the response to a contralateral house and an ipsilateral face is comparable to a contralateral house and ipsilateral house. Conversely, the response to a contralateral face and an ipsilateral house is only slightly weaker than the response to a contralateral face and ipsilateral face. Other category-selective areas, though not all, showed responses that were much more strongly modulated by contralateral than ipsilateral stimuli. These findings tentatively suggest that, under natural viewing conditions, peripheral stimuli are represented primarily in contralateral category-selective areas and that detection of peripheral stimuli is carried out largely by the contralateral hemisphere.
Meeting abstract presented at VSS 2017
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