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Michael Compton, Edward O'Neil, Lars Strother, Jacqueline Snow; Exploring the Real Object Advantage in Recognition Memory using fMRI. Journal of Vision 2016;16(12):1405. doi: 10.1167/16.12.1405.
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© 2017 Association for Research in Vision and Ophthalmology.
There is accruing evidence that suggests real objects are visually processed and represented differently as compared to matched planar image displays. Recent behavioral studies have shown that real objects are more memorable than images – a phenomenon we refer to as the 'real object memory advantage'. We used event-related fMRI to examine whether or not the real object advantage is attributable to the recruitment of ventral stream object-selective areas during recognition memory. Participants completed a study phase in which they were presented with a large set of everyday objects. Critically, half of the stimuli were presented as real-world objects and the remainder as high-resolution color images. The images were matched closely to their real-world counterparts for size, viewpoint, and illumination. Two hours later, participants completed a two-AFC recognition task in an fMRI scanner. At test, the stimuli were displayed as words to ensure that behavioral and brain-based differences were not attributable to congruence in display format between study and test. Behaviorally, recognition performance for stimuli encoded as real objects was superior to those encoded as images, replicating our earlier result. Surprisingly, our preliminary fMRI analyses did not reveal any compelling evidence of neural correlates of the real object advantage in the ventral visual object recognition system, which was presumably engaged during encoding. Rather, the neural basis of the real object memory advantage appears to reside in dorsal (i.e., fronto-parietal) and anterior ventral regions of cortex. In summary, our behavioral data replicate and extend previous findings, and our neuroimaging data reveal a previously undiscovered neural basis of the real object memory advantage.
Meeting abstract presented at VSS 2016
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