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Jillian Nguyen, Jay Ravaliya, Ushma Majmudar, Thomas Papathomas, Elizabeth Torres; Blind prediction of perceptual states using patterns of motor variability. Journal of Vision 2014;14(10):832. doi: https://doi.org/10.1167/14.10.832.
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© ARVO (1962-2015); The Authors (2016-present)
We heavily rely on information from our visual system to help coordinate our daily interactions in the environment. Yet, our understanding of how top-down visual processes modulate the execution of fundamental goal-directed and transitional motor actions remains unclear. Here we utilize a robust 3D depth inversion illusion (3D-DII) to explore how top-down processes influence reach dynamics when participants grab toward a target embedded in a 3D scene, specifically asking whether their motor action is governed by the 3D-DIIs real or perceived geometry. Two 3D stimuli were used: (1) a proper-perspective (or forced perspective) in which perspective-painted cues were congruent with the bottom-up signals of binocular disparity and motion parallax, and (2) a reverse-perspective, in which the painted cues competed with bottom-up signals, eliciting bistable percepts of: (a) veridical depth and (b) illusory reverse-depth percept in which concave parts are perceived as convex and vice versa. The reverse-perspective 3D-DII generates nearly 90-degree differences in perceived surface orientation under veridical and illusory states, generating optimal conditions to explore differences in target approach. Subjects viewed the stimuli and grabbed at planar disk targets while we recorded their movements. Variability analyses of the normalized peak velocity in the arms retraction reveal informative distributions that blindly separate reaches performed under illusory and veridical states under the reverse-perspective, as well as for reaches conducted on the proper-perspective stimulus. This provides compelling evidence for the effect of top-down processes on somatosensation, allowing for the blind separation of reaches performed under each perceptual state based on self-emerging motor signatures.
Meeting abstract presented at VSS 2014
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