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Simen Hagen, Quoc Vuong, Lisa Scott, Tim Curran, James Tanaka; Examining the role of motion in expert object recognition.. Journal of Vision 2017;17(10):65. doi: https://doi.org/10.1167/17.10.65.
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© ARVO (1962-2015); The Authors (2016-present)
Motion information contributes to multiple functions during the early stages of vision (e.g., attract attention, segment objects from the background); however, it also contributes to later stages of object recognition. For example, human observers can detect the presence of a human, judge its actions, judge its gender and identity simply based on motion cues conveyed in a point-light display. In the current study we examined whether real-world experience in an object domain can influence the sensitivity to the motion of objects within that domain. People with- and without-extensive experience in the bird domain were shown point-light displays of upright and inverted birds in flight, or upright and inverted human walkers, and asked to discriminate them from spatially scrambled point-light displays of the same stimuli. While the spatially scrambled stimuli retained the local motion of each dot of the moving objects, it disrupted the global percept of the object in motion. To estimate a detection threshold in each object domain, we systematically varied the number of noise dots in which the stimuli was embedded using an adaptive stair-case approach. Contrary to our prediction, both groups showed equal sensitivity to global bird motion with no inversion cost. However, consistent with previous work showing a robust inversion effect for human motion, both groups were more sensitive to upright human walkers than their inverted counterparts. Thus, at least under the conditions of our experiment, our result suggests that experience in the bird domain does not influence the sensitivity to global bird motion. However, the inversion effect with humans, but not with birds, suggest that motion recognition within the two domains rely on different mechanisms.
Meeting abstract presented at VSS 2017
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