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Scott Guerin, Stefan Uddenberg, Marcia Johnson, Chun Chun; Decoding the temporal structure of perception and reflection. Journal of Vision 2015;15(12):808. doi: https://doi.org/10.1167/15.12.808.
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© ARVO (1962-2015); The Authors (2016-present)
Perception has a clear temporal structure: each stimulus is preceded and followed by other stimuli. However, we may think about recently encountered stimuli in a temporal order that deviates from the perceptual input. How does the brain generate and maintain distinct representations of temporal structure associated with perception and reflection? In this experiment, we constructed a task that dissociates the temporal structure of perception and reflection. Participants viewed one face and one scene (2 s each) in one of two sequences: Face-Scene or Scene-Face. Following perception, participants were cued to direct their internal attention towards one then the other of the just-seen stimuli (refreshing, Johnson et al., 2005). Participants were instructed to imagine the picture as vividly as possible and answer a question about it (Male/Female or Indoor/Outdoor). Each refresh period lasted 2 s. On half the trials, participants refreshed the pictures in the same order they were perceived. On the other half of trials, participants refreshed the pictures in the reverse order. We applied multi-voxel pattern analysis to decode the temporal structure of perception and reflection. Based on an initial analysis of 12 participants, and consistent with our previous findings, we were able to decode the temporal structure of perception above chance in occipital, ventral temporal, and parietal cortices (all p < .001), with a trend in prefrontal cortex (p = .06). Critically, we were also able to decode the temporal structure of reflection in occipital, ventral temporal, parietal (all p < .01), and prefrontal cortices (p < .05). Consistent with previous studies indicating that perception and reflection share overlapping visual representations, our results indicate that perception and reflection share common neural machinery for the representation of temporal structure.
Meeting abstract presented at VSS 2015
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