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Summation of visual motion across eye movements reflects a non-spatial decision mechanism

Identifieur interne : 000F96 ( Pmc/Corpus ); précédent : 000F95; suivant : 000F97

Summation of visual motion across eye movements reflects a non-spatial decision mechanism

Auteurs : Adam P. Morris ; Charles C. Liu ; Simon J. Cropper ; Jason D. Forte ; Bart Krekelberg ; Jason B. Mattingley

Source :

RBID : PMC:2917252

Abstract

Human vision remains perceptually stable even though retinal inputs change rapidly with each eye movement. Although the neural basis of visual stability remains unknown, a recent psychophysical study pointed to the existence of visual feature-representations anchored in environmental rather than retinal coordinates (e.g. ‘spatiotopic’ receptive fields; Melcher, D., and Morrone, M.C. (2003). Spatiotopic temporal integration of visual motion across saccadic eye movements. Nat Neurosci 6, 877-881). In that study, sensitivity to a moving stimulus presented after a saccadic eye movement was enhanced when preceded by another moving stimulus at the same spatial location prior to the saccade. The finding is consistent with spatiotopic sensory integration, but it could also have arisen from a probabilistic improvement in performance due to the presence of more than one motion signal for the perceptual decision. Here we show that this statistical advantage accounts completely for summation effects in this task. We first demonstrate that measurements of summation are confounded by noise related to an observer's uncertainty about motion onset times. When this uncertainty is minimized, comparable summation is observed irrespective of whether two motion signals occupy the same or different locations in space, and whether they contain the same or opposite directions of motion. These results are incompatible with the tuning properties of motion-sensitive sensory neurons and provide no evidence for a spatiotopic representation of visual motion. Instead, summation in this context reflects a decision mechanism that uses abstract representations of sensory events to optimize choice behavior.


Url:
DOI: 10.1523/JNEUROSCI.1705-10.2010
PubMed: 20660264
PubMed Central: 2917252

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