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Claudio Simoncini, Laurent U. Perrinet, Anna Montagnini, Pascal Mamassian, Guillaume S. Masson; Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation. Journal of Vision 2010;10(7):834. doi: https://doi.org/10.1167/10.7.834.
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© ARVO (1962-2015); The Authors (2016-present)
To measure speed and direction of moving objects, the cortical motion system pools information across different spatiotemporal channels. Here, we investigate this integration process for two different tasks. Primate ocular following responses are driven by speed information extracted by population of speed-tuned neurons. They provide an excellent probe for speed estimation. We contrasted these responses with a psychophysical speed discrimination task ran in the same subjects and with the same stimuli. We used short presentations (250ms) of “motion clouds” (Schrater et al 2000) in which the width of the spatial frequency distribution (σsf) was varied for different mean speed (10-50°/s). Eye movements were recorded with an EyeLink1000, using classical paradigm for ocular following. Stimuli were displayed on a CRT monitor (1280x1024@100Hz) and covered 47° of visual angle. All experiments were run on 2 naive subjects. We found that larger σsfelicited stronger initial eye velocity during the open-loop part of tracking responses. This facilitating effect was larger with higher speeds. By contrast, larger σsf had a detrimental effect upon speed discrimination performance. Speed discrimination thresholds were significantly higher (52%) with large spatial frequency distributions, irrespective of the mean stimulus speed. These results provide a framework to investigate how motion information is adaptively pooled for solving different motion tasks. Paul R. Schrater, David C. Knill and Eero P. Simoncelli (2000) “ Mechanisms of visual motion detection” Nature Neuroscience 3, 64 - 68.
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