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Tytuł:
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Motion opponency at the middle temporal cortex: Preserved motion information and the effect of perceptual learning.
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Autorzy:
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Yu A; Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA.
Zhang R; Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA.
Silva AE; School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada.
Xing Y; Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA.
Thompson B; School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada.; Centre for Eye and Vision Research, Hong Kong.
Liu Z; Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA.; 1285 Psychology Building, Box 951563, Los Angeles, California, USA.
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Źródło:
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The European journal of neuroscience [Eur J Neurosci] 2022 Dec; Vol. 56 (12), pp. 6215-6226. Date of Electronic Publication: 2022 Nov 18.
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Typ publikacji:
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Journal Article; Research Support, Non-U.S. Gov't
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Język:
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English
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Imprint Name(s):
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Publication: : Oxford : Wiley-Blackwell
Original Publication: Oxford, UK : Published on behalf of the European Neuroscience Association by Oxford University Press, c1989-
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MeSH Terms:
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Motion Perception*/physiology
Animals ; Humans ; Temporal Lobe/physiology ; Motion ; Learning ; Magnetic Resonance Imaging/methods ; Photic Stimulation/methods
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References:
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Bradley, D.C., Qian, N., & Andersen, R.A. (1995). Integration of motion and stereopsis in middle temporal cortical area of macaques. Nature, 373(6515), 609-611. https://doi.org/10.1038/373609a0.
Garcia, J.O., & Grossman, E.D. (2009). Motion opponency and transparency in the human middle temporal area. European Journal of Neuroscience, 30(6), 1172-1182. https://doi.org/10.1111/j.1460-9568.2009.06893.x.
Glass, L. (1969). Moiré effect from random dots. Nature, 223(5206), 578-580. https://doi.org/10.1038/223578a0.
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Haxby, J.V., Guntupalli, J.S., Nastase, S.A., & Feilong, M. (2020). Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies. eLife, 9, e56601. https://doi.org/10.7554/eLife.56601.
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Lu, H., Qian, N., & Liu, Z. (2004). Learning motion discrimination with suppressed MT. Vision Research, 44(15), 1817-1825. https://doi.org/10.1016/j.visres.2004.03.002.
Lu, H., Wu, Y.N., & Holyoak, K.J. (2019). Emergence of analogy from relation learning. Proceedings of the National Academy of Sciences, 116(10), 4176-4181. https://doi.org/10.1073/pnas.1814779116.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(85), 2825-2830.
Purushothaman, G., & Bradley, D.C. (2005). Neural population code for fine perceptual decisions in area MT. Nature Neuroscience, 8(1), 99-106. https://doi.org/10.1038/nn1373.
Qian, N., & Andersen, R. (1994). Transparent motion perception as detection of unbalanced motion signals. II. Physiology. The Journal of Neuroscience, 14(12), 7367-7380. https://doi.org/10.1523/JNEUROSCI.14-12-07367.1994.
Qian, N., Andersen, R., & Adelson, E. (1994). Transparent motion perception as detection of unbalanced motion signals. I. Psychophysics. The Journal of Neuroscience, 14(12), 7357-7366. https://doi.org/10.1523/JNEUROSCI.14-12-07357.1994.
Silva, A.E., Thompson, B., & Liu, Z. (2020). Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data. Human Brain Mapping. hbm.25198 https://doi.org/10.1002/hbm.25198.
Thompson, B., & Liu, Z. (2006). Learning motion discrimination with suppressed and un-suppressed MT. Vision Research, 46(13), 2110-2121. https://doi.org/10.1016/j.visres.2006.01.005.
Thompson, B., Tjan, B.S., & Liu, Z. (2013). Perceptual learning of motion direction discrimination with suppressed and unsuppressed MT in humans: An fMRI study. PLoS ONE, 8(1), e53458. https://doi.org/10.1371/journal.pone.0053458.
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Contributed Indexing:
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Keywords: MT; V1; motion; opponency; perceptual learning; support vector machine; suppression
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Entry Date(s):
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Date Created: 20221020 Date Completed: 20221216 Latest Revision: 20221227
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Update Code:
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20240105
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DOI:
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10.1111/ejn.15850
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PMID:
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36266211
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Motion opponency, first observed within the primate middle temporal cortex (MT), refers to the suppressing effect of opposite motion directions on neuronal activity. Namely, when opposing motion directional signals stimulate an MT neuron's receptive field, this neuron's response is comparable with that induced by flicker noise. Under such suppression, it is unknown whether any directional information is still represented at MT. In this study, we applied support vector machine (SVM) learning to human functional magnetic resonance imaging data to investigate if any motion defined orientation information was still available from suppressed MT. We found that, at least at the level of ±45° discrimination, such orientation information was still available. Interestingly, after behavioural perceptual learning that improved human discrimination of fine orientation discrimination (e.g. 42° vs. 48°) using the MT-suppressive motion stimuli, the SVM discrimination of ±45° worsened when functional magnetic resonance imaging (fMRI) signals at post-learning MT were used. This result is consistent with findings in Thompson et al. (2013) that, post-perceptual learning, MT suppression was not released, suggesting that motion opponency was perhaps functionally too important for perceptual learning to overcome.
(© 2022 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)
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