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Justin L. Gardner, PhD

Psychology Stanford University, Palo Alto, CA 
MRI vision attention decision motion inference selection
"justin gardner neuroscience"
Mean distance: 12.89 (cluster 29)


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Stephen G. Lisberger grad student 1996-2002 University of California, San Francisco with the University of California, Berkeley
 (Coordinated target selection for ocular orienting and tracking movements.)
Keiji Tanaka post-doc 2002-2004 RIKEN BSI
David J. Heeger post-doc 2004-2009 NYU


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Daniel Birman grad student 2014-2019
Steeve Laquitaine post-doc RIKEN BSI
Arman Abrahamyan post-doc 2012- RIKEN, BSI
Franco Pestilli post-doc 2009-2009 RIKEN BSI


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R. Allen Waggoner collaborator (Chemistry Tree)
BETA: Related publications


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Birman D, Gardner JL. (2019) A flexible readout mechanism of human sensory representations. Nature Communications. 10: 3500
Fukuda H, Ma N, Suzuki S, et al. (2019) Computing Social Value Conversion in the Human Brain. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience
Gardner JL, Liu T. (2019) Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus. Eneuro. 6
Birman D, Gardner JL. (2018) A quantitative framework for motion visibility in human cortex. Journal of Neurophysiology
Liu T, Cable D, Gardner JL. (2017) Inverted encoding models of human population response conflate noise and neural tuning width. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience
Abrahamyan A, Silva LL, Dakin SC, et al. (2016) Adaptable history biases in human perceptual decisions. Proceedings of the National Academy of Sciences of the United States of America. 113: E3548-57
Birman D, Gardner JL. (2015) Parietal and prefrontal: categorical differences? Nature Neuroscience. 19: 5-7
Gardner JL. (2015) A case for human systems neuroscience. Neuroscience. 296: 130-7
Hara Y, Gardner JL. (2014) Encoding of graded changes in spatial specificity of prior cues in human visual cortex. Journal of Neurophysiology. 112: 2834-49
Hara Y, Pestilli F, Gardner JL. (2014) Differing effects of attention in single-units and populations are well predicted by heterogeneous tuning and the normalization model of attention. Frontiers in Computational Neuroscience. 8: 12
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