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Found 179 results
Author Title Type [ Year(Desc)]
2005
F Ashby, G., Ell S. W., Valentin V. V., & Casale M. B. (2005).  FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience. 17(11), 1728-1743.
F Ashby, G., & W Maddox T. (2005).  Human category learning. Annual Review of Psychology. 56, 149-178.
F Ashby, G., & Valentin V. V. (2005).  Multiple systems of perceptual category learning: Theory and cognitive tests. Handbook of categorization in cognitive science. 547–572.
2006
Ell, S. W., & F Ashby G. (2006).  The effects of category overlap on information-integration and rule-based category learning. Perception & Psychophysics. 68(6), 1013-1026.
2007
F Ashby, G., & Valentin V. V. (2007).  Computational cognitive neuroscience: Building and testing biologically plausible computational models of neuroscience, neuroimaging, and behavioral data. Statistical and process models for cognitive neuroscience and aging. 15–58.
F Ashby, G., & O'Brien J. B. (2007).  The effects of positive versus negative feedback on information-integration category learning. Perception & Psychophysics. 69(6), 865-878.
F Ashby, G., Ennis J. M., & Spiering B. J. (2007).  A neurobiological theory of automaticity in perceptual categorization. Psychological Review. 114(3), 632-656.
2008
F Ashby, G., & Waldschmidt J. G. (2008).  Fitting computational models to fMRI. Behavioral Research Methods. 40(3), 713-721.
Spiering, B. J., & F Ashby G. (2008).  Initial training with difficult items facilitates information integration, but not rule-based category learning. Psychological Science. 19(11), 1169-1177.
F Ashby, G., & O'Brien J. B. (2008).  The P_rep statistic as a measure of confidence in model fitting. Psychonomic Bulletin & Review. 15(1), 16-27.
Spiering, B. J., & F Ashby G. (2008).  Response processes in information-integration category learning. Neurobiology of Learning and Memory. 90(2), 330-338.
Casale, M. B., & F Ashby G. (2008).  A role for the perceptual representation memory system in category learning. Perception & Psychophysics. 70(6), 983-999.
2010
Helie, S., Waldschmidt J. G., & F Ashby G. (2010).  Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics. 72(4), 1013-1031.
W Maddox, T., Glass B. D., O'Brien J. B., J Filoteo V., & F Ashby G. (2010).  Category label and response location shifts in category learning. Psychological Research. 74(2), 219-236.
F Ashby, G., Turner B. O., & Horvitz J. C. (2010).  Cortical and basal ganglia contributions to habit learning and automaticity. Trends in Cognitive Science. 14(5), 208-215.
Helie, S., Roeder J. L., & F Ashby G. (2010).  Evidence for cortical automaticity in rule-based categorization. Journal of Neuroscience. 30(42), 14225-14234.
J Smith, D., Beran M. J., Crossley M. J., Boomer J., & F Ashby G. (2010).  Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes. 36(1), 54-65.
F Ashby, G., & Crossley M. J. (2010).  The neurobiology of categorization. The Making of Human Concepts. Oxford University Press, New York. 75–98.
2011
F Ashby, G., & Crossley M. J. (2011).  A computational model of how cholinergic interneurons protect striatal-dependent learning.. J Cogn Neurosci. 23(6), 1549-66.
Waldschmidt, J. G., & F Ashby G. (2011).  Cortical and striatal contributions to automaticity in information-integration categorization.. Neuroimage. 56(3), 1791-802.
F Ashby, G., Paul E. J., & ToddMaddox W. (2011).  COVIS. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization. 65-87.
F Ashby, G., & W Maddox T. (2011).  Human category learning 2.0. Annals of the New York Academy of Sciences. 1224, 147-161.
Paul, E. J., Boomer J., J Smith D., & F Ashby G. (2011).  Information-integration category learning and the human uncertainty response. Memory & Cognition. 39(3), 536-554.
F Ashby, G., & Helie S. (2011).  The neurodynamics of cognition: A tutorial on computational cognitive neuroscience. Journal of Mathematical Psychology. 55(4), 273-289.
J Smith, D., F Ashby G., Berg M. E., Murphy M. S., Spiering B., Cook R. G., et al. (2011).  Pigeons' categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review. 18(2), 414-421.

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