Publications

Reprint Requests

Contact Form

Found 184 results
[ Author(Desc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
A
F Ashby, G., Noble S., J Filoteo V., Waldron E. M., & Ell S. W. (2003).  Category learning deficits in Parkinson's disease. Neuropsychology. 17(1), 115-24.
F Ashby, G., & Spiering B. J. (2004).  The neurobiology of category learning. Behavioral and Cognitive Neuroscience Reviews. 3(2), 101-113.
F Ashby, G., & W Maddox T. (2005).  Human category learning. Annual Review of Psychology. 56, 149-178.
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., & O'Brien J. B. (2005).  Category learning and multiple memory systems. Trends in Cognitive Science. 9(2), 83-89.
F Ashby, G., Ennis J. M., & Spiering B. J. (2007).  A neurobiological theory of automaticity in perceptual categorization. Psychological Review. 114(3), 632-656.
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., & O'Brien J. B. (2008).  The P_rep statistic as a measure of confidence in model fitting. Psychonomic Bulletin & Review. 15(1), 16-27.
F Ashby, G., & Waldschmidt J. G. (2008).  Fitting computational models to fMRI. Behavioral Research Methods. 40(3), 713-721.
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.
F Ashby, G., & Helie S. (2011).  The neurodynamics of cognition: A tutorial on computational cognitive neuroscience. Journal of Mathematical Psychology. 55(4), 273-289.
F Ashby, G., & W Maddox T. (2011).  Human category learning 2.0. Annals of the New York Academy of Sciences. 1224, 147-161.
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., & Crossley M. J. (2012).  Automaticity and multiple memory systems. Wiley Interdisciplinary Reviews in Cognitive Science. 3(3), 363-376.
F Ashby, G., & W Maddox T. (1993).  Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology. 37, 372–400.
F Ashby, G. (1987).  Counting and timing models in psychophysics and the conjoint Weber's law. Journal of Mathematical Psychology. 31, 419–428.
F Ashby, G., Boynton G., & W Lee W. (1994).  Categorization response time with multidimensional stimuli. Perception & Psychophysics. 55, 11–27.
F Ashby, G. (2014).  Is state-trace analysis an appropriate tool for assessing the number of cognitive systems?. Psychonomic Bulletin & Review. 21(4), 935-946.
F Ashby, G., & Waldron E. M. (1999).  On the nature of implicit categorization. Psychonomic Bulletin & Review. 6, 363–378.
F Ashby, G., Queller S., & Berretty P. M. (1999).  On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics. 61, 1178–1199.
F Ashby, G., & Waldron E. M. (2000).  The neuropsychological bases of category learning. Current Directions in Psychological Science. 9, 10–14.
Ashby, FG. (2001).  Categorization and similarity models: Neuroscience applications.
F Ashby, G., W Lee W., & Balakrishnan JD. (1992).  Comparing the biased choice model and multidimensional decision bound models of identification. Mathematical Social Sciences. 23, 175–197.
F Ashby, G., & Ell S. W. (2002).  Single versus multiple systems of learning and memory. Stevens' handbook of experimental psychology.
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.

Pages