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Found 179 results
[ Author(Desc)] Title Type Year
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A
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. (1998).  Stimulus categorization. Measurement, judgment, and decision making. 251–301.
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.
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.
Ashby, FG. (2001).  Categorization and similarity models: Neuroscience applications.
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., & Ell S. W. (2002).  Single versus multiple systems of learning and memory. Stevens' handbook of experimental psychology.
F Ashby, G. (1989).  Stochastic general recognition theory. Human information processing: Measures, mechanisms, and models. 435–457.
Ashby, F. G. (2015).  An introduction to fMRI. In B. U. Forstmann & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience. 91–112.
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.
F Ashby, G., & Casale M. B. (2003).  The cognitive neuroscience of implicit category learning. Advances in consciousness research. 48, 109–142.
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.
Ashby, F. G., & Soto F. A. (2015).  Multidimensional signal detection theory. In: J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology. 13–34.

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