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Casale, M. B., Roeder J. L., & F Ashby G. (2012).  Analogical transfer in perceptual categorization. Memory & Cognition. 40(3), 434-449.
F Ashby, G., & Crossley M. J. (2012).  Automaticity and multiple memory systems. Wiley Interdisciplinary Reviews in Cognitive Science. 3(3), 363-376.
Helie, S., Waldschmidt J. G., & F Ashby G. (2010).  Automaticity in rule-based and information-integration categorization. Attention, Perception, & Psychophysics. 72(4), 1013-1031.
F Ashby, G., & Berretty P. M. (1997).  Categorization as a special case of decision-making or choice. In A. A. J. Marley (Ed.), Choice, decision, and measurement: Essays in honor of R. Duncan Luce .
F Ashby, G., & Alfonso-Reese L. A. (1995).  Categorization as probability density estimation. Journal of Mathematical Psychology. 39, 216–233.
F Ashby, G., Boynton G., & W Lee W. (1994).  Categorization response time with multidimensional stimuli. Perception & Psychophysics. 55, 11–27.
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., & O'Brien J. B. (2005).  Category learning and multiple memory systems. Trends in Cognitive Science. 9(2), 83-89.
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., & Casale M. B. (2003).  The cognitive neuroscience of implicit category learning. Advances in consciousness research. 48, 109–142.
W Maddox, T., & F Ashby G. (1993).  Comparing decision bound and exemplar models of categorization. Perception & psychophysics. 53, 49–70.
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., & W Maddox T. (1992).  Complex decision rules in categorization: Contrasting novice and experienced performance. Journal of Experimental Psychology: Human Perception and Performance. 18, 50.
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., & Crossley M. J. (2011).  A computational model of how cholinergic interneurons protect striatal-dependent learning.. J Cogn Neurosci. 23(6), 1549-66.
Valentin, V. V., W Maddox T., & F Ashby G. (2014).  A computational model of the temporal dynamics of plasticity in procedural learning: Sensitivity to feedback timing. Frontiers in Psychology. 5, 643.
Crossley, M. J., F Ashby G., & W Maddox T. (2014).  Context-dependent savings in procedural category learning. Brain and Cognition. 92C, 1-10.
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.
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. (1987).  Counting and timing models in psychophysics and the conjoint Weber's law. Journal of Mathematical Psychology. 31, 419–428.
F Ashby, G., Paul E. J., & ToddMaddox W. (2011).  COVIS. In E. M. Pothos & A. J. Wills (Eds.), Formal approaches in categorization. 65-87.
J Smith, D., Johnston J. J. R., Musgrave R. D., Zakrzewski A. C., Boomer J., Church B. A., et al. (2014).  Cross-modal information integration in category learning. Attention, Perception, & Psychophysics. 76(5), 1473-1484.