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Found 179 results
[ Author(Desc)] Title Type Year
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A
Alfonso-Reese, L. A., F Ashby G., & Brainard D. H. (2002).  What makes a categorization task difficult?. Attention, Perception, & Psychophysics. 64, 570–583.
Ashby, F. G. (2024).  On using the fixed-point property of binary mixtures to discriminate among models of recognition memory. Journal of Mathematical Psychology. 123, 102889.
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. (1999).  On the nature of implicit categorization. Psychonomic Bulletin & Review. 6, 363–378.
F Ashby, G., Boynton G., & W Lee W. (1994).  Categorization response time with multidimensional stimuli. Perception & Psychophysics. 55, 11–27.
F Ashby, G., & W Maddox T. (1994).  A response time theory of separability and integrality in speeded classification. Journal of Mathematical Psychology. 38, 423–466.
F Ashby, G., W Maddox T., & W Lee W. (1994).  On the dangers of averaging across subjects when using multidimensional scaling or the similarity-choice model. Psychological Science. 5, 144–151.
F Ashby, G. (2011).  Statistical analysis of fMRI data.
F Ashby, G., & W Lee W. (1993).  Perceptual variability as a fundamental axiom of perceptual science. Advances in psychology. 99, 369–399.
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., & 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., & W Maddox T. (1998).  Stimulus categorization. Measurement, judgment, and decision making. 251–301.
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. (1991).  A response time theory of perceptual independence. Mathematical Psychology. 389–413.
F Ashby, G., & Waldron E. M. (2000).  The neuropsychological bases of category learning. Current Directions in Psychological Science. 9, 10–14.
F Ashby, G. (1989).  Stochastic general recognition theory. Human information processing: Measures, mechanisms, and models. 435–457.
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. (2013).  Human category learning, neural basis. The encyclopedia of the mind. 130–134.
F Ashby, G. (1983).  A biased random walk model for two choice reaction times. Journal of Mathematical Psychology. 27, 277–297.
F Ashby, G. (1982).  Testing the assumptions of exponential, additive reaction time models. Memory & Cognition. 10, 125–134.
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.
F Ashby, G., & Ell S. W. (2002).  Single versus multiple systems of learning and memory. Stevens' handbook of experimental psychology.
F Ashby, G., & Townsend J. T. (1980).  Decomposing the reaction time distribution: Pure insertion and selective influence revisited. Journal of Mathematical Psychology. 21, 93–123.
Ashby, FG. (2001).  Categorization and similarity models: Neuroscience applications.
F Ashby, G., & Crossley M. J. (2010).  The neurobiology of categorization. The Making of Human Concepts. Oxford University Press, New York. 75–98.

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