Publications

Found 178 results
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Book Chapter
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 .
Ashby, F. G., & Valentin V. V. (2018).  The categorization experiment: Experimental design and data analysis. In E. J. Wagenmakers & J. T. Wixted (Eds.), Stevens handbook of experimental psychology and cognitive neuroscience, Fourth Edition, Volume Five: Methodology. New York: Wiley. 307-.
Ashby, F. G. (2018).  Computational cognitive neuroscience. In W. Batchelder, H. Colonius, E. Dzhafarov, & J. Myung (Eds.), New handbook of mathematical psychology, Volume 2. NY: Cambridge University Press. 223-270.
Ashby, F. G., & Wang Y-W. (2023).  Computational cognitive neuroscience models of categorization. In R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Sciences (pp.400-425). Cambridge University Press.
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., Valentin V. V., & Turken AU. (2002).  The effects of positive affect and arousal on working memory and executive attention. In S. Moore & M. Oaksford (Eds.), Emotional Cognition: From Brain to Behaviour. 245–288.
Soto, F. A., & Ashby F. G. (2023).  Encoding models in neuroimaging. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 421-472). Cambridge University Press.
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.
Ashby, F. G., Crossley M. J., & Inglis J. B. (2023).  Mathematical models of human learning. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 163-217). Cambridge University Press.
F Ashby, G. (1992).  Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 449-483.
Ashby, F. G., & Valentin V. V. (2017).  Multiple systems of perceptual category learning: Theory and cognitive tests. In H. Cohen and C. Lefebvre (Eds.), Handbook of Categorization in Cognitive Science (Second Edition). 157–188.
F Ashby, G. (1992).  Multivariate probability distributions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 1-34.
Ashby, F. G., & Soto F. A. (2016).  The neural basis of general recognition theory. In J. W. Houpt & L. M. Blaha (Eds.), Mathematical models of perception and cognition: A Festschrift for James T. Townsend (pp. 1 - 31). New York: Psychology Press. 1-31.
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. (1991).  A response time theory of perceptual independence. Mathematical Psychology. 389–413.
Ashby, F. G., & Wenger M. J. (2023).  Statistical decision theory. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 265-310). Cambridge University Press.
Journal Article
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. (1983).  A biased random walk model for two choice reaction times. Journal of Mathematical Psychology. 27, 277–297.
Soto, F. A., Waldschmidt J. G., Helie S., & F Ashby G. (2013).  Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis. NeuroImage. 71, 284-297.

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