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Found 178 results
[ Author(Asc)] 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., & W Lee W. (1991).  Predicting similarity and categorization from identification. Journal of Experimental Psychology: General. 120, 150.
F Ashby, G. (1988).  Estimating the parameters of multidimensional signal detection theory from simultaneous ratings on separate stimulus components. Perception & Psychophysics. 44, 195–204.
F Ashby, G., & W Maddox T. (1990).  Integrating information from separable psychological dimensions. Journal of Experimental Psychology: Human Perception and Performance. 16, 598.
F Ashby, G., & Perrin N. A. (1988).  Toward a unified theory of similarity and recognition. Psychological Review. 95, 124-150.
F Ashby, G., & Townsend J. T. (1986).  Varieties of perceptual independence. Psychological Review. 93, 154-179.
F Ashby, G. (1982).  Deriving exact predictions from the cascade model. Psychological Review. 89, 599 607.
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., & 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.
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.
Ashby, F. G., & Vucovich L. E. (2016).  The role of feedback contingency in perceptual category learning. Journal of Experimental Psychology: Learning, Memory & Cognition. 42(11), 1731-1746.
Ashby, F. G., Valentin V. V., & von Meer S. S. (2015).  Differential effects of dopamine-directed treatments on cognition. Neuropsychiatric Disease and Treatment. 11, 1859-1875.
Ashby, F. G. (2000).  A stochastic version of general recognition theory. Journal of Mathematical Psychology. 44, 310–329.
Ashby, F. G. (2019).  State-trace analysis misinterpreted and misapplied: Reply to Stephens, Matzke, and Hayes (2019). Journal of Mathematical Psychology. 91, 195-200.
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., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
Ashby, F. G. (2019).  Statistical Analysis of fMRI Data, Second Edition. Cambridge, MA: MIT Press.
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.
Ashby, F. G., Smith J. D., & Rosedahl L. (2020).  Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty. Memory & Cognition. 48, 541-552.
Ashby, F. G., & Bamber D. (2022).  State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108, 102655.
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.
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.
Ashby, F. G., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. Cambridge University Press.
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.
Ashby, F. G., & Casale M. B. (2005).  Empirical dissociations between rule-based and similarity-based categorization. Behavioral and Brain Sciences. 28(1), 15-16.
Ashby, F. G. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.

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