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Found 185 results
[ Author(Desc)] 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. (1995).  Resurrecting information theory: A review of "Information, Sensation, and Perception," by Kenneth H. Norwich. The American Journal of Psychology. 108, 609-614.
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. (1992).  Pattern recognition by human and machine. Review of "Adaptive Pattern Recognition and Neural Networks", by Yoh-Han Pao. Journal of Mathematical Psychology. 36, 146-153.
F Ashby, G. (1992).  Multivariate probability distributions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 1-34.
F Ashby, G. (1992).  Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 449-483.
F Ashby, G., & W Lee W. (1992).  On the relationship among identification, similarity, and categorization: Reply to Nosofsky and Smith (1992). Journal of Experimental Psychology: General. 121, 385-393.
F Ashby, G., & Gott R. E. (1988).  Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition. 14, 33.
F Ashby, G., & W Lee W. (1991).  Predicting similarity and categorization from identification. Journal of Experimental Psychology: General. 120, 150.
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. (In Press).  Supervisory control of declarative and procedural responding. Memory & Cognition.
Ashby, F. G. (2026).  Sense of agency and decision making. Frontiers in Psychology. 17, 1816045.
Ashby, F. G. (2026).  Statistical decision theory in perception and cognition: Signal detection & general recognition theories. MIT Press.
Ashby, F. G., & Ashby G. Z. (2026).  Sense of agency and addiction. Frontiers in Psychology. 1725049.
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.
Ashby, F. G., Zetzer H. A., Conoley C. W., & Pickering A. D. (2024).  Just Do It: A Neuropsychological Theory of Agency, Cognition, Mood, and Dopamine. Journal of Experimental Psychology: General. 153(6), 1582–1604.
F Ashby, G., Tein J-Y., & Balakrishnan JD. (1993).  Response time distributions in memory scanning. Journal of Mathematical Psychology. 37, 526–555.
Ashby, F. G. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.
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., & 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., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. 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., 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., & Bamber D. (2022).  State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108, 102655.
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., & 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.

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