Publications

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Found 178 results
Author Title Type [ Year(Asc)]
2023
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.
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. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.
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., 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.
2022
Inglis, J. B., Bird J., & Ashby F. G. (2022).  A general recognition theory model for identifying an ideal stimulus. Attention, Perception, & Psychophysics. 84, 2408–2421.
Rosedahl, L. A., & Ashby F. G. (2022).  Linear separability, irrelevant variability, and categorization difficulty. Journal of Experimental Psychology: Learning, Memory, & Cognition. 48, 159-172.
Ashby, F. G., & Bamber D. (2022).  State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108, 102655.
Kovacs, P., & Ashby F. G. (2022).  On what it means to automatize a rule. Cognition. 226, 105168.
2018
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.
Crossley, M. J., Maddox W. T., & Ashby F. G. (2018).  Increased cognitive load enables unlearning in procedural category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition. 44, 1845-1853.
Soto, F. A., Vucovich L. E., & Ashby F. G. (2018).  Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data. PLOS Computational Biology. 14(10), e1006470.

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