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Found 179 results
Author [ Title(Desc)] 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 
N
F Ashby, G., Ennis J. M., & Spiering B. J. (2007).  A neurobiological theory of automaticity in perceptual categorization. Psychological Review. 114(3), 632-656.
F Ashby, G., & Crossley M. J. (2010).  The neurobiology of categorization. The Making of Human Concepts. Oxford University Press, New York. 75–98.
F Ashby, G., & Spiering B. J. (2004).  The neurobiology of category learning. Behavioral and Cognitive Neuroscience Reviews. 3(2), 101-113.
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.
Helie, S., Paul E. J., & F Ashby G. (2012).  A neurocomputational account of cognitive deficits in Parkinson's disease. Neuropsychologia. 50(9), 2290-2302.
Helie, S., Roeder J. L., Vucovich L., Rünger D., & F Ashby G. (2015).  A neurocomputational model of automatic sequence production. Journal of Cognitive Neuroscience. 27(7), 1412-1426.
Paul, E. J., & F Ashby G. (2013).  A neurocomputational theory of how explicit learning bootstraps early procedural learning. Frontiers in Computational Neuroscience. 7, 177.
Kovacs, P., Helie S., Tran A. N., & Ashby F. G. (2021).  A neurocomputational theory of how rule-guided behaviors become automatic. Psychological Review. 128, 488-508.
F Ashby, G., & Helie S. (2011).  The neurodynamics of cognition: A tutorial on computational cognitive neuroscience. Journal of Mathematical Psychology. 55(4), 273-289.
F Ashby, G., & Waldron E. M. (2000).  The neuropsychological bases of category learning. Current Directions in Psychological Science. 9, 10–14.
F Ashby, G., Alfonso-Reese L. A., Waldron E. M., & others (1998).  A neuropsychological theory of multiple systems in category learning. Psychological Review. 105, 442-481.
F Ashby, G., Isen A. M., & others (1999).  A neuropsychological theory of positive affect and its influence on cognition. Psychological Review. 106, 529-550.
Ashby, F. G., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. Cambridge University Press.
Rosedahl, L., & F Ashby G. (2018).  A New Stimulus Set for Cognitive Research.
Soto, F. A., & Ashby F. G. (2019).  Novel representations that support rule-based categorization are acquired on-the-fly during category learning. Psychological Research. 83, 544-566.
O
F Ashby, G., W Maddox T., & Bohil C. J. (2002).  Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition. 30, 666–677.
P
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.
Cantwell, G., Riesenhuber M., Roeder J. L., & Ashby F. G. (2017).  Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience. Neural Networks. 89, 31-38.
Townsend, J. T., Hu G. G., & F Ashby G. (1981).  Perceptual sampling of orthogonal straight line features. Psychological Research. 43, 259–275.
W Maddox, T., & F Ashby G. (1996).  Perceptual separability, decisional separability, and the identification–speeded classification relationship. Journal of Experimental Psychology: Human perception and performance. 22, 795.
F Ashby, G., & W Lee W. (1993).  Perceptual variability as a fundamental axiom of perceptual science. Advances in psychology. 99, 369–399.
J Smith, D., F Ashby G., Berg M. E., Murphy M. S., Spiering B., Cook R. G., et al. (2011).  Pigeons' categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review. 18(2), 414-421.
F Ashby, G., & W Lee W. (1991).  Predicting similarity and categorization from identification. Journal of Experimental Psychology: General. 120, 150.
F Ashby, G., & O'Brien J. B. (2008).  The P_rep statistic as a measure of confidence in model fitting. Psychonomic Bulletin & Review. 15(1), 16-27.
W Maddox, T., Prinzmetal W., Ivry R. B., & F Ashby G. (1994).  A probabilistic multidimensional model of location information. Psychological Research. 56, 66–77.

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