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Found 184 results
Author [ Title(Asc)] 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
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.
Paul, E. J., & F Ashby G. (2013).  A neurocomputational theory of how explicit learning bootstraps early procedural learning. Frontiers in Computational Neuroscience. 7, 177.
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.
Helie, S., Paul E. J., & F Ashby G. (2012).  A neurocomputational account of cognitive deficits in Parkinson's disease. Neuropsychologia. 50(9), 2290-2302.
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.
F Ashby, G., & Spiering B. J. (2004).  The neurobiology of category learning. Behavioral and Cognitive Neuroscience Reviews. 3(2), 101-113.
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., Ennis J. M., & Spiering B. J. (2007).  A neurobiological theory of automaticity in perceptual categorization. Psychological Review. 114(3), 632-656.
Paul, E. J., J Smith D., Valentin V. V., Turner B. O., Barbey A. K., & Ashby F. G. (2015).  Neural networks underlying the metacognitive uncertainty response. Cortex. 71, 306-22.
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
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., & Waldron E. M. (1999).  On the nature of implicit categorization. Psychonomic Bulletin & Review. 6, 363–378.
M
F Ashby, G. (1992).  Multivariate probability distributions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 1-34.
F Ashby, G., & Valentin V. V. (2005).  Multiple systems of perceptual category learning: Theory and cognitive tests. Handbook of categorization in cognitive science. 547–572.
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.
Cantwell, G., Crossley M. J., & Ashby F. G. (2015).  Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory. Psychonomic Bulletin & Review. 22(6), 1598-1613.
W Maddox, T., F Ashby G., & Waldron E. M. (2002).  Multiple attention systems in perceptual categorization. Memory & Cognition. 30, 325–339.
Ashby, F. G., & Soto F. A. (2015).  Multidimensional signal detection theory. In: J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology. 13–34.
F Ashby, G. (1992).  Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 449-483.
Inglis, J. B., Valentin V. V., & F Ashby G. (2020).  Modulation of dopamine for adaptive learning: A neurocomputational model. Computational Brain & Behavior. 1–19.
F Ashby, G., & Casale M. B. (2003).  A model of dopamine modulated cortical activation. Neural Networks. 16(7), 973-984.
Townsend, J. T., & F Ashby G. (1978).  Methods of modeling capacity in simple processing systems. Cognitive theory. 3, 200–239.
Townsend, J. T., & F Ashby G. (1984).  Measurement scales and statistics: The misconception misconceived.. Psychological Bulletin. 96, 394 401.
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.
L
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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