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Found 19 results
Author Title Type [ Year(Desc)]
Filters: First Letter Of Title is N  [Clear All Filters]
1998
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.
1999
F Ashby, G., & Waldron E. M. (1999).  On the nature of implicit categorization. Psychonomic Bulletin & Review. 6, 363–378.
F Ashby, G., Isen A. M., & others (1999).  A neuropsychological theory of positive affect and its influence on cognition. Psychological Review. 106, 529-550.
2000
F Ashby, G., & Waldron E. M. (2000).  The neuropsychological bases of category learning. Current Directions in Psychological Science. 9, 10–14.
2001
F Ashby, G., & Ell S. W. (2001).  The neurobiology of human category learning. Trends in cognitive sciences. 5, 204–210.
2004
F Ashby, G., & Spiering B. J. (2004).  The neurobiology of category learning. Behavioral and Cognitive Neuroscience Reviews. 3(2), 101-113.
2007
F Ashby, G., Ennis J. M., & Spiering B. J. (2007).  A neurobiological theory of automaticity in perceptual categorization. Psychological Review. 114(3), 632-656.
2010
F Ashby, G., & Crossley M. J. (2010).  The neurobiology of categorization. The Making of Human Concepts. Oxford University Press, New York. 75–98.
2011
F Ashby, G., & Helie S. (2011).  The neurodynamics of cognition: A tutorial on computational cognitive neuroscience. Journal of Mathematical Psychology. 55(4), 273-289.
2012
Helie, S., Paul E. J., & F Ashby G. (2012).  A neurocomputational account of cognitive deficits in Parkinson's disease. Neuropsychologia. 50(9), 2290-2302.
2013
Paul, E. J., & F Ashby G. (2013).  A neurocomputational theory of how explicit learning bootstraps early procedural learning. Frontiers in Computational Neuroscience. 7, 177.
2015
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.
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.
2016
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.
2017
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
2018
Rosedahl, L., & F Ashby G. (2018).  A New Stimulus Set for Cognitive Research.
2019
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.
2021
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.
2023
Ashby, F. G., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. Cambridge University Press.