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Found 178 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 
I
Spiering, B. J., & F Ashby G. (2008).  Initial training with difficult items facilitates information integration, but not rule-based category learning. Psychological Science. 19(11), 1169-1177.
F Ashby, G., & W Maddox T. (1990).  Integrating information from separable psychological dimensions. Journal of Experimental Psychology: Human Perception and Performance. 16, 598.
Ashby, F. G. (2015).  An introduction to fMRI. In B. U. Forstmann & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience. 91–112.
J
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. in press.
L
Helie, S., & F Ashby G. (2012).  Learning and transfer of category knowledge in an indirect categorization task. Psychological Research. 76(3), 292-303.
Helie, S., Ell S. W., & Ashby F. G. (2015).  Learning robust cortico-cortical associations with the basal ganglia: an integrative review. Cortex. 64, 123-35.
Ashby, F. G. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.
Rosedahl, L. A., & Ashby F. G. (2022).  Linear separability, irrelevant variability, and categorization difficulty. Journal of Experimental Psychology: Learning, Memory, & Cognition. 48, 159-172.
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.
M
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.
Townsend, J. T., & F Ashby G. (1984).  Measurement scales and statistics: The misconception misconceived.. Psychological Bulletin. 96, 394 401.
Townsend, J. T., & F Ashby G. (1978).  Methods of modeling capacity in simple processing systems. Cognitive theory. 3, 200–239.
F Ashby, G., & Casale M. B. (2003).  A model of dopamine modulated cortical activation. Neural Networks. 16(7), 973-984.
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. (1992).  Multidimensional models of categorization. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 449-483.
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.
W Maddox, T., F Ashby G., & Waldron E. M. (2002).  Multiple attention systems in perceptual categorization. Memory & Cognition. 30, 325–339.
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.
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.
F Ashby, G. (1992).  Multivariate probability distributions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 1-34.
N
F Ashby, G., & Waldron E. M. (1999).  On the nature of implicit categorization. Psychonomic Bulletin & Review. 6, 363–378.
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.
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
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.

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