Publications
Reprint Requests
The neurobiology of human category learning.
Trends in cognitive sciences. 5, 204–210.
(2001). The neurobiology of category learning.
Behavioral and Cognitive Neuroscience Reviews. 3(2), 101-113.
(2004). The neurobiology of categorization.
The Making of Human Concepts. Oxford University Press, New York. 75–98.
(2010). A neurobiological theory of automaticity in perceptual categorization.
Psychological Review. 114(3), 632-656.
(2007). Neural networks underlying the metacognitive uncertainty response.
Cortex. 71, 306-22.
(2015). A neural interpretation of exemplar theory.
Psychological Review. 124(4), 472-482.
(2017). 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.
(2016). On the nature of implicit categorization.
Psychonomic Bulletin & Review. 6, 363–378.
(1999). Multivariate probability distributions.
In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 1-34.
(1992). Multiple systems of perceptual category learning: Theory and cognitive tests.
Handbook of categorization in cognitive science. 547–572.
(2005). 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.
(2017). Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory.
Psychonomic Bulletin & Review. 22(6), 1598-1613.
(2015). Multiple attention systems in perceptual categorization.
Memory & Cognition. 30, 325–339.
(2002). 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.
(2015). Multidimensional models of categorization.
In F. G. Ashby (Ed.), Multidimensional models of perception and cognition. 449-483.
(1992). Modulation of dopamine for adaptive learning: A neurocomputational model.
Computational Brain & Behavior. 1–19.
(2020). A model of dopamine modulated cortical activation.
Neural Networks. 16(7), 973-984.
(2003). Methods of modeling capacity in simple processing systems.
Cognitive theory. 3, 200–239.
(1978). Measurement scales and statistics: The misconception misconceived..
Psychological Bulletin. 96, 394 401.
(1984). 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.
(2023). Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data.
PLOS Computational Biology. 14(10), e1006470.
(2018). Linear separability, irrelevant variability, and categorization difficulty.
Journal of Experimental Psychology: Learning, Memory, & Cognition. 48, 159-172.
(2022). Length of the state trace: A method for partitioning model complexity.
Journal of Mathematical Psychology. 113, 102755.
(2023). Learning robust cortico-cortical associations with the basal ganglia: an integrative review.
Cortex. 64, 123-35.
(2015). Learning and transfer of category knowledge in an indirect categorization task.
Psychological Research. 76(3), 292-303.
(2012).