Publications
Reprint Requests
Computational neuroscientific models of categorization.
in R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Sciences.
(In Press). Linear separability, irrelevant variability, and categorization difficulty.
Journal of Experimental Psychology: Learning, Memory, & Cognition.
(In Press). A neurocomputational theory of how rule-guided behaviors become automatic.
Psychological Review.
(In Press). Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty.
Memory & Cognition. 48, 541-552.
(2020). Modulation of dopamine for adaptive learning: A neurocomputational model.
Computational Brain & Behavior. 1–19.
(2020). A role for the medial temporal lobes in category learning.
Learning & Memory. 27, 441-450.
(2020). A difficulty predictor for perceptual category learning.
Journal of Vision. 19(6), 20.
(2019). Novel representations that support rule-based categorization are acquired on-the-fly during category learning.
Psychological Research. 83, 544-566.
(2019). State-trace analysis misinterpreted and misapplied: Reply to Stephens, Matzke, and Hayes (2019).
Journal of Mathematical Psychology. 91, 195-200.
(2019).
(2019). Testing analogical rule transfer in pigeons (Columba livia).
Cognition. 183, 256-268.
(2019). The categorization experiment: Experimental design and data analysis.
In E. J. Wagenmakers & J. T. Wixted (Eds.), Stevens handbook of experimental psychology and cognitive neuroscience, Fourth Edition, Volume Five: Methodology. New York: Wiley. 307-.
(2018). Computational cognitive neuroscience.
In W. Batchelder, H. Colonius, E. Dzhafarov, & J. Myung (Eds.), New handbook of mathematical psychology, Volume 2. NY: Cambridge University Press. 223-270.
(2018). Increased cognitive load enables unlearning in procedural category learning.
Journal of Experimental Psychology: Learning, Memory, & Cognition. 44, 1845-1853.
(2018). Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data.
PLOS Computational Biology. 14(10), e1006470.
(2018).
(2018).
(2018). Retinal-specific category learning.
Nature Human Behaviour. 1.
(2018). Trial-by-trial switching between procedural and declarative categorization systems.
Psychological Research. 82, 371-384.
(2018). Hierarchical control of procedural and declarative category-learning systems.
NeuroImage. 150, 150-161.
(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.
(2017). A neural interpretation of exemplar theory.
Psychological Review. 124(4), 472-482.
(2017). Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience.
Neural Networks. 89, 31-38.
(2017). Quantitative modeling of category learning deficits in various patient populations.
Neuropsychology. 31, 862-876.
(2017). Testing separability and independence of perceptual dimensions with general recognition theory: A tutorial and new R package (grtools).
Frontiers in Psychology. 8, 696.
(2017).