Publications
Reprint Requests
Just Do It: A Neuropsychological Theory of Agency, Cognition, Mood, and Dopamine.
Journal of Experimental Psychology: General. 153(6), 1582–1604.
(2024). Computational cognitive neuroscience models of categorization.
In R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Sciences (pp.400-425). Cambridge University Press.
(2023). Encoding models in neuroimaging.
In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 421-472). Cambridge University Press.
(2023). Length of the state trace: A method for partitioning model complexity.
Journal of Mathematical Psychology. 113, 102755.
(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.
(2023).
(2023). Statistical decision theory.
In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 265-310). Cambridge University Press.
(2023). A general recognition theory model for identifying an ideal stimulus.
Attention, Perception, & Psychophysics. 84, 2408–2421.
(2022). Linear separability, irrelevant variability, and categorization difficulty.
Journal of Experimental Psychology: Learning, Memory, & Cognition. 48, 159-172.
(2022). State trace analysis: What it can and cannot do.
Journal of Mathematical Psychology. 108, 102655.
(2022). On what it means to automatize a rule.
Cognition. 226, 105168.
(2022). A neurocomputational theory of how rule-guided behaviors become automatic.
Psychological Review. 128, 488-508.
(2021). When instructions don't help: Knowing the optimal strategy facilitates rule-based but not information-integration category learning.
Journal of Experimental Psychology: Human Perception & Performance. 47, 1226-1236.
(2021). 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).