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Found 169 results
Author Title Type [ Year(Asc)]
In Press
F. Ashby, G., & Wang Y-W. (In Press).  Computational neuroscientific models of categorization. in R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Sciences.
Rosedahl, L. A., & F Ashby G. (In Press).  Linear separability, irrelevant variability, and categorization difficulty. Journal of Experimental Psychology: Learning, Memory, & Cognition.
Kovacs, P., Helie S., Tran A. N., & Ashby F. G. (In Press).  A neurocomputational theory of how rule-guided behaviors become automatic. Psychological Review.
Rosedahl, L. A., Serota R., & F. Ashby G. (In Press).  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.
2020
Ashby, F. G., Smith J. D., & Rosedahl L. (2020).  Dissociations between rule-based and information-integration categorization are not caused by differences in task difficulty. Memory & Cognition. 48, 541-552.
Inglis, J. B., Valentin V. V., & F Ashby G. (2020).  Modulation of dopamine for adaptive learning: A neurocomputational model. Computational Brain & Behavior. 1–19.
Wang, Y-W., & Ashby F. G. (2020).  A role for the medial temporal lobes in category learning. Learning & Memory. 27, 441-450.
2019
Rosedahl, L., & Ashby F. G. (2019).  A difficulty predictor for perceptual category learning. Journal of Vision. 19(6), 20.
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.
Ashby, F. G. (2019).  State-trace analysis misinterpreted and misapplied: Reply to Stephens, Matzke, and Hayes (2019). Journal of Mathematical Psychology. 91, 195-200.
Ashby, F. G. (2019).  Statistical Analysis of fMRI Data, Second Edition. Cambridge, MA: MIT Press.
Qadri, M. A. J., Ashby F. G., Smith J. D., & Cook R. G. (2019).  Testing analogical rule transfer in pigeons (Columba livia). Cognition. 183, 256-268.
2018
Ashby, F. G., & Valentin V. V. (2018).  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-.
Ashby, F. G. (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.
Crossley, M. J., Maddox W. T., & Ashby F. G. (2018).  Increased cognitive load enables unlearning in procedural category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition. 44, 1845-1853.
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.
Rosedahl, L. A., & F. Ashby G. (2018).  A Neural Interpretation of Exemplar Theory.
Rosedahl, L., & F Ashby G. (2018).  A New Stimulus Set for Cognitive Research.
Rosedahl, L. A., Eckstein M. P., & F Ashby G. (2018).  Retinal-specific category learning. Nature Human Behaviour. 1.
Crossley, M. J., Roeder J. L., Helie S., & Ashby F. G. (2018).  Trial-by-trial switching between procedural and declarative categorization systems. Psychological Research. 82, 371-384.
2017
Turner, B. O., Crossley M. J., & Ashby F. G. (2017).  Hierarchical control of procedural and declarative category-learning systems. NeuroImage. 150, 150-161.
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.
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
Cantwell, G., Riesenhuber M., Roeder J. L., & Ashby F. G. (2017).  Perceptual category learning and visual processing: An exercise in computational cognitive neuroscience. Neural Networks. 89, 31-38.
Filoteo, J. V., Maddox W. T., & Ashby F. G. (2017).  Quantitative modeling of category learning deficits in various patient populations. Neuropsychology. 31, 862-876.

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