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Found 164 results
Author Title Type [ Year(Asc)]
In Press
Rosedahl, L., & Ashby F. G. (In Press).  A Difficulty Predictor for Perceptual Category Learning. Journal of Vision.
Ashby, F. G., Smith J. D., & Rosedahl L. (In Press).  Dissociations between Rule-Based and Information-Integration Categorization are Not Caused by Differences in Task Difficulty. Memory & Cognition.
Soto, F. A., & Ashby F. G. (In Press).  Novel representations that support rule-based categorization are acquired on-the-fly during category learning. Psychological Research.
Ashby, F. G. (In Press).  Statistical Analysis of fMRI Data, Second Edition. Cambridge, MA: MIT Press.
2020
Inglis, J. B., Valentin V. V., & F Ashby G. (2020).  Modulation of Dopamine for Adaptive Learning: a Neurocomputational Model. Computational Brain & Behavior. 1–19.
2019
Ashby, F. G. (2019).  State-trace analysis misinterpreted and misapplied: Reply to Stephens, Matzke, and Hayes (2019). Journal of Mathematical Psychology. 91, 195-200.
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.
Soto, F. A., Zheng E., Fonseca J., & Ashby F. G. (2017).  Testing separability and independence of perceptual dimensions with general recognition theory: A tutorial and new R package (grtools). Frontiers in Psychology. 8, 696.
Helie, S., Turner B. O., Crossley M. J., Ell S. W., & Ashby F. G. (2017).  Trial-by-trial identification of categorization strategy using iterative decision-bound modeling. Behavioral Research Methods. 49(3), 1146-1162.
2016
Crossley, M. J., Paul E. J., Roeder J. L., & Ashby F. G. (2016).  Declarative strategies persist under increased cognitive load. Psychonomic Bulletin & Review. 23(1), 213-22.
Soto, F. A., Bassett D. S., & Ashby F. G. (2016).  Dissociable changes in functional network topology underlie early category learning and development of automaticity. NeuroImage. 141, 220-241.
Valentin, V. V., Maddox W. T., & Ashby F. G. (2016).  Dopamine dependence in aggregate feedback learning: A computational cognitive neuroscience approach. Brain & Cognition. 109, 1-18.

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