Reprint Requests

Found 167 results
Author Title Type [ Year(Asc)]
In Press
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.
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.