Publications

Reprint Requests

Contact Form

Found 184 results
Author [ Title(Asc)] Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
R
Ennis, D. M., & F Ashby G. (1993).  The relative sensitivities of same-different and identification judgment models to perceptual dependence. Psychometrika. 58, 257–279.
MacCallum, R., & F Ashby G. (1986).  Relationships between linear systems theory and covarian ce structure modeling. Journal of Mathematical Psychology. 30, 1–27.
F Ashby, G., & W Lee W. (1992).  On the relationship among identification, similarity, and categorization: Reply to Nosofsky and Smith (1992). Journal of Experimental Psychology: General. 121, 385-393.
F Ashby, G., & W Maddox T. (1993).  Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology. 37, 372–400.
Q
Filoteo, J. V., Maddox W. T., & Ashby F. G. (2017).  Quantitative modeling of category learning deficits in various patient populations. Neuropsychology. 31, 862-876.
P
Crossley, M. J., Madsen N. R., & F Ashby G. (2012).  Procedural learning of unstructured categories. Psychonomic Bulletin & Review. 19(6), 1202-1209.
F Ashby, G., Ell S. W., & Waldron E. M. (2003).  Procedural learning in perceptual categorization. Memory & Cognition. 31(7), 1114-1125.
Crossley, M. J., & F Ashby G. (2015).  Procedural learning during declarative control. Journal of Experimental Psychology: Learning, Memory & Cognition. 41(5), 1388-1403.
W Maddox, T., Prinzmetal W., Ivry R. B., & F Ashby G. (1994).  A probabilistic multidimensional model of location information. Psychological Research. 56, 66–77.
F Ashby, G., & O'Brien J. B. (2008).  The P_rep statistic as a measure of confidence in model fitting. Psychonomic Bulletin & Review. 15(1), 16-27.
F Ashby, G., & W Lee W. (1991).  Predicting similarity and categorization from identification. Journal of Experimental Psychology: General. 120, 150.
J Smith, D., F Ashby G., Berg M. E., Murphy M. S., Spiering B., Cook R. G., et al. (2011).  Pigeons' categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review. 18(2), 414-421.
F Ashby, G., & W Lee W. (1993).  Perceptual variability as a fundamental axiom of perceptual science. Advances in psychology. 99, 369–399.
W Maddox, T., & F Ashby G. (1996).  Perceptual separability, decisional separability, and the identification–speeded classification relationship. Journal of Experimental Psychology: Human perception and performance. 22, 795.
Townsend, J. T., Hu G. G., & F Ashby G. (1981).  Perceptual sampling of orthogonal straight line features. Psychological Research. 43, 259–275.
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.
F Ashby, G. (1992).  Pattern recognition by human and machine. Review of "Adaptive Pattern Recognition and Neural Networks", by Yoh-Han Pao. Journal of Mathematical Psychology. 36, 146-153.
O
F Ashby, G., W Maddox T., & Bohil C. J. (2002).  Observational versus feedback training in rule-based and information-integration category learning. Memory & Cognition. 30, 666–677.
N
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.
Rosedahl, L., & F Ashby G. (2018).  A New Stimulus Set for Cognitive Research.
Ashby, F. G., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. Cambridge University Press.
F Ashby, G., Isen A. M., & others (1999).  A neuropsychological theory of positive affect and its influence on cognition. Psychological Review. 106, 529-550.
F Ashby, G., Alfonso-Reese L. A., Waldron E. M., & others (1998).  A neuropsychological theory of multiple systems in category learning. Psychological Review. 105, 442-481.
F Ashby, G., & Waldron E. M. (2000).  The neuropsychological bases of category learning. Current Directions in Psychological Science. 9, 10–14.
F Ashby, G., & Helie S. (2011).  The neurodynamics of cognition: A tutorial on computational cognitive neuroscience. Journal of Mathematical Psychology. 55(4), 273-289.

Pages