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Found 184 results
Author [ Title(Desc)] 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 
P
W Maddox, T., Prinzmetal W., Ivry R. B., & F Ashby G. (1994).  A probabilistic multidimensional model of location information. Psychological Research. 56, 66–77.
Crossley, M. J., & F Ashby G. (2015).  Procedural learning during declarative control. Journal of Experimental Psychology: Learning, Memory & Cognition. 41(5), 1388-1403.
F Ashby, G., Ell S. W., & Waldron E. M. (2003).  Procedural learning in perceptual categorization. Memory & Cognition. 31(7), 1114-1125.
Crossley, M. J., Madsen N. R., & F Ashby G. (2012).  Procedural learning of unstructured categories. Psychonomic Bulletin & Review. 19(6), 1202-1209.
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.
R
F Ashby, G., & W Maddox T. (1993).  Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology. 37, 372–400.
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.
MacCallum, R., & F Ashby G. (1986).  Relationships between linear systems theory and covarian ce structure modeling. Journal of Mathematical Psychology. 30, 1–27.
Ennis, D. M., & F Ashby G. (1993).  The relative sensitivities of same-different and identification judgment models to perceptual dependence. Psychometrika. 58, 257–279.
Spiering, B. J., & F Ashby G. (2008).  Response processes in information-integration category learning. Neurobiology of Learning and Memory. 90(2), 330-338.
F Ashby, G., Tein J-Y., & Balakrishnan JD. (1993).  Response time distributions in memory scanning. Journal of Mathematical Psychology. 37, 526–555.
W Maddox, T., F Ashby G., & Gottlob L. R. (1998).  Response time distributions in multidimensional perceptual categorization. Perception & Psychophysics. 60, 620–637.
F Ashby, G., & W Maddox T. (1991).  A response time theory of perceptual independence. Mathematical Psychology. 389–413.
F Ashby, G., & W Maddox T. (1994).  A response time theory of separability and integrality in speeded classification. Journal of Mathematical Psychology. 38, 423–466.
Rünger, D., F Ashby G., Picard N., & Strick P. L. (2013).  Response-mode shifts during sequence learning of macaque monkeys. Psychological Research. 77(2), 223-233.
F Ashby, G. (1995).  Resurrecting information theory: A review of "Information, Sensation, and Perception," by Kenneth H. Norwich. The American Journal of Psychology. 108, 609-614.
Rosedahl, L. A., Eckstein M. P., & F Ashby G. (2018).  Retinal-specific category learning. Nature Human Behaviour. 1.
Wang, Y-W., & Ashby F. G. (2020).  A role for the medial temporal lobes in category learning. Learning & Memory. 27, 441-450.
Casale, M. B., & F Ashby G. (2008).  A role for the perceptual representation memory system in category learning. Perception & Psychophysics. 70(6), 983-999.
Ashby, F. G., & Vucovich L. E. (2016).  The role of feedback contingency in perceptual category learning. Journal of Experimental Psychology: Learning, Memory & Cognition. 42(11), 1731-1746.
S
W Maddox, T., & F Ashby G. (1998).  Selective attention and the formation of linear decision boundaries: Comment on McKinley and Nosofsky (1996). Journal of Experimental Psychology: Human Perception & Performance. 24, 301-321.
Ashby, F. G., & Ashby G. Z. (2026).  Sense of agency and addiction. Frontiers in Psychology. 1725049.
Ashby, F. G. (In Press).  Sense of agency and decision making. Frontiers in Psychology.
J Filoteo, V., Paul E. J., F Ashby G., Frank G. K. W., Helie S., Rockwell R., et al. (2014).  Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa. Neuropsychology. 28(5), 741-51.
Helie, S., Paul E. J., & F Ashby G. (2012).  Simulating the effects of dopamine imbalance on cognition: From positive affect to Parkinson's disease. Neural Networks. 32, 74-85.

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