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Found 180 results
Author Title [ Type(Desc)] Year
Journal Article
J Smith, D., Boomer J., Zakrzewski A. C., Roeder J. L., Church B. A., & F Ashby G. (2014).  Deferred feedback sharply dissociates implicit and explicit category learning. Psychological Science. 25(2), 447-457.
W Maddox, T., F Ashby G., & Bohil C. J. (2003).  Delayed feedback effects on rule-based and information-integration category learning. Journal of Experimental Psychology: Learning, Memory & Cognition. 29(4), 650-662.
F Ashby, G. (1982).  Deriving exact predictions from the cascade model. Psychological Review. 89, 599 607.
Ashby, F. G., Valentin V. V., & von Meer S. S. (2015).  Differential effects of dopamine-directed treatments on cognition. Neuropsychiatric Disease and Treatment. 11, 1859-1875.
Rosedahl, L., & Ashby F. G. (2019).  A difficulty predictor for perceptual category learning. Journal of Vision. 19(6), 20.
W Maddox, T., F Ashby G., A Ing D., & Pickering A. D. (2004).  Disrupting feedback processing interferes with rule-based but not information-integration category learning. Memory & Cognition. 32(4), 582-591.
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.
W Maddox, T., & F Ashby G. (2004).  Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioral Processes. 66(3), 309-332.
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.
F Ashby, G., Queller S., & Berretty P. M. (1999).  On the dominance of unidimensional rules in unsupervised categorization. Perception & Psychophysics. 61, 1178–1199.
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.
Ell, S. W., & F Ashby G. (2004).  Dynamical trajectories in category learning. Perception & Psychophysics. 66(8), 1318-1340.
Ell, S. W., & F Ashby G. (2006).  The effects of category overlap on information-integration and rule-based category learning. Perception & Psychophysics. 68(6), 1013-1026.
Waldron, E. M., & F Ashby G. (2001).  The effects of concurrent task interference on category learning: Evidence for multiple category learning systems. Psychonomic Bulletin & Review. 8, 168–176.
F Ashby, G., & O'Brien J. B. (2007).  The effects of positive versus negative feedback on information-integration category learning. Perception & Psychophysics. 69(6), 865-878.
Ashby, F. G., & Casale M. B. (2005).  Empirical dissociations between rule-based and similarity-based categorization. Behavioral and Brain Sciences. 28(1), 15-16.
Crossley, M. J., F Ashby G., & W Maddox T. (2013).  Erasing the engram: The unlearning of procedural skills. Journal of Experimental Psychology: General. 142(3), 710-741.
F Ashby, G. (1988).  Estimating the parameters of multidimensional signal detection theory from simultaneous ratings on separate stimulus components. Perception & Psychophysics. 44, 195–204.
Helie, S., Roeder J. L., & F Ashby G. (2010).  Evidence for cortical automaticity in rule-based categorization. Journal of Neuroscience. 30(42), 14225-14234.
Crossley, M. J., Horvitz J. C., Balsam P. D., & Ashby F. G. (2016).  Expanding the role of striatal cholinergic interneurons and the midbrain dopamine system in appetitive instrumental conditioning. Journal of Neurophysiology. 115(1), 240-54.
Townsend, J. T., & F Ashby G. (1982).  Experimental test of contemporary mathematical models of visual letter recognition.. Journal of Experimental Psychology: Human Perception and Performance. 8, 834.
F Ashby, G., & Waldschmidt J. G. (2008).  Fitting computational models to fMRI. Behavioral Research Methods. 40(3), 713-721.
F Ashby, G., Prinzmetal W., Ivry R., & W Maddox T. (1996).  A formal theory of feature binding in object perception. Psychological Review. 103, 165-192.
F Ashby, G., Ell S. W., Valentin V. V., & Casale M. B. (2005).  FROST: a distributed neurocomputational model of working memory maintenance. Journal of Cognitive Neuroscience. 17(11), 1728-1743.
Inglis, J. B., Bird J., & Ashby F. G. (2022).  A general recognition theory model for identifying an ideal stimulus. Attention, Perception, & Psychophysics. 84, 2408–2421.

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