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Found 185 results
Author Title [ Type(Asc)] Year
Journal Article
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.
W Maddox, T., & F Ashby G. (2004).  Dissociating explicit and procedural-learning based systems of perceptual category learning. Behavioral Processes. 66(3), 309-332.
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., 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.
Rosedahl, L., & Ashby F. G. (2019).  A difficulty predictor for perceptual category learning. Journal of Vision. 19(6), 20.
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.
F Ashby, G. (1982).  Deriving exact predictions from the cascade model. Psychological Review. 89, 599 607.
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.
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.
Mumford, J. A., Turner B. O., F Ashby G., & Poldrack R. A. (2012).  Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage. 59(3), 2636-2643.
F Ashby, G., & Townsend J. T. (1980).  Decomposing the reaction time distribution: Pure insertion and selective influence revisited. Journal of Mathematical Psychology. 21, 93–123.
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.
F Ashby, G., & Gott R. E. (1988).  Decision rules in the perception and categorization of multidimensional stimuli. Journal of Experimental Psychology: Learning, Memory, and Cognition. 14, 33.
F Ashby, G., W Maddox T., & W Lee W. (1994).  On the dangers of averaging across subjects when using multidimensional scaling or the similarity-choice model. Psychological Science. 5, 144–151.
J Smith, D., Johnston J. J. R., Musgrave R. D., Zakrzewski A. C., Boomer J., Church B. A., et al. (2014).  Cross-modal information integration in category learning. Attention, Perception, & Psychophysics. 76(5), 1473-1484.
F Ashby, G. (1987).  Counting and timing models in psychophysics and the conjoint Weber's law. Journal of Mathematical Psychology. 31, 419–428.
Waldschmidt, J. G., & F Ashby G. (2011).  Cortical and striatal contributions to automaticity in information-integration categorization.. Neuroimage. 56(3), 1791-802.
F Ashby, G., Turner B. O., & Horvitz J. C. (2010).  Cortical and basal ganglia contributions to habit learning and automaticity. Trends in Cognitive Science. 14(5), 208-215.
Crossley, M. J., F Ashby G., & W Maddox T. (2014).  Context-dependent savings in procedural category learning. Brain and Cognition. 92C, 1-10.
Chen, Y., Zada Z., Nastase S. A., Ashby F. G., & Ghosh S. S. (2026).  Context modulates brain state dynamics and behavioral responses during narrative comprehension. Imaging Neuroscience. 4, https://doi.org/10.1162/IMAG.a.1116.
Valentin, V. V., W Maddox T., & F Ashby G. (2014).  A computational model of the temporal dynamics of plasticity in procedural learning: Sensitivity to feedback timing. Frontiers in Psychology. 5, 643.
F Ashby, G., & Crossley M. J. (2011).  A computational model of how cholinergic interneurons protect striatal-dependent learning.. J Cogn Neurosci. 23(6), 1549-66.
F Ashby, G., & Valentin V. V. (2007).  Computational cognitive neuroscience: Building and testing biologically plausible computational models of neuroscience, neuroimaging, and behavioral data. Statistical and process models for cognitive neuroscience and aging. 15–58.
F Ashby, G., & W Maddox T. (1992).  Complex decision rules in categorization: Contrasting novice and experienced performance. Journal of Experimental Psychology: Human Perception and Performance. 18, 50.
F Ashby, G., W Lee W., & Balakrishnan JD. (1992).  Comparing the biased choice model and multidimensional decision bound models of identification. Mathematical Social Sciences. 23, 175–197.

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