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Found 34 results
Author Title Type [ Year(Desc)]
Filters: Author is Ashby, F G.  [Clear All Filters]
2000
Ashby, F. G. (2000).  A stochastic version of general recognition theory. Journal of Mathematical Psychology. 44, 310–329.
2015
Soto, F. A., & Ashby F. G. (2015).  Categorization training increases the perceptual separability of novel dimensions. Cognition. 139, 105-129.
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.
Soto, F. A., Vucovich L., Musgrave R., & Ashby F. G. (2015).  General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception. Psychonomic Bulletin & Review. 22(1), 88-111.
J Smith, D., Zakrzewski A. C., Johnston J. J. R., Roeder J. L., Boomer J., Ashby F. G., et al. (2015).  Generalization of category knowledge and dimensional categorization in humans (Homo sapiens) and nonhuman primates (Macaca mulatta). Journal of Experimental Psychology: Animal Learning & Cognition. 41(4), 322-335.
Ashby, F. G. (2015).  An introduction to fMRI. In B. U. Forstmann & E.-J. Wagenmakers (Eds.), An introduction to model-based cognitive neuroscience. 91–112.
Helie, S., Ell S. W., & Ashby F. G. (2015).  Learning robust cortico-cortical associations with the basal ganglia: an integrative review. Cortex. 64, 123-35.
Ashby, F. G., & Soto F. A. (2015).  Multidimensional signal detection theory. In: J. R. Busemeyer, Z. Wang, J. T. Townsend, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology. 13–34.
Cantwell, G., Crossley M. J., & Ashby F. G. (2015).  Multiple stages of learning in perceptual categorization: evidence and neurocomputational theory. Psychonomic Bulletin & Review. 22(6), 1598-1613.
Paul, E. J., J Smith D., Valentin V. V., Turner B. O., Barbey A. K., & Ashby F. G. (2015).  Neural networks underlying the metacognitive uncertainty response. Cortex. 71, 306-22.
2016
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.
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.
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.
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.
Ashby, F. G., & Soto F. A. (2016).  The neural basis of general recognition theory. In J. W. Houpt & L. M. Blaha (Eds.), Mathematical models of perception and cognition: A Festschrift for James T. Townsend (pp. 1 - 31). New York: Psychology Press. 1-31.
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.
Roeder, J. L., & Ashby F. G. (2016).  What is automatized during perceptual categorization?. Cognition. 154, 22-33.
2017
Turner, B. O., Crossley M. J., & Ashby F. G. (2017).  Hierarchical control of procedural and declarative category-learning systems. NeuroImage. 150, 150-161.
Ashby, F. G., & Valentin V. V. (2017).  Multiple systems of perceptual category learning: Theory and cognitive tests. In H. Cohen and C. Lefebvre (Eds.), Handbook of Categorization in Cognitive Science (Second Edition). 157–188.
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
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.
Filoteo, J. V., Maddox W. T., & Ashby F. G. (2017).  Quantitative modeling of category learning deficits in various patient populations. Neuropsychology. 31, 862-876.
Soto, F. A., Zheng E., Fonseca J., & Ashby F. G. (2017).  Testing separability and independence of perceptual dimensions with general recognition theory: A tutorial and new R package (grtools). Frontiers in Psychology. 8, 696.
Helie, S., Turner B. O., Crossley M. J., Ell S. W., & Ashby F. G. (2017).  Trial-by-trial identification of categorization strategy using iterative decision-bound modeling. Behavioral Research Methods. 49(3), 1146-1162.
2018
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-.

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