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Found 178 results
[ Author(Desc)] Title 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 
A
Ashby, F. G., & Rosedahl L. (2017).  A neural interpretation of exemplar theory. Psychological Review. 124(4), 472-482.
Ashby, F. G. (2019).  Statistical Analysis of fMRI Data, Second Edition. Cambridge, MA: MIT Press.
F Ashby, G., Valentin V. V., & Turken AU. (2002).  The effects of positive affect and arousal on working memory and executive attention. In S. Moore & M. Oaksford (Eds.), Emotional Cognition: From Brain to Behaviour. 245–288.
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.
Ashby, F. G., & Bamber D. (2022).  State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108, 102655.
Ashby, F. G., Crossley M. J., & Inglis J. B. (2023).  Mathematical models of human learning. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 163-217). Cambridge University Press.
Ashby, F. G., & Wenger M. J. (2023).  Statistical decision theory. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 265-310). Cambridge University Press.
Ashby, F. G., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. Cambridge University Press.
Ashby, F. G., & Wang Y-W. (2023).  Computational cognitive neuroscience models of categorization. In R. Sun (Ed.), The Cambridge Handbook of Computational Cognitive Sciences (pp.400-425). Cambridge University Press.
Ashby, F. G., & Casale M. B. (2005).  Empirical dissociations between rule-based and similarity-based categorization. Behavioral and Brain Sciences. 28(1), 15-16.
Ashby, F. G. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.
F Ashby, G., Tein J-Y., & Balakrishnan JD. (1993).  Response time distributions in memory scanning. Journal of Mathematical Psychology. 37, 526–555.
Ashby, F. G., Zetzer H. A., Conoley C. W., & Pickering A. D. (2024).  Just Do It: A Neuropsychological Theory of Agency, Cognition, Mood, and Dopamine. Journal of Experimental Psychology: General. in press.
B
Balakrishnan, JD., & F Ashby G. (1991).  Is subitizing a unique numerical ability?. Perception & Psychophysics. 50, 555–564.
Balakrishnanl, JD., & F Ashby G. (1992).  Subitizing: Magical numbers or mere superstition?. Psychological Research. 54, 80–90.
C
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.
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.
Casale, M. B., Roeder J. L., & F Ashby G. (2012).  Analogical transfer in perceptual categorization. Memory & Cognition. 40(3), 434-449.
Casale, M. B., & F Ashby G. (2008).  A role for the perceptual representation memory system in category learning. Perception & Psychophysics. 70(6), 983-999.
Crossley, M. J., & F Ashby G. (2015).  Procedural learning during declarative control. Journal of Experimental Psychology: Learning, Memory & Cognition. 41(5), 1388-1403.
Crossley, M. J., F Ashby G., & W Maddox T. (2014).  Context-dependent savings in procedural category learning. Brain and Cognition. 92C, 1-10.
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.
Crossley, M. J., Madsen N. R., & F Ashby G. (2012).  Procedural learning of unstructured categories. Psychonomic Bulletin & Review. 19(6), 1202-1209.
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.
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.

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