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Found 11 results
Author [ Title(Desc)] Type Year
Filters: Author is Valentin, Vivian V  [Clear All Filters]
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C
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-.
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.
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.
D
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.
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.
E
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.
F
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.
M
Inglis, J. B., Valentin V. V., & F Ashby G. (2020).  Modulation of dopamine for adaptive learning: A neurocomputational model. Computational Brain & Behavior. 1–19.
F Ashby, G., & Valentin V. V. (2005).  Multiple systems of perceptual category learning: Theory and cognitive tests. Handbook of categorization in cognitive science. 547–572.
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.
N
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.