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Found 184 results
Author Title Type [ Year(Asc)]
2026
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.
Ashby, F. G., & Ashby G. Z. (2026).  Sense of agency and addiction. Frontiers in Psychology. 1725049.
Ashby, F. G. (2026).  Sense of agency and decision making. Frontiers in Psychology. 17, 1816045.
Ashby, F. G. (2026).  Statistical decision theory in perception and cognition: Signal detection & general recognition theories. MIT Press.
Crossley, M. J., Pelzer B. O., & Ashby F. G. (2026).  Is there a criterion in criterial learning? Insights from studying feedback delays. Journal of Experimental Psychology: Learning, Memory, and Cognition. Advance online publication, https://dx.doi.org/10.1037/xlm0001590.
2024
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. 153(6), 1582–1604.
Ashby, F. G. (2024).  On using the fixed-point property of binary mixtures to discriminate among models of recognition memory. Journal of Mathematical Psychology. 123, 102889.
2023
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.
Soto, F. A., & Ashby F. G. (2023).  Encoding models in neuroimaging. In F. G. Ashby, H. Colonius, & E. Dzhafarov (Eds.), The new handbook of mathematical psychology, Volume 3 (pp. 421-472). Cambridge University Press.
Ashby, F. G. (2023).  Length of the state trace: A method for partitioning model complexity. Journal of Mathematical Psychology. 113, 102755.
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., Colonius H., & Dzhafarov E. (2023).  The new handbook of mathematical psychology, Volume 3. Perceptual and Cognitive Processes. 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.
2022
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.
Rosedahl, L. A., & Ashby F. G. (2022).  Linear separability, irrelevant variability, and categorization difficulty. Journal of Experimental Psychology: Learning, Memory, & Cognition. 48, 159-172.
Ashby, F. G., & Bamber D. (2022).  State trace analysis: What it can and cannot do. Journal of Mathematical Psychology. 108, 102655.
Kovacs, P., & Ashby F. G. (2022).  On what it means to automatize a rule. Cognition. 226, 105168.
2021
Kovacs, P., Helie S., Tran A. N., & Ashby F. G. (2021).  A neurocomputational theory of how rule-guided behaviors become automatic. Psychological Review. 128, 488-508.
Rosedahl, L. A., Serota R., & Ashby F. G. (2021).  When instructions don't help: Knowing the optimal strategy facilitates rule-based but not information-integration category learning. Journal of Experimental Psychology: Human Perception & Performance. 47, 1226-1236.
2020
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.
Inglis, J. B., Valentin V. V., & F Ashby G. (2020).  Modulation of dopamine for adaptive learning: A neurocomputational model. Computational Brain & Behavior. 1–19.
Wang, Y-W., & Ashby F. G. (2020).  A role for the medial temporal lobes in category learning. Learning & Memory. 27, 441-450.
2019
Rosedahl, L., & Ashby F. G. (2019).  A difficulty predictor for perceptual category learning. Journal of Vision. 19(6), 20.
Soto, F. A., & Ashby F. G. (2019).  Novel representations that support rule-based categorization are acquired on-the-fly during category learning. Psychological Research. 83, 544-566.
Ashby, F. G. (2019).  State-trace analysis misinterpreted and misapplied: Reply to Stephens, Matzke, and Hayes (2019). Journal of Mathematical Psychology. 91, 195-200.

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