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Found 34 results
Author Title Type [ Year(Desc)]
Filters: Author is Ashby, F G.  [Clear All Filters]
2018
Ashby, F. G. (2018).  Computational cognitive neuroscience. In W. Batchelder, H. Colonius, E. Dzhafarov, & J. Myung (Eds.), New handbook of mathematical psychology, Volume 2. NY: Cambridge University Press. 223-270.
Crossley, M. J., Maddox W. T., & Ashby F. G. (2018).  Increased cognitive load enables unlearning in procedural category learning. Journal of Experimental Psychology: Learning, Memory, & Cognition. 44, 1845-1853.
Soto, F. A., Vucovich L. E., & Ashby F. G. (2018).  Linking signal detection theory and encoding models to reveal independent neural representations from neuroimaging data. PLOS Computational Biology. 14(10), e1006470.
Crossley, M. J., Roeder J. L., Helie S., & Ashby F. G. (2018).  Trial-by-trial switching between procedural and declarative categorization systems. Psychological Research. 82, 371-384.
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.
Ashby, F. G. (2019).  Statistical Analysis of fMRI Data, Second Edition. Cambridge, MA: MIT Press.
Qadri, M. A. J., Ashby F. G., Smith J. D., & Cook R. G. (2019).  Testing analogical rule transfer in pigeons (Columba livia). Cognition. 183, 256-268.

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