Publications

Reprint Requests

Contact Form

Found 178 results
[ Author(Asc)] 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 
E
Ell, S. W., & F Ashby G. (2004).  Dynamical trajectories in category learning. Perception & Psychophysics. 66(8), 1318-1340.
C
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.
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.
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.
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.
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.
B
Balakrishnanl, JD., & F Ashby G. (1992).  Subitizing: Magical numbers or mere superstition?. Psychological Research. 54, 80–90.
Balakrishnan, JD., & F Ashby G. (1991).  Is subitizing a unique numerical ability?. Perception & Psychophysics. 50, 555–564.
A
F Ashby, G., & Crossley M. J. (2011).  A computational model of how cholinergic interneurons protect striatal-dependent learning.. J Cogn Neurosci. 23(6), 1549-66.
F Ashby, G., & Townsend J. T. (1980).  Decomposing the reaction time distribution: Pure insertion and selective influence revisited. Journal of Mathematical Psychology. 21, 93–123.
F Ashby, G. (1982).  Testing the assumptions of exponential, additive reaction time models. Memory & Cognition. 10, 125–134.
F Ashby, G. (1983).  A biased random walk model for two choice reaction times. Journal of Mathematical Psychology. 27, 277–297.
F Ashby, G. (1987).  Counting and timing models in psychophysics and the conjoint Weber's law. Journal of Mathematical Psychology. 31, 419–428.
F Ashby, G., & W Maddox T. (1991).  A response time theory of perceptual independence. Mathematical Psychology. 389–413.
F Ashby, G., W Lee W., & Balakrishnan JD. (1992).  Comparing the biased choice model and multidimensional decision bound models of identification. Mathematical Social Sciences. 23, 175–197.
F Ashby, G., & W Maddox T. (1993).  Relations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology. 37, 372–400.
F Ashby, G., & W Lee W. (1993).  Perceptual variability as a fundamental axiom of perceptual science. Advances in psychology. 99, 369–399.
F Ashby, G., W Maddox T., & W Lee W. (1994).  On the dangers of averaging across subjects when using multidimensional scaling or the similarity-choice model. Psychological Science. 5, 144–151.

Pages