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Found 18 results
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J Smith, D., Zakrzewski A. C., Johnston J. J. R., Roeder J. L., Boomer J., Ashby F. G., et al. (2015).  Generalization of category knowledge and dimensional categorization in humans (Homo sapiens) and nonhuman primates (Macaca mulatta). Journal of Experimental Psychology: Animal Learning & Cognition. 41(4), 322-335.
J Smith, D., Beran M. J., Crossley M. J., Boomer J., & F Ashby G. (2010).  Implicit and explicit category learning by macaques (Macaca mulatta) and humans (Homo sapiens). Journal of Experimental Psychology: Animal Behavior Processes. 36(1), 54-65.
J Smith, D., F Ashby G., Berg M. E., Murphy M. S., Spiering B., Cook R. G., et al. (2011).  Pigeons' categorization may be exclusively nonanalytic. Psychonomic Bulletin & Review. 18(2), 414-421.
J Smith, D., Crossley M. J., Boomer J., Church B. A., Beran M. J., & F Ashby G. (2012).  Implicit and explicit category learning by capuchin monkeys (Cebus apella). Journal of Comparative Psychology. 126(3), 294-304.
J Smith, D., Berg M. E., Cook R. G., Murphy M. S., Crossley M. J., Boomer J., et al. (2012).  Implicit and explicit categorization: A tale of four species. Neuroscience and Biobehavioral Reviews. 36(10), 2355-2369.
J Smith, D., Boomer J., Zakrzewski A. C., Roeder J. L., Church B. A., & F Ashby G. (2014).  Deferred feedback sharply dissociates implicit and explicit category learning. Psychological Science. 25(2), 447-457.
J Smith, D., Johnston J. J. R., Musgrave R. D., Zakrzewski A. C., Boomer J., Church B. A., et al. (2014).  Cross-modal information integration in category learning. Attention, Perception, & Psychophysics. 76(5), 1473-1484.
J Smith, D., Zakrzewski A. C., Herberger E. R., Boomer J., Roeder J. L., F Ashby G., et al. (2015).  The time course of explicit and implicit categorization. Attention, Perception, & Psychophysics. 77(7), 2476-2490.
Soto, F. A., Waldschmidt J. G., Helie S., & F Ashby G. (2013).  Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis. NeuroImage. 71, 284-297.
Soto, F. A., Vucovich L., Musgrave R., & Ashby F. G. (2015).  General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception. Psychonomic Bulletin & Review. 22(1), 88-111.
Soto, F. A., Zheng E., Fonseca J., & Ashby F. G. (2017).  Testing separability and independence of perceptual dimensions with general recognition theory: A tutorial and new R package (grtools). Frontiers in Psychology. 8, 696.
Soto, F. A., Bassett D. S., & Ashby F. G. (2016).  Dissociable changes in functional network topology underlie early category learning and development of automaticity. NeuroImage. 141, 220-241.
Soto, F. A., & Ashby F. G. (2015).  Categorization training increases the perceptual separability of novel dimensions. Cognition. 139, 105-129.
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.
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.
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.
Spiering, B. J., & F Ashby G. (2008).  Initial training with difficult items facilitates information integration, but not rule-based category learning. Psychological Science. 19(11), 1169-1177.
Spiering, B. J., & F Ashby G. (2008).  Response processes in information-integration category learning. Neurobiology of Learning and Memory. 90(2), 330-338.