15 found
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  1.  18
    ALCOVE: An Exemplar-Based Connectionist Model of Category Learning.John K. Kruschke - 1992 - Psychological Review 99 (1):22-44.
  2.  18
    Bayesian Estimation Supersedes the T Test.John K. Kruschke - 2013 - Journal of Experimental Psychology: General 142 (2):573-603.
  3.  3
    Rules and Exemplars in Category Learning.Michael A. Erickson & John K. Kruschke - 1998 - Journal of Experimental Psychology: General 127 (2):107-140.
  4. What to Believe: Bayesian Methods for Data Analysis.John K. Kruschke - 2010 - Trends in Cognitive Sciences 14 (7):293-300.
  5.  6
    Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting.John K. Kruschke - 2006 - Psychological Review 113 (4):677-699.
  6.  18
    Models of Categorization.John K. Kruschke - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 267--301.
  7.  12
    Population of Linear Experts: Knowledge Partitioning and Function Learning.Michael L. Kalish, Stephan Lewandowsky & John K. Kruschke - 2004 - Psychological Review 111 (4):1072-1099.
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  8.  12
    The Perception of Causality: Feature Binding in Interacting Objects.John K. Kruschke & Michael M. Fragassi - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. pp. 441--446.
  9.  5
    An Evolutionary Analysis of Learned Attention.Richard A. Hullinger, John K. Kruschke & Peter M. Todd - 2015 - Cognitive Science 39 (6):1172-1215.
    Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it (...)
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  10.  49
    Bridging Levels of Analysis: Comment on McClelland Et Al. And Griffiths Et Al.John K. Kruschke - 2010 - Trends in Cognitive Sciences 14 (8):344-345.
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  11.  5
    Are Rules and Instances Subserved by Separate Systems?Robert L. Goldstone & John K. Kruschke - 1994 - Behavioral and Brain Sciences 17 (3):405-405.
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  12. Learning of Rules That Have High-Frequency Exceptions: New Empirical Data and a Hybrid Connectionist Model.John K. Kruschke & Michael A. Erickson - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 514--519.
     
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  13.  7
    How Connectionist Models Learn: The Course of Learning in Connectionist Networks.John K. Kruschke - 1990 - Behavioral and Brain Sciences 13 (3):498-499.
  14.  5
    State Transitions in Constraint Satisfaction Networks.John K. Kruschke - 1989 - Behavioral and Brain Sciences 12 (3):407-408.
  15.  4
    Concept Learning and Categorization: Models.John K. Kruschke - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
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