J K Kruschke

Summary

Affiliation: Indiana University
Country: USA

Publications

  1. doi request reprint Bayesian estimation supersedes the t test
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405 7007, USA
    J Exp Psychol Gen 142:573-603. 2013
  2. doi request reprint Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 7007, USA
    Br J Math Stat Psychol 66:45-56. 2013
  3. doi request reprint What to believe: Bayesian methods for data analysis
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th St, Bloomington, IN 47405 7007, USA
    Trends Cogn Sci 14:293-300. 2010
  4. ncbi request reprint Bayesian approaches to associative learning: from passive to active learning
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405 7007, USA
    Learn Behav 36:210-26. 2008
  5. ncbi request reprint Locally Bayesian learning with applications to retrospective revaluation and highlighting
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 7007, USA
    Psychol Rev 113:677-99. 2006
  6. ncbi request reprint Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting
    John K Kruschke
    Department of Psychology, Indiana University Bloomington, Bloomington, 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 31:830-45. 2005
  7. ncbi request reprint Base rates in category learning
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405, USA
    J Exp Psychol Learn Mem Cogn 22:3-26. 1996
  8. ncbi request reprint The inverse base-rate effect is not explained by eliminative inference
    J K Kruschke
    Department of Psychology, Indiana University Bloomington, 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 27:1385-400. 2001
  9. ncbi request reprint Blocking and backward blocking involve learned inattention
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405 7007, USA
    Psychon Bull Rev 7:636-45. 2000
  10. ncbi request reprint A model of probabilistic category learning
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 25:1083-119. 1999

Detail Information

Publications20

  1. doi request reprint Bayesian estimation supersedes the t test
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington, IN 47405 7007, USA
    J Exp Psychol Gen 142:573-603. 2013
    ..The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms...
  2. doi request reprint Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 7007, USA
    Br J Math Stat Psychol 66:45-56. 2013
    ..Practically, the conclusion cautions against use of the Bayesian p-value in favour of direct model expansion and Bayesian evaluation...
  3. doi request reprint What to believe: Bayesian methods for data analysis
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, 1101 E 10th St, Bloomington, IN 47405 7007, USA
    Trends Cogn Sci 14:293-300. 2010
    ....
  4. ncbi request reprint Bayesian approaches to associative learning: from passive to active learning
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405 7007, USA
    Learn Behav 36:210-26. 2008
    ..The Kalman filter predictions are disconfirmed in at least one case...
  5. ncbi request reprint Locally Bayesian learning with applications to retrospective revaluation and highlighting
    John K Kruschke
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405 7007, USA
    Psychol Rev 113:677-99. 2006
    ....
  6. ncbi request reprint Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting
    John K Kruschke
    Department of Psychology, Indiana University Bloomington, Bloomington, 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 31:830-45. 2005
    ..This predicted correlation is obtained for both choice and eye gaze. A connectionist model that implements attentional learning is shown to fit the data and account for individual differences by variation in its attentional parameters...
  7. ncbi request reprint Base rates in category learning
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405, USA
    J Exp Psychol Learn Mem Cogn 22:3-26. 1996
    ..Four new experiments provide evidence consistent with those principles. The principles are formalized in a new connectionist model that can rapidly shift attention to distinctive features...
  8. ncbi request reprint The inverse base-rate effect is not explained by eliminative inference
    J K Kruschke
    Department of Psychology, Indiana University Bloomington, 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 27:1385-400. 2001
    ..A connectionist implementation of attentional theory fits the data well. The author concludes that people can use eliminative inference but that it cannot account for the inverse base-rate effect...
  9. ncbi request reprint Blocking and backward blocking involve learned inattention
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405 7007, USA
    Psychon Bull Rev 7:636-45. 2000
    ..The results are predicted by the hypothesis that people learn not to attend to the blocked cue...
  10. ncbi request reprint A model of probabilistic category learning
    J K Kruschke
    Department of Psychology, Indiana University, Bloomington 47405 7007, USA
    J Exp Psychol Learn Mem Cogn 25:1083-119. 1999
    ..The model formalizes 3 explanatory principles: rapidly shifting attention with learned shifts, decreasing learning rates, and graded similarity in exemplar representation...
  11. ncbi request reprint Single-system models and interference in category learning: commentary on Waldron and Ashby (2001)
    Robert M Nosofsky
    Department of Psychology, Indiana University, Bloomington 47405, USA
    Psychon Bull Rev 9:169-74; discussion 175-80. 2002
    ..In contrast to Waldron and Ashby's argument, we demonstrate that the single-system ALCOVE model (Kruschke, 1992) naturally predicts the result by assuming that its selective-attention learning process is disrupted by the concurrent task...
  12. ncbi request reprint Rule-based extrapolation: a continuing challenge for exemplar models
    Stephen E Denton
    Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405 7007, USA
    Psychon Bull Rev 15:780-6. 2008
    ..Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models...
  13. ncbi request reprint Decision boundaries in one-dimensional categorization
    M L Kalish
    Department of Psychology, Indiana University Bloomington, USA
    J Exp Psychol Learn Mem Cogn 23:1362-77. 1997
    ..When combined with the results of previous research, this suggests that a comprehensive model of categorization must involve both rules and exemplars, and possibly other representations as well...
  14. ncbi request reprint Rules and exemplars in category learning
    M A Erickson
    Department of Psychology, Indiana University Bloomington, USA
    J Exp Psychol Gen 127:107-40. 1998
    ..A key element in correctly modeling these results was capturing the interaction between the rule- and exemplar-based representations by using shifts of attention between rules and exemplars...
  15. ncbi request reprint Attention and salience in associative blocking
    Stephen E Denton
    Department of Psychological and Brain Sciences, 1101 E 10th St, Indiana University, Bloomington, IN 47405 7007, USA
    Learn Behav 34:285-304. 2006
    ..A connectionist model that implements attentional learning is shown to fit the main trends in the data. Model comparisons suggest that mere forgetting, implemented as weight decay, cannot explain the results...
  16. ncbi request reprint Category representation for classification and feature inference
    Mark K Johansen
    Department of Psychology, Indiana University Bloomington, Bloomington, IN, USA
    J Exp Psychol Learn Mem Cogn 31:1433-58. 2005
    ..Only the set of rules model accounted for all the inference learning conditions in these experiments...
  17. ncbi request reprint Using cognitive science methods to assess the role of social information processing in sexually coercive behavior
    T A Treat
    Department of Psychology, Indiana University, Bloomington, USA
    Psychol Assess 13:549-65. 2001
    ..Overall, the study demonstrates the feasibility and utility of cognitive science methods for studying information processing in psychopathology...
  18. doi request reprint Attentional processes in stereotype formation: a common model for category accentuation and illusory correlation
    Jeffrey W Sherman
    Department of Psychology, University of California, Davis, CA 95616, USA
    J Pers Soc Psychol 96:305-23. 2009
    ..Implications for the natures of stereotype formation, illusory correlation, and impression formation are discussed...
  19. ncbi request reprint Population of linear experts: knowledge partitioning and function learning
    Michael L Kalish
    Institute of Cognitive Science, University of Louisiana at Lafayette, Lafayette, LA 70504 3772, USA
    Psychol Rev 111:1072-99. 2004
    ..POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction...
  20. ncbi request reprint Rule-based extrapolation in perceptual categorization
    Michael A Erickson
    Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Psychon Bull Rev 9:160-8. 2002
    ..Moreover, four alternate exemplar explanations, including one suggested by Nosofsky and Johansen, cannot account for our new findings...