Some Writings John K. Kruschke

Doing Bayesian Data Analysis

These documents are protected by various copyright laws, but in each case I am allowed to distribute copies to individuals for personal, research use. Your click on any of the links below constitutes your request to me for a personal copy of the linked article, and my delivery of a personal copy. Any other use is prohibited.

Kruschke, J. K. (2013). Bayesian estimation supersedes the t  test. Journal of Experimental Psychology: General, 142(2), 573-603. (doi: 10.1037/a0029146) Software for the article is available here. Watch the video, and this additional video that includes discussion of sequential testing. (Both presented at the 2012 Psychonomic Society meeting.) Learn Bayesian data analysis from the book and the blog!

Kruschke, J. K. (2013). Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, ‘Philosophy and the practice of Bayesian statistics’. British Journal of Mathematical and Statistical Psychology, 66, 45-56. (Notice minor errata on p. 50.) (doi: 10.1111/j.2044-8317.2012.02063.x)

Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The time has come: Bayesian methods for data analysis in the organizational sciences. Organizational Research Methods, 15(4), 722-752. (doi: 10.1177/1094428112457829) Software for the article is available here. Learn Bayesian data analysis from the book and the blog!

George, D. N., & Kruschke, J. K. (2012). Contextual modulation of attention in human category learning. Learning and Behavior, 40, 530-541. (doi:10.3758/s13420-012-0072-8)

Kruschke, J. K. (2011). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6(3), 299-312. (The PDF linked above has a correction; the published version was missing Equation 1. The erroneous published version can be found at doi:10.1177/1745691611406925.) For another example of the advantage of Bayesian estimation over the Bayes factor approach, see this blog post. Kruschke, J. K. (2011). Introduction to special section on Bayesian data analysis. Perspectives on Psychological Science, 6(3), 272-273. (doi:10.1177/1745691611406926). Learn Bayesian data analysis from the book and the blog!

Trueblood, J. S., Kachergis, G., & Kruschke, J. K. (2011). A cue-imputation Bayesian model of information aggregation. In: L. Carlson, C. H¨olscher, and T. F. Shipley (Eds.), Proceedings of the Cognitive Science Society, pp. 1298–1303. (ISBN 978-0-9768318-7-7)

Kruschke, J. K. (2011). Models of attentional learning. In: E. M. Pothos and A. J. Wills (eds.), Formal Approaches in Categorization, pp. 120-152. Cambridge University Press. If you're looking for a quick summary of my research regarding attention in learning, here's the thing to read first, except that it's light on the most recent work. Other brief summaries include Kruschke, J. K. (2006, ICDL conference), Kruschke, J. K. (2003, Current Directions) and the penultimate section (p. 852+) of Kruschke (2001, J. Math. Psych.) .

Collins, E. C., Percy, E. J., Smith, E. R., & Kruschke, J. K. (2011). Integrating Advice and Experience: Learning and Decision Making With Social and Nonsocial Cues. Journal of Personality and Social Psychology, 100(6), 967-982. (doi:10.1037/a0022982)

Treat, T. A., Viken, R. J., Kruschke, J. K., & McFall, R. M. (2011). Men's Memory for Women's Sexual-interest and Rejection Cues. Applied Cognitive Psychology, 25, 802–810. (doi:10.1002/acp.1751)

Treat, T. A., Kruschke, J. K., Viken, R. J., & McFall, R. M. (2011). Application of associative learning paradigms to clinically relevant individual differences in cognitive processing. In: T. Schachtman & S. Reilly (Eds.), Associative learning and Conditioning Theory: Human and Non-Human Applications, ch. 17, pp. 376–398. Oxford, UK: Oxford University Press.

Kruschke, J. K. (2010). Bridging levels of analysis: comment on McClelland et al. and Griffiths et al. Trends in Cognitive Sciences, 14(8), 344-345. (doi:10.1016/j.tics.2010.05.007)

Jacobs, R. A. & Kruschke, J. K. (2010). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 8-21. (doi:10.1002/wcs.80)

Kruschke, J. K. (2010). Bayesian data analysis. Wiley Interdisciplinary Reviews: Cognitive Science, 1(5), 658-676. (doi:10.1002/wcs.72) This article emphasizes a critical flaw of p-values in null hypothesis significance testing, and two different Bayesian approaches to assessing null values. Learn Bayesian data analysis from the book and the blog!

Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293-300. (doi:10.1016/j.tics.2010.05.001) This article emphasizes advantages of Bayesian data analysis and the fact that Bayesian data analysis is appropriate regardless of the status of Bayesian models of cognition. Learn Bayesian data analysis from the book and the blog!

Kruschke, J. K., and Hullinger, R. A. (2010). Evolution of attention in learning. In: N. A. Schmajuk (Ed.), Computational Models of Conditioning, pp. 10-52. Cambridge University Press.

Kruschke, J. K., and Denton, S. E. (2010). Backward blocking of relevance-indicating cues: Evidence for locally Bayesian learning. In: C. J. Mitchell and M. E. LePelley (Eds.), Attention and Learning: From Brain to Behaviour, pp. 278-304. Oxford, UK: Oxford University Press. ISBN 9780199550531.

Bishara, A. J., Kruschke, J. K., Stout, J. C., Bechara, A., McCabe, D. P., and Busemeyer, J. R. (2010). Sequential learning models for the Wisconsin card sort task: Assessing processes in substance dependent individuals. Journal of Mathematical Psychology, 54, 5-13.

Treat, T. A., Viken, R. J., Kruschke, J. K., and McFall, R. M. (2010). Role of attention, memory, and covariation-detection processes in clinically significant eating-disorder symptoms. Journal of Mathematical Psychology, 54, 184-195.

Kruschke, J. K. (2009). Highlighting: A canonical experiment. In: B. Ross (Ed.), The Psychology of Learning and Motivation, 51, 153-185. Raw data from experiment (.txt file). Posterior distributions from Bayesian data analysis (.zip file). First save this zip file, then extract it to obtain 11 Rdata files, one for each of the 11 test items. To open an Rdata file, invoke R (see below) and then load an Rdata file to access a matrix named alphaMat. This matrix has 4 columns corresponding to αE,αL,αEo,αLo. The matrix has 5000 rows, for the 5000 steps in the MCMC chain. The 11 data files have the same matrix name, so rename each matrix as you load it! Experiment program (.R file). Runs as-is in R (in Windows). You do not need this file to examine the data or posterior distributions. R? It's a general purpose programming language, popular among statisticians. It's available free: http://www.r-project.org/. Learn Bayesian data analysis from the book and the blog!

Sherman, J. W., Kruschke, J. K., Sherman, S. J., Percy, E. J., Petrocelli, J. V., and Conrey, F. R. (2009). Attentional processes in stereotype formation: A common model for category accentuation and illusory correlation. Journal of Personality and Social Psychology, 96(2), 305-323. Winner of ISCON Best Social Cognition Paper.

Kruschke, J. K. (2008). Bayesian approaches to associative learning: From passive to active learning. Learning & Behavior, 36(3), 210-226. Overhead slides for accompanying talk.

Denton, S. E., Kruschke, J. K., and Erickson, M. A. (2008). Rule-based extrapolation: A continuing challenge for exemplar models. Psychonomic Bulletin & Review, 15(4), 780-786.

Kruschke, J. K. (2008). Models of categorization. In: R. Sun (Ed.), The Cambridge Handbook of Computational Psychology, pp. 267-301. New York: Cambridge University Press.

Treat, T. A., McFall, R. M., Viken, R. J., Kruschke, J. K., Nosofsky, R. M., and Wang, S. S. (2007). Clinical cognitive science: Applying quantitative models of cognitive processing to examine cognitive aspects of psychopathology. In: R. W. J. Neufeld (Ed.), Advances in Clinical Cognitive Science: Formal Modeling of Processes and Symptoms, pp. 179-205. Washington DC: American Psychological Association.

Kruschke, J. K. (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113(4), 677-699. (doi:10.1037/0033-295X.113.4.677) Presentation regarding these ideas at the UCLA IPAM Graduate Summer School, July 2007: Overhead slides (PDF format). (save the file before trying to open it) Real Audio video recording of presentation. Matlab code for computer simulations in this article: Main program. Script with various experiment paradigms; calls main program.

Kruschke, J. K. (2006). Locally Bayesian learning. Proceedings of the Annual Conference of the Cognitive Science Society. This conference paper reports simulations of the Kalman filter and rational model that were not included in the journal article above.

Kruschke, J. K. (2006). Learned Attention. Presentation at the Fifth International Conference on Development and Learning, Indiana University May 31-June 3, 2006.

Denton, S. E., and Kruschke, J. K. (2006). Attention and salience in associative blocking. Learning & Behavior, 34(3), 285-304.

Johansen, M. K., and Kruschke, J. K. (2005). Category representation for classification and feature inference. Journal of Experimental Psychology: Learning, Memory & Cognition, 31(6), 1433-1458.

Kruschke, J. K., Kappenman, E. S. & Hetrick, W. P. (2005). Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting. Journal of Experimental Psychology: Learning, Memory & Cognition, 31(5), 830-845.

Kruschke, J. K. (2005). Learning involves attention. In: G. Houghton (Ed.), Connectionist Models in Cognitive Psychology, Ch. 4, pp. 113-140. Hove, East Sussex, UK: Psychology Press. This chapter (above) reports two new experiments about learning after highlighting and blocking. For related work about learning of changed cue relevance, see Kruschke & Blair (2000) and Kruschke (1996, Connection Science).

Kruschke, J. K. (2005). Category Learning. In: K. Lamberts and R. L. Goldstone (Eds.), The Handbook of Cognition, Ch. 7, pp. 183-201. London: Sage.

Kalish, M. L., Lewandowsky, S., and Kruschke, J. K. (2004). Population of linear experts: Knowledge partitioning and function learning. Psychological Review, 111(4), 1072-1099. (doi:10.1037/0033-295X.111.4.1072) This article (above) reports new experiments in function learning with data addressed by a mixture of experts model. For related work involving mixture of experts models, see Erickson & Kruschke(2002) , Kruschke (2001, JMP) , Erickson & Kruschke (1998) , and Kruschke & Erickson (1994) . For related experiments showing multimodal responses in function learning, see Kruschke (2001 conference).

Kruschke, J. K. (2003). Attentional theory is a viable explanation of the inverse base rate effect: A reply to Winman, Wennerholm, and Juslin (2003). Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 1396-1400.

Kruschke, J. K. (2003). Attention in learning. Current Directions in Psychological Science, 12, 171-175.

Kruschke, J. K. (2003). Statistical methods: Overview. Macmillan Encyclopedia of Cognitive Science, Vol. 4, 225-232. London: Macmillan Publishers Ltd. I like this brief introduction to null hypothesis significance testing (NHST), but at the time of writing it I had only begun to learn about Bayesian methods. I now firmly believe that Bayesian methods are far superior to NHST, and NHST ought to be abandoned. I now teach only Bayesian methods.

Treat, T. A., McFall, R. M., Viken, R. J., Nosofsky, R. M., MacKay, D. B., & Kruschke, J. K. (2002). Assessing clinically relevant perceptual organization with multidimensional scaling techniques. Psychological Assessment, 14(3), 239-252.

Erickson, M. A. & Kruschke, J. K. (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9(1), 160-168.

Nosofsky, R. M. & Kruschke, J. K. (2002). Single-system models and interference in category learning: Commentary on Waldron and Ashby (2001). Psychonomic Bulletin & Review, 9(1), 169-174.

Kruschke, J. K. (2001). Toward a unified model of attention in associative learning. Journal of Mathematical Psychology, 45, 812-863.

Kruschke, J. K. (2001). The inverse base rate effect is not explained by eliminative inference. Journal of Experimental Psychology: Learning, Memory & Cognition, 27, 1385-1400.

Treat, T. A., McFall, R. M., Viken, R. J. & Kruschke, J. K. (2001). Using cognitive science methods to assess the role of social information processing in sexually coercive behavior. Psychological Assessment, 13(4), 549-565.

Kruschke, J. K. (2001). Cue competition in function learning: Blocking and highlighting. Presented at the 3rd International Conference on Memory, July 2001, Valencia, Spain. This article (above) reports that highlighting and blocking occur for continous cues and continuous outcomes. Therefore, models of funtion learning should also incorporate attention shifting mechanisms.

Kruschke, J. K. & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636-645. This article (above) reports experiments about learning after blocking. For related work regarding learning after blocking and highlighting, see the Kruschke (2005) chapter: Learning involves attention. For other work regarding transfer of learning across a relevance shift, see Kruschke (1996) Connection Science.

Kalish, M. L. & Kruschke, J. K. (2000). The role of attention shifts in the categorization of continuous dimensioned stimuli. Psychological Research, 64, 105-116.

Kruschke, J. K., & Johansen, M. K. (1999). A Model of Probabilistic Category Learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 25, 1083-1119.

Kruschke, J. K., Johansen, M. K., & Blair, N. J. (June 14, 1999). Exemplar model account of inference learning: Comment on Yamauchi and Markman (1998). Unpublished manuscript..

Dennis, S. & Kruschke, J. K. (1998). Shifting attention in cued recall. Australian Journal of Psychology, 50, 131-138..

Fagot, J., Kruschke, J. K., Depy, D., & Vauclair, J. (1998). Associative learning in humans (Homo sapiens) and baboons (Papio papio): Species differences in learned attention to visual features. Animal Cognition, 1, 123-133.

Erickson, M. A. & Kruschke, J. K. (1998). Rules and Exemplars in Category Learning. Journal of Experimental Psychology: General, 127, 107-140.

Kalish, M. L. & Kruschke, J. K. (1997). Decision boundaries in one dimensional categorization. Journal of Experimental Psychology: Learning, Memory and Cognition, 23, 1362-1377.

Kruschke, J. K. (1997). Selective attention in associative learning. (Review of the book by D. R. Shanks, The Psychology of Associative Learning.) Journal of Mathematical Psychology, 41, 207-211. ABSTRACT.

Kruschke, J. K. (1996). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 22, 3-26.

Kruschke, J. K. (1996). Dimensional relevance shifts in category learning. Connection Science, 8(2), 225-247. (doi:10.1080/095400996116893) Data from relevance shift experiment (.txt format).

Kruschke, J. K. & Fragassi, M. M. (1996). The perception of causality: Feature binding in interacting objects. In: Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society, pp. 441-446. Hillsdale, NJ: Erlbaum. Here's a write-up of a 1987 conference presentation on the same topic. Kruschke, J. K. (1987). The perception of causality: A performance measure of ampliation. Paper presented at the Ninth Annual Berkeley-Stanford Conference in Cognitive Psychology.

Kruschke, J. K. (1996). An interactive classroom demonstration for explaining propositional and analogue representation. Teaching of Psychology, 23, 162-165.

Kruschke, J. K. (1996). Principles of Human Category Learning in Connectionist Models. Presentation at the Symposium on Connectionism and Psychology, 26th International Congress of Psychology Montreal, 17 August 1996. Outline of symposium and presentation.

Kruschke, J. K. (1996). Explanatory principles in ALCOVE. Paper for the Cognitive Modelling Workshop of the Seventh Australian Conference on Neural Networks, 9 April 1996, Australian National University, Canberra. View html of Kruschke (1996).

Kruschke, J. K. and Bradley, A. L. (June 1995). Extensions to the Delta Rule for Associative Learning. Indiana University Cognitive Science Research Report 141 (June 1995).

Kruschke, J. K. & Erickson, M. A. (1994). Learning of rules that have high-frequency exceptions: New empirical data and a hybrid connectionist model. In: Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society, pp.514-519. Hillsdale, NJ: Erlbaum.

Kruschke, J. K. (1993). Three principles for models of category learning. In: G. V. Nakamura, R. Taraban and D. L. Medin (eds.), The Psychology of Learning and Motivation: Special Volume on Categorization by Humans and Machines, v.29, 57-90. San Diego: Academic Press. ABSTRACT.

Kruschke, J. K. (1993). Human category learning: Implications for backpropagation models. Connection Science, 5(1), 3-36. (doi:10.1080/09540099308915683) Data from filtration/condensation experiment (.txt format). Data from catastrophic forgetting experiment (.txt format).

Nosofsky, R. M. & Kruschke, J. K. (1992). Investigations of an exemplar-based connectionist model of category learning. In: D. L. Medin (ed.), The Psychology of Learning and Motivation, v.28, 207-250. San Diego: Academic Press. ABSTRACT.

Nosofsky, R. M., Kruschke, J. K., & McKinley, S. (1992). Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory and Cognition, 18, 211-233. ABSTRACT.

Kruschke, J. K. (1992). ALCOVE: An exemplar-based connectionist model of category learning. Psychological Review, 99(1), 22-44. (doi:10.1037/0033-295X.99.1.22) Computer program for ALCOVE, in C. Example of parameter file. Example of pattern file. Example of network architecture file.

Kruschke, J. K., & Movellan, J. R. (1991). Benefits of gain: Speeded learning and minimal hidden layers in back-propagation networks. IEEE Transactions on Systems, Man and Cybernetics, 21, 273-280.

Kruschke, J. K. (1989). Distributed bottlenecks for improved generalization in back-propagation networks. International Journal of Neural Networks Research and Applications, 1, 187-193.