For Prospective Students

Information for Prospective Students or Post-Docs

in the Lab of Prof. John K. Kruschke

Qualities of students sought. I am looking for students who have a strong interest in moral psychology (a.k.a. the science of morality) and a strong interest in mathematics, statistics, and machine learning. Students who have interests bridging into educational, clinical, neural, or applied sciences are encouraged. It also helps to tolerate bad puns and allusions to Monty Python.

Prospective graduate students may apply for an IGERT fellowship. $30,000 annual stipend, plus tuition, fees, and health insurance. More info: http://igert.cogs.indiana.edu/training.html. Deadline is December 31, 2010 (and presumably a late December deadline for 2011).

Research topics.

My primary research is taking a new direction into the science of morality. Cognitive science has the opportunity to make valuable contributions to this compelling and vital domain.

I am also interested in implications of Bayesian data analysis. For example, Bayesian optimal adaptive design has applications to optimal active learning and optimal teaching.

• The bulk of my previous research has focused on models of attention in basic associative learning. I will continue to collaborate and consult on projects in associative learning. My research on this topic centers on mathematical models of attention in learning, formalized in both connectionist and Bayesian frameworks. The diagrams below show the general framework of my attentional learning models. The research program is both theoretical and empirical: Models are tested by numerous experiments conducted with human participants. For more information, see my publications page. For less information, click here.
The left panel shows the general framework: There is a learned mapping from stimulus cues to an attentional allocation across those cues. There is also a learned mapping from the attentionally filtered cues to the outcomes. The attentional shifting typically occurs before the learning of the mappings. This general framework has been implemented in a variety of ways, as suggested by the smaller panels on the right. Most recently, the framework has been implemented by locally Bayesian learning of each mapping.

But will it blend? Some vitae highlights appear on a separate page. While I am keenly interested in the research topics mentioned above, I also very much enjoy teaching. At present, my primary teaching focus is Bayesian data analysis.

Some students or post-docs with whom I have published (or expect to soon).
Rick Hullinger
Joined the graduate program in 2004.

  • Kruschke, J. K., and Hullinger, R. A. (2010). Evolution of attention in learning. In: N. A. Schmajuk (Ed.), Computational models of conditioning. Cambridge University Press.
  • Stephen Denton
    Ph.D. 2009.

    Post-doctoral research with Dr. Rich Shiffrin and Dr. Rob Nosofsky at I.U.

  • 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, **.
  • Denton, S. E., Kruschke, J. K., & Erickson, M. A. (2008). Rule-based extrapolation: A continuing challenge for exemplar models. Psychonomic Bulletin & Review, 15(4), 780-786.
  • Denton, S. E., & Kruschke, J. K. (2006). Attention and salience in associative blocking. Learning & Behavior, 34(3), 285-304.
  • Anthony Bishara
    Post doctoral researcher, 2006-2008, working primarily with Julie Stout and Jerry Busemeyer. Now on the faculty of the College of Charleston, South Carolina.
  • Bishara, A. J., Kruschke, J. K., Stout, J. C., Bechara, A., McCabe, D. P., & Busemeyer, J. R. (2009). Sequential learning models for the Wisconsin card sort task: Assessing processes in substance dependent individuals. Journal of Mathematical Psychology, 54, 5-13.
  • Emily Kappenman
    B.S. with Honors, 2005.

    Emily was co-advised by Dr. Bill Hetrick. Emily won the Psychology Department's J. R. Kantor Award for excellence in undergraduate research, and subsequently won an NSF Graduate Fellowship. In graduate school she worked with Steve Luck at the University of California at Davis.

  • 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.
  • Mark Johansen
    PhD 2002.

    After his PhD, Mark joined the faculty of Cardiff University, Wales, UK.

  • Johansen, M. K., & 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., & Johansen, M. K. (1999). A Model of Probabilistic Category Learning. Journal of Experimental Psychology: Learning, Memory and Cognition, 25, 1083-1119.
  • Nate Blair
    PhD 2001.

    Nate did post-doctoral research with Dr. Barbara Dosher at the University of California at Irvine. He then joined the staff of the Office of Institutional Research at California State University at Sacramento.

  • Kruschke, J. K. & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636-645.
  • Teresa Treat
    PhD 2000.

    Teresa's primary mentor was Dr. Dick McFall, but she devoted a lot of energy to our lab too. After a clinical intership, Teresa joined the faculty of Yale University, and then the faculty of the University of Iowa.

  • Treat, T. A., Viken, R. J., Kruschke, J. K., and McFall, R. M. (2009). Role of attention, memory, and covatiation-detection processes in clinically significant eating-disorder symptoms. Journal of Mathematical Psychology, 54, 184-195.
  • Treat, T. A., McFall, R. M., Viken, R. J., Kruschke, J. K., Nosofsky, R. M., & 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, pp. 179-205. Washington, D. C.: American Psychological Association.
  • 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, 239-252.
  • 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, 549-565.
  • Michael Erickson
    PhD 1999. Outstanding Dissertation Award from the I.U. Cognitive Science Program.

    Michael did post-doctoral research with Lynne Reder and then Jay McClelland at Carnegie Mellon University. Michael then joined the faculty of the University of California at Riverside, and then the faculty of Hawaii Pacific University.

  • Denton, S. E., Kruschke, J. K., & Erickson, M. A. (2008). Rule-based extrapolation: A continuing challenge for exemplar models. Psychonomic Bulletin & Review,
  • Erickson, M. A. & Kruschke, J. K. (2002). Rule-based extrapolation in perceptual categorization. Psychonomic Bulletin & Review, 9, 160-168.
  • Erickson, M. A. & Kruschke, J. K. (1998). Rules and Exemplars in Category Learning. Journal of Experimental Psychology: General, 127, 107-140.
  • 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.
  • Mike Kalish
    Post-doctoral researcher, 1993-1995.

    Mike then joined the faculty of the University of Western Australia, Perth, and then the University of Louisiana at Lafayette.

  • 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.
  • Kalish, M. L. & Kruschke, J. K. (2000). The role of attention shifts in the categorization of continuous dimensioned stimuli. Psychological Research, 64, 105-116.
  • Kalish, M. L. & Kruschke, J. K. (1997). Decision boundaries in one dimensional categorization. Journal of Experimental Psychology: Learning, Memory and Cognition, 23, 1362-1377.
  • My (Kruschke's) graduate mentor was Prof. Stephen Palmer, University of California at Berkeley, during the years 1983-1989. Steve was tremendously supportive and encouraging of my intellectual pursuits. He showed me repeatedly --by example and by direct instruction-- what it meant to think rigorously and incisively, and what it meant to write and present clearly. (BTW, his textbook, Vision Science, is nothing less than monumental and shows the clarity and scope of his teaching.) He was perspicacious enough to teach a seminar regarding the just-published PDP (parallel distributed processing, a.k.a. connectionism) books, which became a launching pad for my subsequent ideas. I worked hard on a number of projects with Steve (regarding reference frames in shape perception), and with Danny Kahneman and Anne Triesman and John Watson (regarding perception of causality). Unfortunately, none of the projects produced data that were publishable! The lack of publications meant that Steve's support was all the more crucial. Danny Kahneman told me, as I was fearfully sending out job applications, "At this point Steve can do more for you than you can do for you." Thanks Steve!