For Prospective Students

Information for Prospective Students or Post-Docs
in the Lab of Prof. John K. Kruschke

No new graduate students are being sought at this time, unless they are independently funded.

Research Topics: I am interested in the science of moral judgment, broadly construed. Moral psychology encompasses a panorama of topics, and I am open to considering projects across the spectrum. Ongoing projects include research into how people decide to punish freeloaders, how people evaluate police use of force, and how people's moral sensitivities affect perceived humor. Past projects included research into the role of moral judgment in literary narrative. I am also interested in norm acquisition, the inference of agent intention, the role of emotion in moral judgment, etc. New projects could be purely empirical, but I am keen to involve computer modeling, for example with Bayesian networks, evolutionary models, reinforcement learning, agent based models, etc. I am also devoted to Bayesian data analysis, but research into statistical methods per se is not my primary focus.

Research Culture: I encourage interdisciplinary topics and interdisciplinary collaborations. If we develop a project that would naturally involve some other faculty, I am eager to include them. Indiana University has a culture of active and cordial interdisciplinary collaboration among many faculty across various programs, departments, and schools, including the Cognitive Science program. Within my own lab, I am keenly aware of the need for reproducible research with methodological rigor not just flash. (Here's a link to one of my blog posts, on the topic of Bayesian approaches to replication analysis.)

Qualities sought in students. I am looking for student collaborators who have a demonstrated interest in moral psychology, and who can eagerly and efficiently digest a vast literature. I encourage applications from students who have interests bridging into other fields; that is, moral psychology applied to other domains, or moral psychology through the lens of other perspectives. A successful student forges ahead independently and has excellent time-management skills. Students should also have a strong interest in Bayesian data analysis, along with strong skills in computer programming, and in mathematics too. Such skills can be learned and developed in graduate school, but it helps to have previous experience. Computer programming is needed for computerized experiments, data analysis, and computational models. We use software such as R, jsPsych, javascript, Python, JAGS, Stan, etc. Mathematical savvy is also important for understanding statistical and computational models, even if the primary focus is on empirical research.

About me (Prof. Kruschke): A brief biography can be found here. For more information about my publications, see this page. Contact information is here or here. For less information, click here.

Some students or post-docs with whom I have published, or expect to soon:
Brad Celestin
Ph.D. anticipated 2018

  • Projects on morality in law enforcement and morality in humor.
  • Torrin Liddell
    Ph.D. anticipated 2017
    Seeking career in data science and research.
  • Liddell, T. M. & Kruschke, J. K. (2014). Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type. Judgment and Decision Making, 9(6), 523-547.
  • Breithaupt, F., Gardner, K. M., Kruschke, J. K., Liddell, T. M., & Zorowitz, S. (2013). The disappearance of moral choice in serially reproduced narratives. Workshop on Computational Models of Narrative, 36-42.
  • Kruschke, J. K., & Liddell, T. M. (2017). The Bayesian New Statistics. Psychonomic Bulletin & Review
  • Liddell, T. M., & Kruschke, J. K. (under revision). Analyzing ordinal data: Support for a Bayesian approach.
  • Kruschke, J. K., & Liddell, T. M. (2017). Bayesian Analysis for Newcomers. Psychonomic Bulletin & Review
  • Young Ahn
    Ph.D. 2012
    Primary advisor was Jerome Busemeyer, also Brian O'Donnell and Julie Stout. Post-doc at Virginia Tech, then joined the faculty of Ohio State University.

  • Ahn, W.-Y., Vasilev, G., Lee, S.-H., Busemeyer, J. R., Kruschke, J. K., Bechara, A., & Vassileva, J. (2014). Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. Frontiers in Psychology: Decision Neuroscience, 5(00849).
  • Rick Hullinger
    Ph.D. 2011.
    Teaching awards in grad school, then Lecturer and Director of Pedagogy in Dept of Psych and Brain Sci at Indiana University.
  • Hullinger, R. A., Kruschke, J. K., and Todd, P. M. (2015). An Evolutionary Analysis of Learned Attention. Cognitive Science, 39, 1172-1215.
  • 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 Rich Shiffrin and Rob Nosofsky at I.U., then Tom Palmeri at Vanderbilt 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, pp. 278-304. Oxford, UK: Oxford University Press.
  • 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 Jerome 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 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
    Ph.D. 2002.
    After his Ph.D., 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
    Ph.D. 2001.
    Nate did post-doctoral research with Barbara Dosher at the University of California at Irvine. Then he became a Lecturer in the Dept of Psychology at California State University Sacramento.
  • Kruschke, J. K. & Blair, N. J. (2000). Blocking and backward blocking involve learned inattention. Psychonomic Bulletin & Review, 7, 636-645.
  • Teresa Treat
    Ph.D. 2000.
    Teresa's primary mentor was Dick McFall, but Teresa devoted a lot of energy to our lab too. After a clinical intership, she 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
    Ph.D. 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, then the University of Louisiana at Lafayette, and then Syracuse University.
  • 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!