Michael Young, Ph. D.
Office: BH 491
Dr. Michael Young began his career as a computer scientist out of the University of Illinois in 1984 with a specialization in Artificial Intelligence. He received his MS in Computer Science in 1990 and a Ph.D. in Experimental Psychology from the University of Minnesota/Twin Cities in 1995. After spending 12 years at Southern Illinois University at Carbondale, Dr. Young joined Kansas State University in 2012 as the Head of the Department of Psychological Sciences.
Dr. Young's primary research program involves the study of decision making in dynamic environments. He is currently studying (a) the variables that influence the identification of causes in continuously unfolding environments and (b) the situational and individual variables related to impulsive and risky choice in video game environments. He continues to integrate his background in computer science with his interest in psychology through the development of computational models of environment-behavior relations. Dr. Young's love of mathematics also is revealed by his occasional side project evaluating various statistical and design methods using Monte Carlo simulation.
For more information, go to Dr. Young's laboratory web page.
Undergraduate students begin in the lab by getting involved in the conduct of ongoing research on judgment and decision making. I normally require a two-semester commitment so that the student can progress to learning additional skills after mastering the basics. Undergraduate and graduate students attend weekly laboratory meetings where everyone is required to present at least once during the term in order to develop their presentation skills. Graduate students usually begin by getting involved in an ongoing project in order to learn the ropes. As their research interests evolve, they begin to develop independent projects as well as continuing to collaborate with Dr. Young and his students in their projects. My goal in graduate training is to prepare the student to function as an independent scientist.
By the way, Dr. Young loves to talk about research design and statistics, so you will come out with a strong skill set in these areas. Because of their strong statistical training, many of my previous graduate students have ended up teaching graduate statistics or doing significant statistical consulting as professors or postdoctoral scientists.
Current Graduate Students
- Tony McCoy
- Angela Crumer
- Lisa Vangsness
Grant Funding (last 5 years)
- Factors affecting consumer choice of confectionaries. PI of grant funded by the Hershey Company. Total award: $50,000. 2014.
- Waiting for a better future: Deciding when to “cash in” when outcomes are continuously improving. PI of grant funded by the National Institute for Drug Abuse (E.A. Jacobs, Co-Investigator). Total award: $218,250. 2010 – 2014.
- Choosing among causal agents in a dynamic environment. PI of grant funded by the National Science Foundation. Total award: $100,897. 2007 – 2010.
Recent Publications (*indicates student co-author, ** for undergraduate)
- *Webb, T.L, & Young, M.E. (2015). Waiting when both certainty and magnitude are increasing: Certainty overshadows magnitude. Journal of Behavioral Decision Making, 28, 294-307. doi: 10.1002/bdm.1850.
- Peissig, J.J., Nagasaka, Y., Young, M.E., Wasserman, E.A., & Beiderman, I. (2015). Using the reassignment procedure to test object representation in pigeons and people. Learning and Behavior, 43, 188-207.
- Sutherland, S.C., Harteveld, C., & Young, M.E. (2015). The role of environmental predictability and costs in relying on automation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2535-2544). Association for Computing Machinery: New York.
- *Rung, J., & Young, M.E. (2015). Learning to wait for more likely or just more: Greater tolerance to delays of reward with increasingly longer delays. Journal of the Experimental Analysis of Behavior, 103, 108-124.
- Young, M.E., & *McCoy, A.W. (2015). A delay discounting task produces a greater likelihood of waiting than a deferred gratification task. Journal of the Experimental Analysis of Behavior, 103, 180-195.
- Young, M.E. (2014). Sex differences in the inference and perception of causal relations within a video game. Frontiers in Psychology, 5, 926. doi: 10.3389/fpsyg.2014.00926.
- Young, M.E., *Webb, T.L., **Rung, J., & *McCoy, A.W. (2014). Outcome probability versus magnitude: When waiting benefits one at the cost of the other. PLOS ONE, 9(6), e98996. doi:10.1371/journal.pone.0098996.
- Young, M.E., & Racey, D.R. (2014). Effects of response frequency constraints on learning in a non-stationary multi-armed bandit task. Special Issue on Behavioral Variability in International Journal of Comparative Psychology, 27, 106-122.
- **Rung, J.M., & Young, M.E. (2014). Training tolerance to delay using the escalating interest task. Psychological Record, 64, 423-431. doi:10.1007/s40732-014-0045-8
- Lazareva, O., Young, M.E., & Wasserman, E.A. (2014). A three-component model of relational responding in a transposition task. Journal of Experimental Psychology: Animal Learning and Cognition, 40, 63-80.
- Young, M.E., *Webb, T.L., **Rung, J., & Jacobs, E.A. (2013). Sensitivity to changing contingencies in an impulsivity task. Journal of the Experimental Analysis of Behavior, 99, 335-345.
- Limongi, R., *Sutherland, S.C., *Zhu, J., Young, M.E., & Habib, R. (2013). Temporal prediction errors modulate cingulate-insular coupling. NeuroImage, 71, 147-157.
- Young, M.E., *Webb, T.L., *Sutherland, S.C., & Jacobs, E.A. (2013). Magnitude effects for experienced rewards at short delays in the escalating interest task. Psychonomic Bulletin and Review, 20, 302-309.