Michael Young, Ph. D.

mike young

Contact Information

Office: BH 491
Phone: 532-0602
E-mail: michaelyoung@ksu.edu

Vita (pdf)

Google Scholar Profile

Young Lab

Research Interests

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.

 

Student Involvement

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

 

Grant Funding (last 5 years)

  • 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 – 2013.
  • Choosing among causal agents in a dynamic environment.  PI of grant funded by the National Science Foundation.  Total award: $100,897. 2007 – 2010.
  • Choosing among causal agents in a stressful environment.  PI of grant funded by the Air Force Office for Scientific Research.  Original award: $142,899. 2007-2009

 

Recent Publications (*indicates student co-author, ** for undergraduate)

  • 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, 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.
  • Castro, L., Wasserman, E.A, & Young, M.E. (2012). Variations on variability: Effects of display composition on same-different discrimination in pigeons. Learning and Behavior, 40, 416-426.
  • Young, M.E., *Cole, J. J., & *Sutherland, S.C. (2012). Rich stimulus sampling for between-subjects designs improves model selection. Behavior Research Methods, 44, 176-188.
  • Young, M.E. (2012). Contemporary thought on the environmental cues that determine causal decisions. T.R. Zentall & E.A. Wasserman (Eds.), Oxford Handbook of Comparative Cognition (pp. 141-156). New York: Oxford University Press.
  • Young, M.E., & *Cole, J.J. (2012). Human sensitivity to the magnitude and probability of a continuous causal relation in a video game. Journal of Experimental Psychology: Animal Behavior Processes, 38, 11-22.