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Department of Psychological Sciences

Michael Young, Ph. D.

Pic of Dr. Young

Contact Information

Office: BH 473
E-mail: michaelyoung@ksu.edu
ORCID: 0000-0003-1493-9548

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, a position he held until 2021. In 2021-22, he served as the Faculty Fellow in the Office of Institutional Research and Assessment, in 2022-24 as the Graduate School Faculty Associate, and starting in 2023 as the Associate Director of CNAP.

Dr. Young's primary research program involves the study of decision making in dynamic environments. He is currently studying 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.  The lab normally requires 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.  His 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 his previous graduate students have ended up teaching graduate statistics or doing significant statistical consulting as professors, postdoctoral scientists, or in industry.

Current Graduate Students

  • Patrick Hancock
  • Robby Southern
  • Kari Payne
  • Luke Watson

Recent Publications

(* indicates graduate student co-author, ** for undergraduate)

  • Cox, D.J., & Young, M.E. (in press). We live in interesting times: Introduction to the special section on big data and behavior science. Perspectives on Behavior Science.
  • *Frankot, M., Young, M.E., & Vonder Haar, C. (in press). Understanding individual subject variability through large behavioral datasets: Analytical and statistical considerations. Perspectives on Behavior Science.
  • Young, M.E., & *Howatt, B.H. (2023). When smaller sooner depletes a pool of resources faster.  Experimental Psychology, 70, 215-231. 
  • *Frankot, M., Mueller, P.M., Young, M.E., & Vonder Haar, C. (2023). Statistical power and false power rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience. Neuropsychopharmacology, 48, 1612–1622.
  • Soto, P.L., Young, M.E., *DiMarco, G., *George, B., Melnikova, T., Savonenko, A., & Harris, B. (2023). Longitudinal assessment of cognitive function in the APPswe/PSEN1dE9 mouse model of Alzheimer’s-related beta-amyloidosis. Neurobiology of Aging, 128, 85-99.
  • Young, M.E., & *Howatt, B.H. (2023). Cue transparency leads to a myopic decision habit: Optimal training ratios to avoid overshadowing.  Learning & Motivation, 82.
  • Hildebrandt, B.A., Fisher, H., LaPalombara, Z., Young, M.E., & Ahmari, S.E. (2023). Corticostriatal dynamics underlying components of binge-like consumption of palatable food in mice. Appetite, 183.
  • Young, M.E., & *Howatt, B.H. (2023).  Resource limitations: A taxonomy. Behavioural Processes, 206
  • *Brandner, J.L., Brase, G.L., & Young, M.E. (2023). Size, scale, and design matter: Commentary on Lewis, Al-Shawaf, Semchenko, and Evans (2022). Evolution and Human Behavior, 44, 140-143.
  • *Howatt, B.H., & Young, M.E. (2023).  The function of sound in the balloon analogue risk task. Behavior Research Methods, 55, 3433–3445.
  • Young, M.E., & *Howatt, B.H. (2022).  Learning when to wait and when to act. Learning and Behavior, 50, 207-221. 
  • *Rader, A.E., Young, M.E., Leaf, J.B. (2021). A quantitative analysis of accuracy, reliability and bias in judgements of functional analyses.  Journal of the Experimental Analysis of Behavior, 116, 166-181.
  • Young, M.E. & Hoane, M.H. (2021).  Mixed effects modeling of Morris water maze data revisited: Bayesian censored regression. Learning and Behavior, 49, 307–320.
  • Silveira, M., Camargo, J.C., Aggio, N.M., Ribeiro, G.W., Cortez, M.D., Young, M.E., & de Rose, J.C. (2021). The influence of training procedure and stimulus valence on the long-term maintenance of equivalence relations. Behavioural Processes, 185.
  • *Vangsness, L., & Young, M.E. (2021). More isn’t always better:  When metacognitive prompts are misleading. Metacognition and Learning, 16, 135-156.
  • Wasserman, E. A., Young, M. E., & Castro, L. (2021). Mechanisms of same-different conceptualization: Entropy happens!  Current Opinion in Behavioral Sciences, 37, 19-28.