The Science of Decision Making
We use computational models to better understand the mental processes involved in making a decision. Computation models use computer programs to simulate and study complex systems.
We have three main areas of study:
Deliberation is a decision making process where information about the object or event in question is intentionally gathered over time. We observe this process in perceptual, choice, judgment, and probability domains. Our understanding of this evidence accumulation process is precise enough that (in controlled laboratory settings) we can predict the choices people will make, the time it will take to make them, and the confidence they will have in them. We are currently working to use these models to predict behavior in more complex conditions.
The decisions people make are shaped as much by psychological processes as the choice environments they make their decisions in. So, what are the critical properties of these choice environments, and how are these structures used to make decisions? We wanted to understand how people use the relationship between risks and rewards to make decisions. This led us to study why the inverse relationship is so influential, and how people use this relationship to make inferences about the chances of different outcomes.
Often, computational models in psychology are used to understand behavior in the lab. We are interested in taking these models from the laboratory, and translating them into real-world tools for identifying and improving problematic decision. We have used computational models to identify decision making deficits among real-world risk takers, like drug users. Also, we analyzed students who quit school to analyze critical events which contributed to their decisions. Most recently, we used these models to investigate police officers’ decisions in active shooter situations.
Hertwig, R., Pleskac, T. J., Pachur, T., & Center for Adaptive Rationality. (2019). Taming Uncertainty. MIT Press. https://doi.org/10.7551/mitpress/11114.001.0001
Kvam, P. D., Pleskac, T. J., Yu, S., & Busemeyer, J. R. (2015). Interference effects of choice on confidence: Quantum characteristics of evidence accumulation. Proceedings of the National Academy of Sciences, 112(34), 10645–10650. https://doi.org/10.1073/pnas.1500688112
Yu, S., Pleskac, T. J., & Zeigenfuse, M. D. (2015). Dynamics of postdecisional processing of confidence. Journal of Experimental Psychology: General, 144(2), 489–510. https://doi.org/10.1037/xge0000062
Zeigenfuse, M. D., Pleskac, T. J., & Liu, T. (2014). Rapid decisions from experience. Cognition, 131(2), 181–194. https://doi.org/10.1016/j.cognition.2013.12.012
Pleskac, T. J. (2012). Comparability Effects in Probability Judgments. Psychological Science, 23(8), 848-854. https://doi.org/10.1177/0956797612439423
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychological review, 117(3), 864–901. https://doi.org/10.1037/A0019737
Pleskac, T. J., Conradt, L., Leuker, C., & Hertwig, R. (2021). The ecology of competition: A theory of risk–reward environments in adaptive decision making. Psychological Review, 128(2), 315–335. https://doi.org/10.1037/rev0000261
Leuker, C., Pachur, T., Hertwig, R., & Pleskac, T. J. (2018). Exploiting risk–reward structures in decision making under uncertainty. Cognition, 175, 186–200. https://doi.org/10.1016/j.cognition.2018.02.019
Pleskac, T. J., & Hertwig, R. (2014). Ecologically rational choice and the structure of the environment. Journal of Experimental Psychology: General, 143(5), 2000-2019. https://doi.org/10.1037/xge0000013
Pleskac, T. J. (2007). A signal detection analysis of the recognition heuristic. Psychonomic Bulletin & Review, 14(3), 379–391. https://doi.org/10.3758/BF03194081.
Johnson, D., Cesario, J., & Pleskac, T. J. (2018). How prior information and police experience impacts decisions to shoot. Journal of Personality & Social Psychology, 115(4), 601–623. https://doi.org/10.1037/ pspa0000130
Pleskac, T. J., Cesario, J., & Johnson, D. J. (2018). How race affects evidence accumulation during the decision to shoot. Psychonomic Bulletin & Review, 25, 1301–1330. https://doi.org/10.3758/s13423-017-1369-6
Pleskac, T. J., Keeney, J., Merritt, S. M., Schmitt, N., & Oswald, F. L. (2011). A detection model of college withdrawal. Organizational Behavior and Human Decision Processes, 115(1), 85–98. https://doi.org/ 10.1016/j.obhdp.2010.12.001
Pleskac, T. J. (2008). Decision making and learning while taking sequential risks. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34(1), 167-185. https://doi.org/10.1037/0278-73220.127.116.11
Wallsten, T. S., Pleskac, T. J., & Lejuez, C. W. (2005). Modeling Behavior in a Clinically Diagnostic Sequential Risk-Taking Task. Psychological Review, 112(4), 862–880. https://doi.org/10.1037/0033-295X.112.4.862