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.

Choice Environment

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.

Modeling Applications

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.


2021/01/01The ecology of competition: A theory of risk–reward environments in adaptive decision makingAdaptive Toolbox, Choice Environment, Risk-Reward2021-01-01 00:00:30
2019/01/01Taming UncertaintyAdaptive Toolbox, Book, Decision Making, Uncertainty2019-01-01 00:00:55
2018/01/01How prior information and police experience impact decisions to shootChoice Environment, Evidence Accumulation, Police Shootings, Real World Scenario2018-01-01 00:00:15
2018/01/01Exploiting risk–reward structures in decision making under uncertaintyChoice Environment, Risk-Reward, Uncertainty2018-01-01 00:00:02
2015/01/01Interference effects of choice on confidence: Quantum characteristics of evidence accumulationConfidence, Decision Making, Evidence Accumulation2015-01-01 12:00:08
2015/01/01Dynamics of postdecisional processing of confidenceConfidence, Decision Making, Evidence Accumulation2015-01-01 12:00:06
2014/01/01Rapid decisions from experienceDecision Making, Preferential Choice, Time Pressure2014-01-01 12:00:53
2014/01/01Ecologically rational choice and the structure of the environment.Ambiguity Aversion, Choice Environment, Risk-Reward2014-01-01 00:00:36
2012/01/01Comparability Effects in Probability JudgmentsIndependence Violations, Probability Judgments, Similarity2012-01-01 00:00:45
2011/01/01A detection model of college withdrawalDecision Making, Real World Scenario, Signal Detection, Student Withdrawal2011-01-01 00:00:26
2010/01/01Two-stage dynamic signal detection: A theory of choice, decision time, and confidenceConfidence, Decision Time, Evidence Accumulation, Preferential Choice, Signal Detection2010-01-01 00:00:08
2008/01/01Decision making and learning while taking sequential risksAdaptive Toolbox, Decision Making, Real World Scenario, Risk-Reward2008-01-01 00:00:28
2007/01/01A signal detection analysis of the recognition heuristicChoice Environment, Recognition, Signal Detection, Similarity2007-01-01 00:00:50
2005/01/01Modeling Behavior in a Clinically Diagnostic Sequential Risk-Taking TaskAdaptive Toolbox, Real World Scenario, Risk-Reward2005-01-01 00:00:15