Current Projects

The primary questions addressed in the DIS Lab concern the intersection of decision-making, judgment, and memory, with a focus on developing theoretical and computational models of behavior.

Current research in the DIS Lab includes research on information foraging, incentive structures in decision tasks, explainable artificial intelligence in decision support systems, and forecasting.

Information Search

As society transitions between the information and intelligence ages, it is necessary to better understand the process by which people decide which information and which information sources are useful. The DIS Lab focuses on the role of beliefs and expectations in selecting or designing hypothesis tests--a phenomenon we refer to as hypothesis-guided search. Understanding how beliefs change over time as a function of information acquisition and information source selection is critically important for understanding behaviors as diverse as clinical judgment and ideological polarization.

Decision Support Systems

Decision-aiding tools, including those infused with artificial intelligence, are becoming prolific in modern society. One goal of the DIS lab is to develop models that simulate the interactions between decision-makers, the strategies they employ, and opaque automation in decision-aiding tools. In addition to highlighting the importance of design decisions on the efficacy of decision-aids, our simulation work informs experimental investigations of human-algorithm systems by providing predictions about the influence of engineered features in decision-aiding tools.


Across myriad domains (e.g., medicine, finance, marketing), people would greatly benefit from enhancing diagnostic reasoning associated with forecasting future events. The DIS Lab conducts research to build and evaluate models that emphasize the causal reasoning underlying forecasting behavior. Our work is intended to motivate the design of tools that would to support improved identification of causal relations in complex environments, enhanced anticipation of future events, and automated analysis of the likelihood for possible outcomes.

Selected Works

Illingworth, D. A., & Thomas, R. P. (2022). Strength of belief guides information foraging. Psychological Science, 33(3), 450-462.

Illingworth, D. A., & Feigh, K. M. (2022). Impact mapping for geospatial reasoning and decision-making. Human Factors, 64(8), 1363-1378. 

Illingworth, D. A., Lawrence, A., Dougherty, M. R., & Thomas, R. P. (2023, July). Using Perspective Taking and Information Paucity to Explore Alternative Realities. In International Conference on Human-Computer Interaction (pp. 17-31). Cham: Springer Nature Switzerland. 

Parmar, S., Illingworth, D. A., & Thomas, R. P. (2021, September). Model blindness: A framework for understanding how model-based decision support systems can lead to performance degradation. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 65, pp. 680-684). Sage CA: Los Angeles, CA: SAGE Publications.

Parmar, S., Illingworth, D. A., & Thomas, R. P. (2023, September). Model blindness II: Investigating a model-based recommender system’s impact on decision making. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 67, No. 1, pp. 163-170). Sage CA: Los Angeles, CA: SAGE Publications.

Selected Presentations


HCII 2023

PS 2023 Final.pdf

Psychonomics 2023

PS 2022 Final.pdf

Psychonomics 2022


Frequent Collaborators

Rick Thomas

Georgia Institute of Technology

Decision Processes Laboratory

Michael Dougherty

University of Maryland, College Park

Decision, Attention, and Memory Laboratory