Ion Juvina, Ph.D. (Advisor); Joseph W. Houpt, Ph.D. (Committee Member); Valerie L. Shalin, Ph.D. (Committee Member); Zheng Xu, Ph.D. (Committee Member)
Doctor of Philosophy (PhD)
Counterfactual reasoning can be used in task-switching scenarios, such as design and planning tasks, to learn from past behavior, predict future performance, and customize interventions leading to enhanced performance. Previous research has focused on external factors and personality traits; there is a lack of research exploring how the decision-making process relates to both task-switching and counterfactual predictions. The purpose of this dissertation is to describe and explain individual differences in task-switching strategy and cognitive processes using machine learning techniques and linear ballistic accumulator (LBA) models, respectively, and apply those results in counterfactual models to predict behavior. Applying machine learning techniques to real-world task-switching data identifies a pattern of individual strategies that predicts out-of-sample clustering better than random assignment and identifies the most important factors contributing to the strategies. Comparing parameter estimates from several different LBA models, on both simulated and real data, indicates that a model based on information foraging theory that assumes all tasks are evaluated simultaneously and holistically best explains task-switching behavior. The resulting parameter values provide evidence that people have a switch-avoidance tendency, as reported in previous research, but also show how this tendency varies by participant. Including parameters that describe individual strategies and cognitive mechanisms in counterfactual prediction models provides little benefit over a baseline intercept-only model to predict a holdout dataset about real-world task switching behavior and performance, which may be due to the complexity and noise in the data. The methods developed in this research provide new opportunities to model and understand cognitive processes for decision-making strategies based on information foraging theory, which has not been considered previously. The results from this research can be applied to future task-switching scenarios as well as other decision-making tasks, both in a laboratory setting as well as the real-world, and have implications for understanding how these decisions are made.
Department or Program
Department of Mathematics and Statistics
Year Degree Awarded
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