This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Cognitive models of sequential choice in the optimal stopping task

Erin Bugbee
Carnegie Mellon University ~ Department of Social and Decision Sciences
Chase McDonald
Carnegie Mellon University, United States of America ~ Social & Decision Sciences
Dr. Erin McCormick
Air Force Research Laboratory
Joshua Fiechter
Christian Lebiere
Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213 USA
Dr. Leslie Blaha
Air Force Office of Scientific Research
Prof. Cleotilde (Coty) Gonzalez
Carnegie Mellon University ~ Social and Decision Sciences Department

In the optimal stopping problem, a decision maker aims to select the option that maximizes reward in a sequence, under the condition that they must select it at the time of presentation. Past literature suggests that people use a series of thresholds to make decisions (Lee, 2006), and researchers have developed a hierarchical Bayesian model, Bias-From-Optimal (BFO), to characterize these thresholds (Guan et al., 2015, 2020). BFO relies on optimal thresholds and the idea that people’s thresholds are characterized by how far they are from optimal and how this bias increases or decreases throughout the sequence. In this work, we challenge the assumption that people use thresholds to make decisions. We develop a cognitive model based on Instance-Based Learning Theory (Gonzalez et al., 2003) to demonstrate an inductive process by which individual thresholds are derived, without assuming that people use thresholds or relying on optimal thresholds. The IBL model makes decisions by considering the current value and the distance of its position from the end of the sequence, and learns through feedback from past decisions. Using this model, we simulate the choices of 56 individuals and compare these simulations with empirical data provided by Guan et al. (2020). Our results demonstrate that the IBL model replicates human behavior and generates the BFO model’s thresholds, without assuming any thresholds. Overall, our approach improves upon previous methods by producing cognitively plausible choices, resembling those of humans. The IBL model can therefore be used to predict human risk tendencies in sequential choice tasks.



sequential choice tasks
optimal stopping problem
cognitive models
instance-based learning
Learning as a metric of model viability Last updated 3 years ago

Hi Erin, I really enjoyed your talk! I seem to remember that in the original Lee paper(s), participants don’t learn in the optimal stopping task (and I know the UC Irvine folks have some casually collected data from over the years showing the same.). What do you think about this discrepancy between your model and human behavior? Is this a ...

Dr. Beth Baribault 2 comments

Hi Erin, Really clear talk, and a fascinating core idea of generating optimal stopping behavior from a general cognitive architectural approach with default parameters rather than parametric threshold inference. (I personally would have used the Baumann and colleagues model, or even my old Lee (2006, CogSci) model as a more process realistic com...

Michael Lee 1 comment
Cite this as:

Bugbee, E. H., McDonald, C., McCormick, E. N., Fiechter, J., Lebiere, C., Blaha, L., & Gonzalez, C. (2021, July). Cognitive models of sequential choice in the optimal stopping task. Paper presented at Virtual MathPsych/ICCM 2021. Via