Abstract
People frequently encounter the challenge of deciding when to stop exploring options to optimize outcomes, such as when selecting an apartment in a fluctuating housing market or booking a dinner reservation on New Year’s Eve. Despite experiencing these decisions on multiple occasions, people often struggle to stop searching optimally. This research investigates human learning abilities in optimal stopping tasks, focusing on feedback and knowledge of option value distributions. Through an experimental sequential choice task, we demonstrate that experience improves performance, with feedback significantly influencing learning. We also find that awareness of the value distribution reduces the duration of the search. A cognitive model accurately predicts these effects, shedding light on human learning processes.
Citation
@inproceedings{bugbee_feedback_2024,
title={Feedback Promotes Learning and Knowledge of the Distribution of Values Hinders Exploration in an Optimal Stopping Task},
author={Bugbee, Erin H and Gonzalez, Cleotilde},
booktitle={Proceedings of the Annual Meeting of the Cognitive Science Society, CogSci 2024},
year={2024},
pages = {4618-4624}
}