Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published January 7, 2022 | Supplemental Material + Published
Journal Article Open

Humans depart from optimal computational models of interactive decision-making during competition under partial information

Abstract

Decision making under uncertainty in multiagent settings is of increasing interest in decision science. The degree to which human agents depart from computationally optimal solutions in socially interactive settings is generally unknown. Such understanding provides insight into how social contexts affect human interaction and the underlying contributions of Theory of Mind. In this paper, we adapt the well-known 'Tiger Problem' from artificial-agent research to human participants in solo and interactive settings. Compared to computationally optimal solutions, participants gathered less information before outcome-related decisions when competing than cooperating with others. These departures from optimality were not haphazard but showed evidence of improved performance through learning. Costly errors emerged under conditions of competition, yielding both lower rates of rewarding actions and accuracy in predicting others. Taken together, this work provides a novel approach and insights into studying human social interaction when shared information is partial.

Additional Information

© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Received 26 February 2021; Accepted 14 December 2021; Published 07 January 2022. We thank the contributions of Julia Spilcke-Liss, Julia Majewski, Freya Leggemann, Franziska Sikorski, Jann Martin and Vivien Breckwoldt with the large amount of data collection. We thank Shannon Klotz, Corinne Donnay, and Rena Patel for help with piloting and initial data assessment. SSK, PD, MS and JG were funded by a Collaborative Research in Computational Neuroscience grant awarded jointly by the German Ministry of Education and Research (BMBF, 01GQ1603) and the United States National Science Foundation (NSF, 1608278). JG and TR were supported by the Collaborative Research Center TRR 169 "Crossmodal Learning" funded by the German Research Foundation (DFG) and the National Science Foundation of China (NSFC). MS gratefully acknowledges support from a Scripps College Faculty Research grant. Data availability: The PIs on this project commit to sharing the data publicly on the National Science Foundation CRCNS site once the initial descriptive and IPOMDP modeling papers are accepted for publication. These authors contributed equally: Michael Spezio and Jan Gläscher. Author Contributions: J.G., M.S., and P.D. developed the study concept. All authors contributed to the study design. Testing and data collection were performed by S.S.K. and T.R. S.S.K. performed the data analysis and interpretation under the supervision of J.G. and M.S. S.S.K., J.G. and M.S. drafted the manuscript, and all authors provided critical revisions. All authors approved the final version of the manuscript for submission and declare no conflict of interest. The authors declare no competing interests.

Attached Files

Published - s41598-021-04272-x.pdf

Supplemental Material - 41598_2021_4272_MOESM1_ESM.pdf

Files

s41598-021-04272-x.pdf
Files (5.4 MB)
Name Size Download all
md5:8c13dbe63e00e04ff7db55858e0d3dfd
5.0 MB Preview Download
md5:fab5f2ee05437abbc9cb6d531f4cf960
432.9 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 23, 2023