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Characterizing human random-sequence generation in competitive and non-competitive environments using Lempel–Ziv complexity

Wong, Alice and Merholz, Garance and Maoz, Uri (2021) Characterizing human random-sequence generation in competitive and non-competitive environments using Lempel–Ziv complexity. Scientific Reports, 11 . Art. No. 20662. ISSN 2045-2322. PMCID PMC8526708. doi:10.1038/s41598-021-99967-6. https://resolver.caltech.edu/CaltechAUTHORS:20211103-215510049

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Abstract

The human ability for random-sequence generation (RSG) is limited but improves in a competitive game environment with feedback. However, it remains unclear how random people can be during games and whether RSG during games can improve when explicitly informing people that they must be as random as possible to win the game. Nor is it known whether any such improvement in RSG transfers outside the game environment. To investigate this, we designed a pre/post intervention paradigm around a Rock-Paper-Scissors game followed by a questionnaire. During the game, we manipulated participants’ level of awareness of the computer’s strategy; they were either (a) not informed of the computer’s algorithm or (b) explicitly informed that the computer used patterns in their choice history against them, so they must be maximally random to win. Using a compressibility metric of randomness, our results demonstrate that human RSG can reach levels statistically indistinguishable from computer pseudo-random generators in a competitive-game setting. However, our results also suggest that human RSG cannot be further improved by explicitly informing participants that they need to be random to win. In addition, the higher RSG in the game setting does not transfer outside the game environment. Furthermore, we found that the underrepresentation of long repetitions of the same entry in the series explains up to 29% of the variability in human RSG, and we discuss what might make up the variance left unexplained.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41598-021-99967-6DOIArticle
http://www.ncbi.nlm.nih.gov/pmc/articles/pmc8526708/PubMed CentralArticle
ORCID:
AuthorORCID
Maoz, Uri0000-0002-7899-1241
Additional Information:© The Author(s) 2021. 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 20 January 2021; Accepted 29 September 2021; Published 19 October 2021. We thank Liad Mudrik and Paul Rapp for helpful discussions about this project. This project was also presented at the Society for Neuroscience 2018 conference, and we are grateful for comments received there that helped improve the manuscript. This publication was made possible in part through the support of a joint grant from the John Templeton Foundation and the Fetzer Institute. The opinions expressed in this publication are those of the author(s) and do not necessarily reflect the views of the John Templeton Foundation or the Fetzer Institute. Author Contributions: G.M. and U.M. initiated the project. G.M. collected some of the data. A.W. collected the rest of the data and analyzed all the data. A.W. and U.M. wrote the main manuscript text. All authors reviewed the manuscript. The authors declare no competing interests.
Funders:
Funding AgencyGrant Number
John Templeton Foundation61283
Fetzer Institute4189
PubMed Central ID:PMC8526708
DOI:10.1038/s41598-021-99967-6
Record Number:CaltechAUTHORS:20211103-215510049
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20211103-215510049
Official Citation:Wong, A., Merholz, G. & Maoz, U. Characterizing human random-sequence generation in competitive and non-competitive environments using Lempel–Ziv complexity. Sci Rep 11, 20662 (2021). https://doi.org/10.1038/s41598-021-99967-6
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111736
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 Nov 2021 15:55
Last Modified:04 Nov 2021 15:55

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