{"title":"Investigating the use of belief-bias to measure acceptance of false information","authors":"Robert Thomson, William Frangia","doi":"10.1007/s10588-024-09388-9","DOIUrl":null,"url":null,"abstract":"<p>Belief-bias occurs when individuals’ prior beliefs impact their ability to judge the validity (i.e., structure) of an argument such that they are predisposed to accept conclusions consistent with their prior beliefs regardless of the argument’s validity. The present study uses a minimal explanation paradigm to evaluate how United States Military Academy cadets assess the validity of arguments surrounding the pull-out from Afghanistan presented by different sources of authority. Participants exhibited a significantly greater likelihood of rejecting an invalid argument with true facts compared to accepting a valid argument with false facts, with overconfidence scores implying they were unaware of this difficulty in reasoning. We also found that participants were were more critical of arguments about US capabilities coming from civilian sources. Results from the HEXACO personality assessment showed that task performance was positively correlated with perfectionism and inquisitiveness sub-scales, implying that those high in those measures were less likely to exhibit belief-bias. Even when factoring-in these traits, results revealed a small yet significant trend for participants to reject valid arguments from their peers compared with senior military and civilian counterparts. Overall, the present study shows a differential impact of belief-bias on true vs false facts, that this is influenced by the underlying source of the argument, and that personality traits mediate these effects.</p>","PeriodicalId":50648,"journal":{"name":"Computational and Mathematical Organization Theory","volume":"55 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational and Mathematical Organization Theory","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s10588-024-09388-9","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Belief-bias occurs when individuals’ prior beliefs impact their ability to judge the validity (i.e., structure) of an argument such that they are predisposed to accept conclusions consistent with their prior beliefs regardless of the argument’s validity. The present study uses a minimal explanation paradigm to evaluate how United States Military Academy cadets assess the validity of arguments surrounding the pull-out from Afghanistan presented by different sources of authority. Participants exhibited a significantly greater likelihood of rejecting an invalid argument with true facts compared to accepting a valid argument with false facts, with overconfidence scores implying they were unaware of this difficulty in reasoning. We also found that participants were were more critical of arguments about US capabilities coming from civilian sources. Results from the HEXACO personality assessment showed that task performance was positively correlated with perfectionism and inquisitiveness sub-scales, implying that those high in those measures were less likely to exhibit belief-bias. Even when factoring-in these traits, results revealed a small yet significant trend for participants to reject valid arguments from their peers compared with senior military and civilian counterparts. Overall, the present study shows a differential impact of belief-bias on true vs false facts, that this is influenced by the underlying source of the argument, and that personality traits mediate these effects.
期刊介绍:
Computational and Mathematical Organization Theory provides an international forum for interdisciplinary research that combines computation, organizations and society. The goal is to advance the state of science in formal reasoning, analysis, and system building drawing on and encouraging advances in areas at the confluence of social networks, artificial intelligence, complexity, machine learning, sociology, business, political science, economics, and operations research. The papers in this journal will lead to the development of newtheories that explain and predict the behaviour of complex adaptive systems, new computational models and technologies that are responsible to society, business, policy, and law, new methods for integrating data, computational models, analysis and visualization techniques.
Various types of papers and underlying research are welcome. Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged. Both applied and theoretical work is strongly encouraged. The editors encourage theoretical research on fundamental principles of social behaviour such as coordination, cooperation, evolution, and destabilization. The editors encourage applied research representing actual organizational or policy problems that can be addressed using computational tools. Work related to fundamental concepts, corporate, military or intelligence issues are welcome.