Do not tell me more, you are honest: a preconceived honesty bias

According to the previous literature, only a few papers found better accuracy than a chance to detect dishonesty, even when more information and verbal cues (VCs) improve precision in detecting dishonesty. A new classification of dishonesty profiles has recently been published, allowing us to study...

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Detalles Bibliográficos
Autores: Pascual Ezama, David, Muñoz García, Adrián, Prelec, Drazen
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/128761
Acceso en línea:https://hdl.handle.net/20.500.14352/128761
Access Level:acceso abierto
Palabra clave:Dishonesty
Cheating
Lying
Behavioral profiles
Detection accuracy
Ciencias Sociales
53 Ciencias Económicas
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spelling Do not tell me more, you are honest: a preconceived honesty biasPascual Ezama, DavidMuñoz García, AdriánPrelec, DrazenDishonestyCheatingLyingBehavioral profilesDetection accuracyCiencias Sociales53 Ciencias EconómicasAccording to the previous literature, only a few papers found better accuracy than a chance to detect dishonesty, even when more information and verbal cues (VCs) improve precision in detecting dishonesty. A new classification of dishonesty profiles has recently been published, allowing us to study if this low success rate happens for all people or if some people have higher predictive ability. This paper aims to examine if (dis)honest people can detect better/worse (un)ethical behavior of others. With this in mind, we designed one experiment using videos from one of the most popular TV shows in the UK where contestants make a (dis)honesty decision upon gaining or sharing a certain amount of money. Our participants from an online MTurk sample (N = 1,582) had to determine under different conditions whether the contestants would act in an (dis)honest way. Three significant results emerged from these two experiments. First, accuracy in detecting (dis)honesty is not different than chance, but submaximizers (compared to maximizers) and radical dishonest people (compare to non-radicals) are better at detecting honesty, while there is no difference in detecting dishonesty. Second, more information and VCs improve precision in detecting dishonesty, but honesty is better detected using only non-verbal cues (NVCs). Finally, a preconceived honesty bias improves specificity (honesty detection accuracy) and worsens sensitivity (dishonesty detection accuracy).FrontiersUniversidad Complutense de Madrid20212021-08-2120212021-08-21journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/128761reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1287612026-06-02T12:44:21Z
dc.title.none.fl_str_mv Do not tell me more, you are honest: a preconceived honesty bias
title Do not tell me more, you are honest: a preconceived honesty bias
spellingShingle Do not tell me more, you are honest: a preconceived honesty bias
Pascual Ezama, David
Dishonesty
Cheating
Lying
Behavioral profiles
Detection accuracy
Ciencias Sociales
53 Ciencias Económicas
title_short Do not tell me more, you are honest: a preconceived honesty bias
title_full Do not tell me more, you are honest: a preconceived honesty bias
title_fullStr Do not tell me more, you are honest: a preconceived honesty bias
title_full_unstemmed Do not tell me more, you are honest: a preconceived honesty bias
title_sort Do not tell me more, you are honest: a preconceived honesty bias
dc.creator.none.fl_str_mv Pascual Ezama, David
Muñoz García, Adrián
Prelec, Drazen
author Pascual Ezama, David
author_facet Pascual Ezama, David
Muñoz García, Adrián
Prelec, Drazen
author_role author
author2 Muñoz García, Adrián
Prelec, Drazen
author2_role author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv Dishonesty
Cheating
Lying
Behavioral profiles
Detection accuracy
Ciencias Sociales
53 Ciencias Económicas
topic Dishonesty
Cheating
Lying
Behavioral profiles
Detection accuracy
Ciencias Sociales
53 Ciencias Económicas
description According to the previous literature, only a few papers found better accuracy than a chance to detect dishonesty, even when more information and verbal cues (VCs) improve precision in detecting dishonesty. A new classification of dishonesty profiles has recently been published, allowing us to study if this low success rate happens for all people or if some people have higher predictive ability. This paper aims to examine if (dis)honest people can detect better/worse (un)ethical behavior of others. With this in mind, we designed one experiment using videos from one of the most popular TV shows in the UK where contestants make a (dis)honesty decision upon gaining or sharing a certain amount of money. Our participants from an online MTurk sample (N = 1,582) had to determine under different conditions whether the contestants would act in an (dis)honest way. Three significant results emerged from these two experiments. First, accuracy in detecting (dis)honesty is not different than chance, but submaximizers (compared to maximizers) and radical dishonest people (compare to non-radicals) are better at detecting honesty, while there is no difference in detecting dishonesty. Second, more information and VCs improve precision in detecting dishonesty, but honesty is better detected using only non-verbal cues (NVCs). Finally, a preconceived honesty bias improves specificity (honesty detection accuracy) and worsens sensitivity (dishonesty detection accuracy).
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-08-21
2021
2021-08-21
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/128761
url https://hdl.handle.net/20.500.14352/128761
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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