2026-06-27T15:46:38Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/1287612025-12-12T01:04:35Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Pascual Ezama, David
author
Muñoz García, Adrián
author
Prelec, Drazen
2025-12-11T12:02:05Z
2025-12-11T12:02:05Z
2021-08-21
Pascual-Ezama D, Muñoz A and Prelec D (2021) Do Not Tell Me More; You Are Honest: A Preconceived Honesty Bias. Front. Psychol. 12:693942. doi: 10.3389/fpsyg.2021.693942
10.3389/fpsyg.2021.693942
https://hdl.handle.net/20.500.14352/128761
1664-1078
https://doi.org/10.3389/fpsyg.2021.693942
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2021.693942/full
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).
eng
Attribution 4.0 International
Do not tell me more, you are honest: a preconceived honesty bias
journal article
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
URL
https://docta.ucm.es/bitstreams/074e05a3-41b8-4170-8c37-e0ad0ed5b0e3/download
File
MD5
bdcc6e4c0c4886730ca513cb33d6355d
248132
application/pdf
Do not tell me more.pdf