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Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants

Studies on hacking have typically focused on motivational aspects and general personality traits of the individuals who engage in hacking; little systematic research has been conducted on predispositions that may be associated not only with the choice to pursue a hacking career but also with perform...

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Autores principales: Harvey, India, Bolgan, Samuela, Mosca, Daniel, McLean, Colin, Rusconi, Elena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868920/
https://www.ncbi.nlm.nih.gov/pubmed/27242491
http://dx.doi.org/10.3389/fnhum.2016.00229
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author Harvey, India
Bolgan, Samuela
Mosca, Daniel
McLean, Colin
Rusconi, Elena
author_facet Harvey, India
Bolgan, Samuela
Mosca, Daniel
McLean, Colin
Rusconi, Elena
author_sort Harvey, India
collection PubMed
description Studies on hacking have typically focused on motivational aspects and general personality traits of the individuals who engage in hacking; little systematic research has been conducted on predispositions that may be associated not only with the choice to pursue a hacking career but also with performance in either naïve or expert populations. Here, we test the hypotheses that two traits that are typically enhanced in autism spectrum disorders—attention to detail and systemizing—may be positively related to both the choice of pursuing a career in information security and skilled performance in a prototypical hacking task (i.e., crypto-analysis or code-breaking). A group of naïve participants and of ethical hackers completed the Autism Spectrum Quotient, including an attention to detail scale, and the Systemizing Quotient (Baron-Cohen et al., 2001, 2003). They were also tested with behavioral tasks involving code-breaking and a control task involving security X-ray image interpretation. Hackers reported significantly higher systemizing and attention to detail than non-hackers. We found a positive relation between self-reported systemizing (but not attention to detail) and code-breaking skills in both hackers and non-hackers, whereas attention to detail (but not systemizing) was related with performance in the X-ray screening task in both groups, as previously reported with naïve participants (Rusconi et al., 2015). We discuss the theoretical and translational implications of our findings.
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spelling pubmed-48689202016-05-30 Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants Harvey, India Bolgan, Samuela Mosca, Daniel McLean, Colin Rusconi, Elena Front Hum Neurosci Neuroscience Studies on hacking have typically focused on motivational aspects and general personality traits of the individuals who engage in hacking; little systematic research has been conducted on predispositions that may be associated not only with the choice to pursue a hacking career but also with performance in either naïve or expert populations. Here, we test the hypotheses that two traits that are typically enhanced in autism spectrum disorders—attention to detail and systemizing—may be positively related to both the choice of pursuing a career in information security and skilled performance in a prototypical hacking task (i.e., crypto-analysis or code-breaking). A group of naïve participants and of ethical hackers completed the Autism Spectrum Quotient, including an attention to detail scale, and the Systemizing Quotient (Baron-Cohen et al., 2001, 2003). They were also tested with behavioral tasks involving code-breaking and a control task involving security X-ray image interpretation. Hackers reported significantly higher systemizing and attention to detail than non-hackers. We found a positive relation between self-reported systemizing (but not attention to detail) and code-breaking skills in both hackers and non-hackers, whereas attention to detail (but not systemizing) was related with performance in the X-ray screening task in both groups, as previously reported with naïve participants (Rusconi et al., 2015). We discuss the theoretical and translational implications of our findings. Frontiers Media S.A. 2016-05-17 /pmc/articles/PMC4868920/ /pubmed/27242491 http://dx.doi.org/10.3389/fnhum.2016.00229 Text en Copyright © 2016 Harvey, Bolgan, Mosca, McLean and Rusconi. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Harvey, India
Bolgan, Samuela
Mosca, Daniel
McLean, Colin
Rusconi, Elena
Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title_full Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title_fullStr Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title_full_unstemmed Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title_short Systemizers Are Better Code-Breakers: Self-Reported Systemizing Predicts Code-Breaking Performance in Expert Hackers and Naïve Participants
title_sort systemizers are better code-breakers: self-reported systemizing predicts code-breaking performance in expert hackers and naïve participants
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868920/
https://www.ncbi.nlm.nih.gov/pubmed/27242491
http://dx.doi.org/10.3389/fnhum.2016.00229
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