December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Cybersecurity and Visual Fatigue
Author Affiliations
  • Genna Telschow
    University of Central Florida
  • Mark Neider
    University of Central Florida
Journal of Vision December 2022, Vol.22, 3406. doi:https://doi.org/10.1167/jov.22.14.3406
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      Genna Telschow, Mark Neider; Cybersecurity and Visual Fatigue. Journal of Vision 2022;22(14):3406. https://doi.org/10.1167/jov.22.14.3406.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

While checking emails, it is important to decide if an email is trustworthy or a phishing attack, a socially engineered deception tactic designed to gain access to secure information or money. The work performance literature suggests that fatigue impairs cognition and slows response times. More specifically, visual fatigue from electronic devices impairs reading comprehension. In previous work we found evidence for a relationship between email classification and fatigue (Telschow & Neider, 2021). The goal of the current study was to transition from broadly studying fatigue’s impact on email classification to more specifically investigating how visual fatigue may impair email classification. Participants were assigned to one of three conditions (control, low-fatigue, and high-fatigue) prior to completing an email classification task in which they classified emails as legitimate or non-legitimate. Participants in the low and high fatigue conditions read chapters 1 through 5 of War and Peace, while those in the control condition did not read anything prior to the email task. Fatigue was manipulated using text contrast; high fatigue participants read yellow low contrast text and the low fatigue participants read black high contrast text on a white background. This manipulation of visual fatigue was adapted from studies of workplace lighting and performance to naturalistically mimic factors that impact work performance. Using the Visual Fatigue Survey and the Fatigue Survey we verified that visual fatigue increased from the control condition to the low and high -fatigue conditions (all p’s< .001). Email classification accuracy was generally poor (~70%; p= 0.55) and did not differ across fatigue conditions. Similarly, there were no differences across fatigue conditions in email classification time (p= .82). We did not find differences across conditions for response bias (p= .67) or d' (p= .79). Overall, our data suggests that visual fatigue may not impair email classification.

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