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The Influence of Task Load and Automation Trust on Deception Detection
Authors:David P Biros  Mark Daly  Gregg Gunsch
Institution:(1) Department of Systems and Engineering Management, Air Force Institute of Technology, 2950 Hobson Boulevard, Wright-Patterson AFB, OH, 45433, USA E-mail;(2) 33rd Information Operation Squadron, Lackland AFB, TX, 78243, USA E-mail;(3) Department of Engineering and Computer Science, 2950 Hobson Boulevard, Wright-Patterson AFB, OH o45433, USA E-mail
Abstract:The purpose of this research was to investigate the effects that user task load level has on the relationship between an individual's trust in and subsequent use of a system's automation. Military decision-makers trust and use information system automation to make many tactical judgments and decisions. In situations of information uncertainty (information warfare environments), decision-makers must remain aware of information reliability issues and temperate their use of system automation if necessary. An individual's task load may have an effect on his use of a system's automation in environments of information uncertainty.It was hypothesized that user task load will have a moderating effect on the positive relationship between system automation trust and use of system automation. Specifically, in situations of information uncertainty (low trust), high task load will have a negative effect on the relationship. To test this hypothesis, an experiment in a simulated command and control micro-world was conducted in which system automation trust and individual task load were manipulated. The findings from the experiment support the positive relationship between automation trust and automation use found in previous research and suggest that task load does have a negative effect on the positive relationship between automation trust and automation use. Experiment participant who incurred a higher task load exhibited an over-reliance on their automated information systems to assist them in their decision-making activities. Such an over-reliance can lead to vulnerabilities of deception and suggests the need for automated deception detection capabilities.
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