Detection and Mitigation of Inefficient Visual Searching
Document Type
Article
Publication Date
12-2020
Abstract
A commonly known cognitive bias is a confirmation bias: the overweighting of evidence supporting a hy- pothesis and underweighting evidence countering that hypothesis. Due to high-stress and fast-paced opera- tions, military decisions can be affected by confirmation bias. One military decision task prone to confirma- tion bias is a visual search. During a visual search, the operator scans an environment to locate a specific target. If confirmation bias causes the operator to scan the wrong portion of the environment first, the search is inefficient. This study has two primary goals: 1) detect inefficient visual search using machine learning and Electroencephalography (EEG) signals, and 2) apply various mitigation techniques in an effort to im- prove the efficiency of searches. Early findings are presented showing how machine learning models can use EEG signals to detect when a person might be performing an inefficient visual search. Four mitigation techniques were evaluated: a nudge which indirectly slows search speed, a hint on how to search efficiently, an explanation for why the participant was receiving a nudge, and instructions to instruct the participant to search efficiently. These mitigation techniques are evaluated, revealing the most effective mitigations found to be the nudge and hint techniques.
Source Publication
Proceedings of the Human Factors and Ergonomics Society Annual Meeting (ISSN 1071-1813 | eISSN 1541-9312)
Recommended Citation
Gallaher, J. P., Kamrud, A. J., & Borghetti, B. J. (2020). Detection and Mitigation of Inefficient Visual Searching. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 64(1), 47-51. https://doi.org/10.1177/1071181320641015
Comments
© 2019 by Human Factors and Ergonomics Society.
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Article first published online: February 9, 2021