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Predicting battlefield vigilance: a multivariate approach to assessment of attentional resources
Technological innovation increasingly requires operators in various applied settings to maintain vigilance for extended periods. However, standard psychometric tests typically predict less than 10% of performance variance. The present study (N = 462) aimed to apply the resource theory of sustained a...
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Published in: | Ergonomics 2014-06, Vol.57 (6), p.856-875 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Technological innovation increasingly requires operators in various applied settings to maintain vigilance for extended periods. However, standard psychometric tests typically predict less than 10% of performance variance. The present study (N = 462) aimed to apply the resource theory of sustained attention to construct a multivariate test battery for predicting battlefield vigilance. The battery included cognitive ability tests, a high-workload short vigilance task and subjective measures of stress response. Four versions of a 60- min simulated military battlefield monitoring task were constructed to represent different operational requirements. The test battery predicted 24-44% of criterion variance, depending on task version, suggesting that it may identify vigilant operators in military and other applied contexts. A multiple-groups path analysis showed that relationships between ability and vigilance were moderated by working memory demands. Findings are consistent with a diffuse theoretical concept of 'resources' in which performance energisation depends on multiple, loosely coupled processes.
Practitioner Summary: Assessment of operators' competence in vigilant monitoring is increasingly important as automation technology becomes more prevalent. This study investigated the validity of a battery of measures of attentional resources in predicting vigilance on a military display monitoring task. Findings confirm that the multivariate approach substantially enhances prediction over existing approaches. |
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ISSN: | 0014-0139 1366-5847 |
DOI: | 10.1080/00140139.2014.899630 |