Title

Predicting Battlefield Vigilance: A Multivariate Approach To Assessment Of Attentional Resources

Keywords

attentional resources; cognitive ability; fatigue; military performance; stress; vigilance

Abstract

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. © 2014 © 2014 Taylor & Francis.

Publication Date

1-1-2014

Publication Title

Ergonomics

Volume

57

Issue

6

Number of Pages

856-875

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/00140139.2014.899630

Socpus ID

84901690334 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84901690334

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