Ciara Sibley

Eye tracking is a non-invasive method to measure an individual’s cognitive state and visual attention allocation patterns, both of which provide valuable insight into their real-time needs when completing complex tasks. With the availability of low-cost systems and the wealth of available information, eye tracking is ever more common in human factors research and applications. However, access to comprehensive, approachable, and adaptable information on various eye tracking metrics is limited. Therefore, we created a publicly available, goal-driven, interactive guide called Generalized Eye Tracking Metrics (GEMs). It supports the learning of (1) metrics, (2) relevant terminology, and (3) analysis approaches, as it relates to the user’s research questions and experimental setup. Our findings from focus groups, interviews, and usability testing among novices and experts indicated that GEMs is a highly usable information source created by and for eye tracking researchers.

We present a theoretical framework and empirical data from a sample of 490 U.S. Navy trainees (air traffic controllers, student naval aviators, and student naval flight officers) exploring the use of attention control tests in personnel selection. Current military selection tests leave room for improvement when predicting individual differences in work-relevant outcomes and reducing adverse impact. We tested whether measures of attention control, working memory capacity, and fluid intelligence improved the prediction of training success above and beyond composite scores currently used by the U.S. Military. For air traffic controllers in training, commonality analyses revealed that attention control explained 9.1% of the unique variance in academic performance, whereas the Armed Forces Qualification Test explained 5.2% of the unique variance. For student naval aviators, incremental validity estimates were generally small and non-significant. For student naval flight officers, commonality analyses revealed that attention control measures explained 11.8% of the unique variance in aviation preflight indoctrination training performance and 4.3% of the unique variance in flight performance. Although these point estimates are based on relatively small samples, they provide preliminary evidence that attention control measures might improve training outcome classification accuracy in real-world samples of military personnel.

We compare the validity of personnel selection measures and novel tests of attention control for explaining individual differences in synthetic work performance, which required participants to monitor and complete multiple ongoing tasks. In Study 1, an online sample of young adults (N = 474, aged 18-35) based in the United States completed three-minute tests of attention control and two tests that primarily measure acquired knowledge, the Wonderlic and the Armed Forces Qualification Test (AFQT). Structural equation modeling revealed that acquired knowledge tests did not predict simulated work performance beyond attention control, whereas attention control did predict simulated work performance controlling for other measures. In Study 2, an in-lab sample of young adults from Georgia Tech and the greater Atlanta community (N = 321, aged 18-35) completed tests of attention control, processing speed, working memory capacity, and versions of two U.S. Military selection tests, one assessing acquired knowledge (the AFQT) and one assessing psychomotor ability (the Performance-Based Measures assessment from the Aviation Selection Test Battery). Structural equation modeling revealed that attention control fully mediated the relationship between the Performance Based Measures and simulated work performance, but the AFQT and processing speed retained unique prediction. We also explore possible gender differences. Collectively, these results suggest that tests of attention control may be a useful supplement to existing personnel selection measures when complex cognitive tasks are the criterion variable of interest.

We present a theoretical framework and empirical data from a sample of 490 U.S. Navy trainees (air traffic controllers, student naval aviators, and student naval flight officers) supporting the use of attention control tests in personnel selection. Current military selection tests leave room for improvement when predicting individual differences in work-relevant outcomes and reducing adverse impact. We tested whether measures of attention control, working memory capacity, and fluid intelligence improved the prediction of training success above and beyond the current composite scores used by the U.S. Military. For air traffic controllers in training, attention control explained 9.1% of the unique variance in academic performance, whereas the Armed Forces Qualification Test explained 5.2% of the unique variance. For student naval aviators, incremental validity estimates were generally small and non-significant. For student naval flight officers, attention control measures explained 11.8% of the unique variance in aviation preflight indoctrination training performance and 4.3% of the unique variance in flight performance. In general, attention control measures improved training outcome classification accuracy, and the antisaccade and selective visual arrays tests of attention control demonstrated relatively small differences between majority and minority subgroups.

We present a theoretical framework and empirical data from a sample of 490 U.S. Navy trainees (air traffic controllers, student naval aviators, and student naval flight officers) supporting the use of attention control tests in personnel selection. Current military selection tests leave room for improvement when predicting individual differences in work-relevant outcomes and reducing adverse impact. We tested whether measures of attention control, working memory capacity, and fluid intelligence improved the prediction of training success above and beyond the current composite scores used by the U.S. Military. For air traffic controllers in training, attention control explained 9.1% of the unique variance in academic performance, whereas the Armed Forces Qualification Test explained 5.2% of the unique variance. For student naval aviators, incremental validity estimates were generally small and non-significant. For student naval flight officers, attention control measures explained 11.8% of the unique variance in aviation preflight indoctrination training performance and 4.3% of the unique variance in flight performance. In general, attention control measures improved training outcome classification accuracy, and the antisaccade and selective visual arrays tests of attention control demonstrated relatively small differences between majority and minority subgroups.