Young drivers are overrepresented in motor vehicle crashes, and are shown to be poorer at anticipating potential threats on the roadway compared with their more experienced peers. Literature demonstrates the effectiveness of driver training programs at improving young drivers’ latent hazard anticipation performance. Various hazard anticipation training studies have been undertaken on different population demographics using different training scenario presentation modes and multiple evaluation testbeds. These error-based feedback training programs (3M) allow trainees to make a mistake, show them how to mediate the mistake, and provide an opportunity to master the target skills. The current meta-analytical review focused on 19 peer-reviewed training studies that utilized eye movements to measure improvements in drivers’ latent hazard anticipation performance following training. The role of four moderating factors (mode of delivery – PC-based or non PC-based; presentation of training – egocentric or exocentric; method of evaluation – on-road or driving simulator; and age of sample – teen novices aged 16–17 or young drivers aged 18–21) on the training effects were explored. Overall, the current meta-analysis suggest that: (a) superficial improvements in training programs does not necessarily further improve the drivers’ latent hazard anticipation; (b) drivers who completed a training program with both egocentric and exocentric training views achieved greater levels of latent hazard anticipation performance than those who completed a training program that contained either view, but not both; and (c) the effect sizes of the 3M-based training programs on latent hazard anticipation were greater for drivers aged 18–21 years than drivers aged 16–17.

Objective We searched for the application of usability in the literature with a focus on adoption, measurements employed, and demonstrated value. Five human factors domains served as a platform for our reflection, which included the last 20 years. Background As usability studies continue to accumulate, there has been only a little past reflection on usability and contributions across a variety of applications. Our research provides a background for general usability, and we target specific usability research subareas within transportation, aging populations, autistic populations, telehealth, and cybersecurity. Method “Usability” research was explored across five different domains within human factors. The goal was not to perform an exhaustive review but, rather, sample usability practices within several specific subareas. We focused on answering three questions: How was usability adopted? How was it measured? How was it framed in terms of value? Conclusion We found that usability is very domain specific. Usability benchmarking studies and empirical standards are rare. The value associated with improving usability ranged widely—from monetary benefits to saving lives. Thus, researchers are motivated to further improve usability practices. A number of data collection and interpretation challenges still call for solutions. Application Findings offer insight into the development of usability, as applied across a variety of subdomains. Our reflection ought to inform future theory development efforts. We are concerned about the lack of established benchmarks, which can help ground data interpretation. Future research should address this gap in the literature. We note that our findings can be used to develop better training materials for future usability researchers.

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Cognitive distraction has been identified as a critical factor for automobile crashes in the United States. Previous research indicates that cognitive tasks such as phone conversations may take attention away from the driving task, degrading driving performance. One continuing issue with cognitive distraction research is establishing a reliable, continuous, online measure of driver workload. Blink rate is a potential candidate for such a measure. The present driving simulator experiment examines whether blink rate is sensitive to variations of driver workload while controlling the vehicle. Participants will navigate simulated environments while performing the auditory 0-and 2-back task with their blink and driving performance data continuously recorded during the drive. Blink and performance data will be compared not only between the different workload conditions, but also to predictions of a human driver performance workload model based on the Improved Performance Research Integration Tool (IMPRINT), developed by the US Army Research Laboratory.