The Effects of Increasing Degree of Unreliable Automation on Older Adults’ Performance

High level automation has the ability to relieve operators from complex, working memory-intensive tasks. When the task is primarily perceptual or cognitive in nature, the amount taken over by the machine can be very high. However, as operators interact with technology that is more automated (i.e., automation is higher in stage and degree), they may become more subject to the negative effects when that technology fails. This concept of reaping greater benefits of higher degrees of automation that is reliable but suffering catastrophic performance consequences when it is unreliable has been termed the lumberjack effect and has been well documented among younger adults (Endsley & Kiris, 1995; Onnasch et al., 2013; Rovira et al., 2017). The cause of this effect is that frequent interaction with reliable, high level automation induces a complacency or disengagement with the task (becoming out of the loop). Thus, when that automation fails, the user has been out of the loop (Endsley & Kiris, 1995) and is thus unprepared to resume the task. As older adults have reduced cognitive abilities, they may be even more subject to the lumberjack effect: benefiting greatly with reliable, high level automation but suffering major performance decrements with unreliable automation. The purpose of the current study was to examine the presence and magnitude of the lumberjack effect in older adults as it has not yet been documented in the literature. Older and younger adults interacted with various levels of automation. We replicated the finding that performance was negatively affected on unreliable trials of automation compared to reliable trials for both age groups (i.e., the lumberjack effect). However, this effect only appeared during low workload conditions and did not appear to be more pronounced in older adults. These results are the first to show that the lumberjack effect, previously observed in younger adults is equally pronounced in older adults. However, what aspect of aging cognition was the source of this similar lumberjack effect is still an empirical question. Future work should be done to understand methods which can help older adults stay in the loop when using automated technology

A Mixed Methodology Study of the Effects of Age, Touchscreens, New Technology, Automation, and Interactions on Pilot Performance

This study examined the effects of age on new technology, touchscreens, automation, and the interaction with pilot performance. Touchscreens have been introduced on the aviation flight deck, combining all pilot tasks in one device in multiple locations. This study is one of the first to examine pilots, touchscreens and age. Previous studies focused on vibration, turbulence, interfaces, ergonomics, and location for incorporating them on the flight deck. This was conducted as an online survey with pilots that have worked with touchscreens in flight operations. The results found that age has an effect on pilots interacting and working with touchscreens. This effect was found with pilots age 60 and above, but there were issues within all age groups interacting and working with touchscreens. Finding the information or path was one issue, as well as layout, design and interface mentioned by all age groups. More training, using actual touchscreens or training devices exactly replicating them, and repetition were stated as ways to alleviate these issues. The amount of touch sensitivity and pressure that are needed to interact and accomplish tasks was another issue that was stated. There is a misunderstanding in some pilots about the differences in devices and touchscreens, capacitive and resistive touch, and the reasons for this. Some pilots that understood the differences still wanted a capacitive touchscreen, like personal devices. The researcher noted that completion of the entire survey from the participants increased as the age increased and the youngest age category had the highest dropout rate

Evaluation of the effects of age-friendly human-machine interfaces on the driver’s takeover performance in highly automated vehicles

The ability to continue driving into old age is strongly associated with older adults’ mobility and wellbeing for those that have been dependant on car use for most of their adult lives. The emergence of highly automated vehicles (HAVs) may have the potential to allow older adults to drive longer and safer. In HAVs, when operating in automated mode, drivers can be completely disengaged from driving, but occasionally they may be required to take back the control of the vehicle. The human-machine interfaces in HAVs play an important role in the safe and comfortable usage of HAVs. To date, only limited research has explored how to design age-friendly HMIs in HAVs and evaluate their effectiveness. This study designed three HMI concepts based on older drivers’ requirements, and conducted a driving simulator investigation with 76 drivers (39 older drivers and 37 younger drivers) to evaluate the effect and relative merits of these HMIs on drivers’ takeover performance, workload and attitudes. Results showed that the ‘R + V’ HMI (informing drivers of vehicle status together with providing the reasons for the manual driving takeover request) led to better takeover performance, lower perceived workload and highly positive attitudes, and is the most beneficial and effective HMI. In addition, The ‘V’ HMI (verbally informing the drivers about vehicle status, including automation mode and speed, before the manual driving takeover request) also had a positive effect on drivers’ takeover performance, perceived workload and attitudes. However, the ‘R’ HMI (solely informing drivers about the reasons for takeover as part of the takeover request) affected older and younger drivers differently, and resulted in deteriorations in performance and more risky takeover for both older and younger drivers compared to the baseline HMI. Moreover, significant age difference was observed in the takeover performance and perceived workload. Above all, this research highlights the significance of taking account older drivers’ requirements into the design of HAVs and the importance of collaboration between automated vehicle and cooperative ITS research communities

The Effect of Age and Advice Accuracy on Compliance with Decision Support

This thesis was designed to determine whether age or the accuracy of advice provided significantly effects compliance with a computerized decision support assistant. 48 participants in two groups, aged 20-40 (younger adults) and 41-69 (older adults), performed a monitoring/vigilance task intended to be similar to screening baggage with an X-ray monitor. A decision support assistant was provided to assist participants in choosing one out of four gray circles that had the most contrast with the background screen. Compliance with the decision support assistant\u27s advice was then assessed. Results indicated that the level of advice accuracy did have a significant effect on compliance with decision support. As the advice accuracy level decreased, compliance decreased for both age groups. Although previous literature indicates that older adults may have negative attitudes toward computers, no significance was found for age or the interaction effect of age and advice accuracy on compliance with decision support technology

Investigating older drivers' takeover performance and requirements to facilitate safe and comfortable human-machine interactions in highly automated vehicles

PhD ThesisThe forthcoming highly automated vehicles (HAVs) may potentially benefit older drivers. However, limited research have investigated the their performance and requirements when interacting with HAVs in order to provide an understanding of what would facilitate a safe and comfortable human-machine interaction with HAVs for them. This thesis fills the research gap using a range of quantitative and qualitative methodologies through four investigations. Firstly, a driving simulator investigation was conducted with 76 participants (39 older and 37 younger drivers) to investigate the effects of age and the state of complete disengagement from driving on the takeover performance. This investigation found that age and complete disengagement from driving negatively affect takeover performance. Then, a second driving simulator investigation was conducted to investigate the effect of age and adverse weather conditions on takeover performance. It was found that age affects takeover performance. And adverse weather conditions, especially snow and fog, lead to a deteriorated takeover performance. Next, a qualitative interview investigation was implemented with 24 older drivers who participated the two driving simulator investigations. This study yielded a wide range of older drivers’ requirements towards the human-machine interactions in HAVs, especially towards the periods of automated driving and taking over control. Lastly, in the third driving simulator investigation, three human-machine interfaces (HMIs) of HAVs were designed based on older drivers’ requirements, their effectiveness on enhancing drivers’ takeover performance were evaluated. It has found that the HMI informing drivers of vehicle status together with the reasons for takeover is the most beneficial HMI to the drivers of HAV. Based on the findings above, the thesis proposed recommendations for facilitating safe and comfortable human-machine interactions in HAVs for older drivers. The thesis concluded the importance of fully considering older adults’ performance, capabilities and requirements during the design of human-machine interactions in HAV