Young Driver Emotions Relate To Their Intention To Engage In Cellphone Distraction

Young drivers’ disproportionate involvement in road crashes due to cellphone distractions calls for remedial programs. Development of such programs requires an understanding of the reasons behind engagement in distractions and identifying factors to leverage, such as emotions. This investigation sought to determine how young drivers’ intention to engage in cellphone distractions is associated with negative emotions. An online survey was administered to 99 young drivers (18 to 25). Participants rated their intention to engage in cellphone distractions while driving and their anticipation of negative emotions while doing so. Our results showed an association between anticipating negative emotions and the intention to engage in cellphone distractions while driving. There was no difference between males and females in their reported intention. However, females reported greater anticipation of negative emotions compared to males. These findings have implications for emotion-based manipulations aimed at reducing distracted driving among young drivers.

Shipboard Operations Room Layout Analysis Using Modeling and Simulation

The introduction of low-profile consoles into shipboard operations room opens new possibilities to design its spatial layout. A modeling study was conducted to examine six layout options by manipulating two design factors: console orientation and supervisor seat assignment. The quality of each option was evaluated algorithmically based on its support to operator interaction. The results revealed the strengths and weaknesses of each design: face-to-face was found to be superior for supporting visual and auditory communication, face-to-back was better at facilitating tactile interaction, whereas back-to-back was preferable for an operations room where extensive interaction involved operators moving to one another’s workstations. Benefits were also predicted for configurations where supervisors were assigned to a side seat, primarily for improving the cost scores of tactile and distance interaction links. Results from this study strongly support the inclusion of faceto-face setup and alternative supervisor seating assignment as options in future shipboard operations room design.

Decision Deferral in a Human-AI Joint Face-Matching Task: Effects on Human Performance and Trust

This study investigates how human performance and trust are affected by the decision deferral rates of an AI-enabled decision support system in a high criticality domain such as security screening, where ethical and legal considerations prevent full automation. In such domains, deferring cases to a human agent becomes an essential process component. However, the systemic consequences of the rate of deferrals on human performance are unknown. In this study, a face-matching task with an automated face verification system was designed to investigate the effects of varying deferral rates. Results show that higher deferral rates are associated with higher sensitivity and higher workload, but lower throughput and lower trust in the AI. We conclude that deferral rates can affect performance and trust perceptions. The tradeoffs between deferral rate, sensitivity, throughput, and trust need to be considered in designing effective human-AI work systems.

Examining human perception of weight during loaded standing and walking

While the psychophysics of weight perception may help assess the effort needed in manual material handling tasks, the perception of weight is subjective and not necessarily accurate. The purpose of this study was to examine weight perception during standing and walking. Participants (n=10) performed a series of weight comparison trials against a reference load while holding loads (standing) or carrying loads (walking). Polynomial logistic regression models were built to examine the effects of walking, box weight ratio, and reference weight level on the probability of detecting a weight difference. The results showed that weight ratio and reference weight level had statistically significant effects on the detection probability while walking did not have a significant effect. Findings from this study can help inform the design of subjective evaluation of job demands involving motion, and it can be further extended to the gradual increase in load of strengthening tasks in therapeutic exercises.

Human Factors in Interactive Machine Learning: A Cybersecurity Case Study

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.

SEEV Modeler: A GUI-Based Tool for the SEEV Model Simulation

The SEEV model of visual scanning offers a quick and easy way of evaluating the attentional demands of various tasks and displays. A SEEV model can be developed without relying on complicated mathematical software or background, making the conceptual model highly accessible. Implementation of SEEV modeling can further be improved by easing the process of running simulations and providing actionable information. In this paper, we showcase the SEEV Modeler, a GUI-based prototype of the computational SEEV model that lowers the technical barriers for human factors practitioners. Furthermore, the prototype’s ability to predict eye movements in dynamic driving scenarios was tested, with an emphasis on the impacts of the attention shifting effort and inhibition of return (IOR) on the model’s prediction performance. The SEEV Modeler produced model fits comparable to those of previous mathematical modeling approaches but also revealed limitations and practical issues to be addressed in the final version.

Human Factors Engineering: Designing for Diversity and Accessibility

As the Human Factors and Ergonomics Society Diversity, Equity, and Inclusion Committee enters its fifth year, it continues to encourage the society, annual meeting attendees, and human factors professionals to improve diversity and equity within the field. At the center of this field are humans and their widely varying needs and abilities. While HFE professionals devote themselves to these needs, their details are often overlooked in order to design for what is assumed to be a majority of users. These assumptions can then lead users to be rejected by products, systems, or objects. This rejection indicates a lack of accessibility, which affects millions worldwide. In this panel, experts in the areas of universal design, healthcare, and accessible design will discuss how to “do” accessibility while demonstrating that accessibility should be considered a required component of usability.

Age Differences in the Malleability of Attitudes toward Automated Shared Mobility

Introduction: The use of shared automated vehicles (SAVs) should lead to several societal and individual benefits, including reduced greenhouse gas emissions, reduced traffic, and improved mobility for persons who cannot safely drive themselves. We define SAVs as on-demand, fully automated vehicles in which passengers are paired with other riders traveling along a similar route. Previous research has shown that younger adults are more likely to report using conventional ridesharing services and are more accepting of new technologies including automated vehicles (AVs). However, older adults, particularly those who may be close to retiring from driving, stand to greatly benefit from SAV services. In order for SAVs to deliver on their aforementioned benefits, they must be viewed favorably and utilized. We sought to investigate how short educational and/or experiential videos might impact younger, middle-aged, and older adult respondents’ anticipated acceptance and attitudes toward SAVs. Knowing what types of introductory experiences improve different age groups’ perceptions of SAVs will be beneficial for tailoring campaigns aiming to promote SAV usage. Methods: We deployed an online survey using the platform Prolific for middle-aged and older respondents, and our departmental participant pool for younger adults, collecting 585 total responses that resulted in 448 valid responses. Respondents answered questions regarding their demographic attributes, their ridesharing history, preconceptions of technology, as well as their anticipated acceptance attitudes towards SAVs as measured by the dimensions of the Automated Vehicle User Perception Survey (AVUPS). After this, respondents were randomly assigned to an intervention condition where they either watched 1) an educational video about how SAVs work and their potential benefits, 2) an experiential video showing a AV navigating traffic, 3) both the experiential and educational videos, or 4) a control video explaining how ridesharing works. Anticipated acceptance attitudes towards SAVs were measured again after this intervention and difference scores calculated to investigate the effect of the intervention conditions. Prolific respondents were paid at a rate of $9.50/hour and younger adults received course credit. Results: Controlling for preconceptions of technology and ridesharing experience, a MANOVA was run on the difference scores of the dimensions of the AVUPS (intention to use, trust/reliability, perceived usefulness (PU), perceived ease of use (PEOU), safety, control/driving-efficacy, cost, authority, media, and social influence). Both older and middle-aged adults expressed significantly greater increases in PEOU and PU of SAVs than younger adults. We also observed an interaction between age and condition for both PU and PEOU. For PU, older adults’ difference scores were found to be significantly greater than younger adults’ for the control video condition. With PEOU, older adults’ difference scores were significantly greater than both younger adults’ for the control video condition, and middle-aged adults had greater difference scores for the educational-only video condition than younger or older adults. Discussion: The increases in PU observed for older adults in the control condition suggests that educating them on how to use currently available ridesharing services might transfer to and/or highlight the benefits that automated ridesharing might provide. The PEOU interactions also suggest that middle-aged adults might respond more positively than younger or older adults to an educational introduction to SAVs. Conclusion: The positive findings pertaining to PU and PEOU show that exposure to information related to SAVs has a positive impact on these attitudes. PU’s and PEOU’s positive relationship to behavioral intentions (BI) in the Technology Acceptance Model, coupled with the findings from this study, bode well for higher fidelity interventions seeking to inform and/or give individuals experience with SAVs. Providing information on how currently available ridesharing services work helped our older adult respondents recognize the potential usefulness of SAVs. Knowing that different age groups may respond better to educational versus experiential interventions, for example middle-aged adults in this study responding more positively to the educational video condition than younger or older adults, may be useful for targeted promotional campaigns.

Mitigating the Effects of Task Switching in Baggage Screeners

Using a simulated baggage screening task, we investigated two literature-supported mitigation strategies for reducing the negative effects of task switching, namely less frequent switching and memory support. The study replicates widely reported switching effects on a complex task. The results also show that people can improve performance when provided memory support. When task switching, people can struggle to retrieve the correct task instruction due to the automatic process behind functional memory decay. Memory support reduces the negative effects of functional decay by providing people a reminder.

Smartphone and Digital Home Assistant Use Among Older Adults: Understanding Adoption and Learning Preferences

Smart technologies have increased dramatically within the last decade. Their availability has improved opportunities for productivity, health, and entertainment, and this is especially true for technologies that act as central hubs to link other devices and applications that expand their capabilities (e.g., smartphones and digital home assistants). Older adults may significantly benefit from integrating these devices into their lives to mitigate the various challenges they face with increasing age (e.g., cognitive, mobility changes). To understand the potential of these technologies for this population, we investigated use patterns, learning preferences, and other perceptions related to the adoption of these devices. Seventy older adult participants responded to an online survey regarding their use and preferences with smartphones and digital home assistants. The results informed our understanding of current adoption rates as well as provided key perspectives to inform their design for older adults.