Program Schedule
Register / Home
| All Times Eastern |
Tuesday, November 11 |
Wednesday, November 12 |
| 11:30am - 1:00pm |
TBA Panel Discussion
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| 1:00pm - 2:00pm |
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- Culture and Driver Behavior: An International Study
- Combining Driving Automation Intent and Hazard Information with an Attention Reminder System: Is More Information Always Better?
- Feasibility and Design of a Virtual Reality Tool to Support Lung Cancer Patients' Treatment Preparedness: EveryBreathMatters
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| 2:00pm - 3:00pm |
- Brains and Bots: EEG Insights into Moral Learning with Social Robots and Text-Based Methods
- Biomathematical Modeling of Fatigue and Cognitive Performance
- Integrating Individual Differences Metrics into Cognitive Models
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- Comparing and Contrasting N-Back, and Self-Directed Memory Selection, as Measures of Working Memory
- Measuring Anticipatory Monitoring Skills Using a Crew Briefing Task
- Mental Health Safety Challenges in Pre-Hospital Emergency Medical Service Providers: A Scoping Review
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Program Details
Biomathematical Modeling of Fatigue and Cognitive Performance
Contributors: Bella Veksler, Tier1 Performance Solutions; Megan Morris, Air Force Research Laboratory; Garrett Swan, Aptima, Inc.; Arielle Stephenson, BAE Systems Inc.; Jessica Tuttle, University of Dayton Research Institute
Cognitive fatigue is a prevalent factor in military communities, resulting in performance degradations and contributing to costly mishaps. It is critical that organizations can predict fatigue effects of cognitive processes in order to implement countermeasures to allay fatigue. Biomathematical models are one means to objectively predict fatigue effects. This effort focuses on examining the relationship between biomathematical model predictions and performance on different cognitive tasks during an operationally relevant 24-hr simulated mobility mission. Thirty-nine mobility pilots wore an actigraph watch while self-reporting sleep, and completed several bouts of psychomotor vigilance, working memory, and executive functioning tasks throughout the mission simulation. Pilots’ sleep data was processed through the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) biomathematical fatigue model and corresponding performance effectiveness values were extracted for each bout and each task. We examined the percentage of pilots who had significant correlations between their performance effectiveness values and the corresponding task dependent measures. We found that for certain participants, biomathematical predictions of fatigue were highly correlated with performance on the psychomotor vigilance task, particularly if the range of effectiveness values varied for that participant. Correlations with other tasks were not as profound, suggesting psychomotor vigilance performance is more sensitive to fatigue.
Brains and Bots: EEG Insights into Moral Learning with Social Robots and Text-Based Methods
Contributors: Sakshi Chauhan, Indian Institute of Technology Mandi; Dr. Palvi Aggarwal, University of Texas, El Paso; Dr. Varun Dutt, Indian Institute of Technology Mandi
How do we more effectively teach moral virtues such as responsibility and fairness? This research identifies novel means of involving students in moral education, a basis for empathy and ethical decision-making. Standard text-based teaching, although efficient, has the danger of being overly theoretical and hard to relate to. To overcome these problems, we implemented two different ways of teaching moral principles: an interactive lesson with a speech-capable robot and a simple reading task, and a control group with no teaching. Seventy-five students aged 18–30 were randomly divided into one of the three groups. We tested their knowledge, monitored their interest in questionnaires, and measured their brain activity on EEG headbands to see how they processed the information better. Outcomes indicated that both the interactive and reading groups scored significantly higher than the control group on tests of comprehension of moral concepts. EEG outcomes also detected heightened mental effort in tasks engaged in learning, although a difference between the two active treatments was not found. These results indicate that active, participatory learning approaches—traditional or technology-enhanced—can optimize moral education performance. From a human factors point of view, providing multiple modes for learning can facilitate the needs and preferences of today's learners.
Comparing and Contrasting N-Back, and Self-Directed Memory Selection, as Measures of Working Memory
Contributors: Yifei Zhou, University of Toronto; You Zhi Hu, University of Toronto; Marc Chignell, University of Toronto
Working memory (WM) is a fundamental cognitive function that is measured using various paradigms, yet different tasks may assess distinct aspects of this cognitive function. This study investigates the relationship between self-directed selection from memory sets and performance on the N-back task, a widely used WM measure. A total of 109 students participated in the study, consisting of 57 females, 51 males, and one student who preferred not to disclose their gender. The results showed that the N-back tasks and self-directed memory tasks measure are significantly correlated, although at r=0.29 the correlation is not particularly high, perhaps partly because our sample of engineering students likely had much lower variation in working memory ability than the general population. Participants also played a numerical Stroop game. The gamified N-Back Task was significantly correlated with the gamified numerical Stroop task, whereas our gamified self-directed memory selection task was not. This raises the possibility that self-directed memory selection is a purer measure of working memory than the N-Back task, because there is less involvement of cognitive flexibility (as exemplified by the numerical Stroop task) in its performance.
Challenges Diversity, Equity, & Inclusion in the Current Political Climate
Panelists: Christy Harper, End to End Research; Jules Trippe, EFN; Abigail Wooldridge, University of Illinois; Sarah Coppola, University of Washington
Moderator: Heather Lum, University of Arizona
The human factors profession is dedicated to optimizing the interaction between humans and systems, ensuring safety, efficiency, and overall well-being. Given its central focus on human behavior, cognition, and physical capabilities, diversity, equity, and inclusion (DEI) are essential components in advancing the field (Gill, McNally, & Berman, 2018). However, many human factors professionals have found themselves struggling with how, when, and if they should consider DEI given the current presidential administration’s policies that have slashed DEI from government, education, and private sectors. Yet, embracing DEI in human factors enhances problem-solving, improves design outcomes, fosters ethical responsibility, and ultimately leads to more inclusive and effective solutions for diverse populations. The goal of this panel is to discuss the current challenges related to DEI in the current political climate and how we may continue to pursue such efforts within our respective professions.
Combining Driving Automation Intent and Hazard Information with an Attention Reminder System: Is More Information Always Better?
Contributors: Dina Kanaan, University of Toronto; Birsen Donmez, University of Toronto
Automation can result in driver inattention and distraction, which has contributed to fatal collisions. One common safeguard implemented by automakers is “attention reminders” (ARs), which issue alerts when drivers are inattentive. Despite some observed benefits, it is unclear whether ARs alone can ensure safe operation of automation. Thus, ARs might be enhanced by combining them with displays conveying additional information, such as automation intent and surrounding hazards. A driving simulator study was conducted to evaluate the effects of combining ARs with additional information on driver visual attention (measured through gaze behavior). Forty-eight participants were assigned to one of three conditions: Baseline AR, AR + automation intent (that the automation would slow down the car), and AR + automation intent + hazard information (location & severity of potential hazard). The findings suggest that combining ARs with both automation intent and hazard information may have diverted attention away from cues in the environment indicating potential traffic conflicts, while combining the AR with automation intent only supported visual attention to the cues. However, the additional information did not show a performance benefit compared to the Baseline AR. Thus, further research should be done to investigate how to enhance ARs with additional supporting information.
Culture and Driver Behavior: An International Study
Contributors: Hao-Jie Su, University of Michigan; Yili Liu, University of Michigan
This research investigates the relationship between driver’s cultures and their comprehension of road signs and behavior. Ninety individuals from three English-speaking countries (30 each from India, the U.S., and the U.K.) participated in this research to perform a road sign comprehension test, give rankings for road sign design features, and complete a Driver Behavior Questionnaire (DBQ). The results show some significant correlations between Hofstede’s cultural dimension scores and participant responses. For example, better comprehension of road signs is associated with lower power distance (PDI) scores and higher individualism (IDV) and motivation towards achievement and success (MAS) scores. More aberrant driver behaviors are associated with higher scores of IDV, MAS, long-term orientation (LTO), and indulgence (IND) but with lower scores of PDI and uncertainty avoidance (UAI). These findings show the importance of considering cultural factors in traffic studies on road signs and driver behavior.
Feasibility and Design of a Virtual Reality Tool to Support Lung Cancer Patients' Treatment Preparedness: EveryBreathMatters
Contributors: Safa Elkefi, SUNY Binghamton University
Lung cancer patients face several challenges during treatment that impact their adherence to the prescribed regimens. VR can potentially enhance patient education, treatment preparedness, and comfort with the treatment environment by creating computer-generated virtual environments. This study uses patient and provider data collected through interviews to design a VR tool, EveryBreathMatters. Designed tool has a layered content including 4 modules providing patients with information on cancer treatment options, related side effects, mental health support and well-being, and links to possible sources to navigate.
Integrating Individual Differences Metrics into Cognitive Models
Contributors: Megan Morris, U.S. Air Force Research Laboratory; Garrett Swan, Aptima, Inc.; Bella Veksler, Tier1 Performance; Jessica Tuttle, University of Dayton Research Institute; Arielle Stephenson, BAE Systems Inc.
Individuals vary in susceptibility to fatigue, resulting in different performance given the same stressors. Current performance modeling capabilities for fatigue typically ignore these individual variations, decreasing predictive accuracy. The current effort examines the predictive utility of integrating subjective individual differences metrics into cognitive models of cognitive processes crucial for operational performance. Stable individual differences metrics - circadian typology, typical sleep duration and sleep need, and hardiness - were measured via self-report questionnaires, and cognitive performance for psychomotor vigilance was measured during a 24-hr simulated air mobility mission where 39 pilots experienced restricted sleep opportunities while performing various mission tasks. The psychomotor vigilance cognitive model was developed in the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture and paired with the ACT-R fatigue module. Individual differences metrics were integrated into the model via modulations of the fatigue module parameter :fpbmc through a Metropolis-Hasting procedure. Integrating individual differences into the cognitive model improved the predictive fit for each pilot, but the practical benefit of the individuation was mostly seen in a few participants. These results suggest that organizations may be able to increase the accuracy of fatigue models by incorporating stable individual differences information from surveys or other means, enhancing fatigue risk management.
Measuring Anticipatory Monitoring Skills Using a Crew Briefing Task
Contributor: Dorrit Billman, NASA Ames Research Center; Barth Baron Jr., San Jose State University; Paige Christine Corry, San Jose State University; Lucas Cusano, NASA Ames Research Center; Melissa Peterson, San Jose State University; Randy Mumaw, San Jose State University
Inadequate monitoring has contributed to aviation accidents and incidents (e.g., see CAST, 2014). One potentially useful approach for mitigating this factor is to identify the skills and knowledge needed for effective anticipatory monitoring and improve their training and assessment. As part of a larger project, we characterized a set of skills and knowledge, developed training and assessment measures, and conducted a study that measured monitoring performance before and after training. We provide an overview of the study and then focus on performance on a generative briefing task. This operationally relevant task required anticipation and monitoring skills to prepare an effective Top of Descent briefing. We describe development of the task, how we coded performance, and the task results. We found that pilots included more information helpful for managing the descent flight path after the tutorial. We also discuss challenges and benefits of developing operationally relevant tasks in a low-fidelity (laptop) instructional setting.
Mental Health Safety Challenges in Pre-Hospital Emergency Medical Service Providers: A Scoping Review
Contributors: Christopher McGlynn, West Virginia University; Avishek Choudhury, West Virginia University
Pre-hospital emergency medical service (EMS) providers face persistent mental health challenges due to repeated exposure to trauma, irregular work hours, and high-pressure environments. These factors contribute to elevated rates of PTSD, burnout, fatigue, depression, anxiety, and suicidality—conditions that impair individual well-being and compromise workplace safety and patient care. This scoping review synthesized findings from 57 peer-reviewed studies published between 2014 and 2024 to identify key mental health risks and intervention gaps among EMS professionals. Guided by PRISMA and evaluated using the Mixed Methods Appraisal Tool, studies were grouped into six thematic areas: PTSD, burnout, occupational stress, depression/anxiety, suicidality, and other related challenges. PTSD was the most frequently studied, with contributing factors including sleep disorders, emotional dysregulation, and lack of social support. Burnout and stress were strongly linked to workplace violence, unhealthy coping mechanisms, and organizational pressures. Sleep deficits emerged as a consistent risk factor across all domains. Protective factors included peer support, resilience, and mental health literacy. Despite the existence of support programs like Critical Incident Stress Management, gaps remain in implementation and effectiveness. Future research must evaluate longitudinal trends, address barriers to care, and explore innovative tools such as AI-driven mental health monitoring to improve EMS workforce safety.