This commentary highlights three problems that can emerge by integrating Digital Twin Technology (DTT) and Human-Machine Systems (HMS), drawing insights from Human-Technology Interaction, Systems Engineering and Computer Science, and Learning Sciences experts, who participated in the IEEE SMC Society/SMST Workshop on HMS-DTT, hosted at the University of Central Florida. The paper focuses on ethics, human and data interoperability, and trust issues. Rather than providing a traditional literature review, it consolidates contributions from workshop discussions and highlights the need for transparent, reliable systems, standardized data protocols, and ethical frameworks to guide development and implementation. Synthesizing diverse perspectives underscores the importance of interdisciplinary approaches in realizing the benefits of HMS and DTT integration while mitigating potential risks. Overall, this work aims to inform future research agendas and foster responsible innovation by integrating viewpoints across disciplines in this rapidly evolving field.

Face masks and coverings are often encountered by facial examiners in high stakes forensic case work. Forensic facial examiners (hereafter “facial examiners”) are trained to make face identifications and their decisions can be used as evidence in court. While research suggests that facial examiners can identify unconcealed faces with high accuracy, their identification performance for masked faces is unknown. Here we provide the first test of facial examiner performance for masked faces. An international sample of 61 facial examiners and 39 professional teams completed face identifications for 20 image pairs, which consisted of one unconcealed face image and one mask wearing face image. Examiners used their normal working procedures to make their identification decisions. The performance of facial examiners was compared against that of controls and six face identification algorithms. Both facial examiners and professional teams outperformed controls, however, professional teams made the least errors of all groups. The face identification algorithms achieved very high accuracy on the task. Our results support the use of facial examiners for the identification of masked faces and suggest a role for teams and human-machine working in applied practice. The findings back the notion that facial examiners use feature comparison, and this strategy is successful for matching pairs of images where one face wears a mask.

Contemporary society faces a growing set of complex global issues representing significant human health, well-being, and sustainability threats. Human Factors and Ergonomics (HFE) has a critical role to play in responding to these issues; however, there remain a set of grand challenges that require resolution. This paper presents and discusses six grand challenges for HFE and related key research thrust areas for each of the challenges. The grand challenges are (1) Evolution in Societal Thinking; (2) Future of Human Work in Industry 5.0; (3) Climate Change and Sustainability; (4) Future of Education and Training; (5) Future of Personalized Health, and (6) Life, Technology, and the Metaverse. These grand challenges and key research thrust areas were derived by twenty HFE professionals who are the authors of this paper. The implications of these grand challenges for education, training, research, and implementation of HFE principles and methods for the benefit of humankind are discussed.

This study describes the impact of ChatGPT use on the nature of work from the perspective of academics and educators. We elucidate six phenomena: (1) the cognitive workload associated with conducting Turing tests to determine if ChatGPT has been involved in work productions; (2) the ethical void and alienation that result from recondite ChatGPT use; (3) insights into the motives of individuals who fail to disclose their ChatGPT use, while, at the same time, the recipient does not reveal their awareness of that use; (4) the sense of ennui as the meanings of texts dissipate and no longer reveal the sender’s state of understanding; (5) a redefinition of utility, wherein certain texts show redundancy with patterns already embedded in the base model, while physical measurements and personal observations are considered as unique and novel; (6) a power dynamic between sender and recipient, inadvertently leaving non-participants as disadvantaged third parties. This paper makes clear that the introduction of AI tools into society has far-reaching effects, initially most prominent in text-related fields, such as academia. Whether these implementations represent beneficial innovations for human prosperity, or a rather different line of social evolution, represents the pith of our present discussion.

The present study investigates how experienced ageism mediates the relationship between perceived ageism from GenAI and age anxiety outcomes with a secondary data analysis from the Older Adult Annotator Demographic and Attitudinal Survey (N = 1,483). Measures consist of older adult (age range: 50–90) responses to the previously validated Aging Anxiety Scale (AAS) and the unvalidated Age Experience Survey (AES). An exploratory factor analysis followed by a confirmatory factor analysis establishes latent variables from both surveys. A structural mediation model was used to assess whether Experienced Ageism (AES) mediated the relationship between attitudes towards algorithmic ageism (AES) and age anxiety outcomes (AAS). Experienced ageism mediated the relationship between attitudes toward algorithmic ageism and implicit age anxieties ( p s < .05) but not explicit age anxieties. Future work should explore how perceived ageism in GenAI influences age anxiety and adoption of GenAI technology among older adults.

Partially automated vehicles (PAVs) relieve human drivers from performing certain basic vehicle control tasks. The human driver remains responsible for automated system supervision despite such support systems. Therefore, long drives with partial automation can induce underload conditions, thereby increasing passive fatigue, impairing situational awareness (SA), and reducing response capacity. As a result, engaging in cognitively demanding tasks has been suggested as an underload countermeasure. The present study examined the acute and chronic effects of adding a Trivia-like Supplementary task as a cognitive non-driving-related task (NDRT) on mitigating driver's underload and induced passive fatigue. Further, it assessed the impact of the Supplementary task engagement on drivers' trust, attention and hazard perception (HP) during partially automated driving (PAD). Twenty-four participants were randomly assigned to one of two conditions: (1) PAD with the Supplementary task and notifications of upcoming hazards or (2) PAD with notifications of upcoming hazards only. Participants experienced two forty-minute monotonous driving sessions, one week apart. Each driving session included four latent but unmaterialized hazardous scenarios. The mental workload was evaluated via objective and subjective methods, passive fatigue was measured using KSS and HP, and attention was assessed via gaze behavior analysis. Contrary to our initial literature-based assumption that driving under PAD without engaging with a Supplementary task would lead to underload, we found that under simulated driving conditions, this monitoring driving task leads to overload rather than underload. Thus, in contrast to our expectations, including a Supplementary task did not counteract mental underload, but rather it relieved the drivers from the primary monitoring task, leading to a reduction in cognitive workload, especially in chronic circumstances. Additionally, both experimental groups maintained high and similar HP performance. The findings suggest that including a Supplementary task and other human-machine interface (HMI), functionalities can modify drivers' behavior and attention allocation strategies over time in various ways, stressing the crucial importance of a mindful systems design to ensure driver attentiveness across continued usage.

This article describes the main contributions made by the late Paul J. Feltovich to the fields of cognitive engineering and decision making.

Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.

Adaptive Instructional Systems (AISs) support learners by adapting a variety of features depending on learner performance and characteristics. A common adaptive intervention is to change the difficulty of the training to maintain an appropriate difficulty level for learners. However, changes in task load have been shown to increase stress in learners. Given the ubiquity of adaptive difficulty and the established detrimental effects of stress on performance, the interplay of adaptive difficulty and stress is under-investigated in AIS literature. The transactional model of stress emphasizes the role of stress appraisal and coping strategies in the stress-performance relationship. This study extends previous research by examining how stress appraisal and emotion-focused coping (EFC) influence task performance in a demanding electronic warfare (EW) task. We explore the role of behavioral markers as indicators of stress responses and discuss their value as an adaptive variable. Results suggest that stress appraisal affects performance indirectly by affecting coping, slowing task behaviors, and subsequently degrading task performance. Our findings suggest that incorporating behavioral markers of stress responses into adaptive instructional systems (AISs) provides a non-intrusive method for real-time stress assessment, allowing for dynamic adjustments to adaptive instruction. These findings offer practical insights for AIS design, highlighting the importance of monitoring stress-related behaviors to optimize learning and performance outcomes. The present work discusses the importance of identifying theoretically relevant models and variables for AIS design and demonstrates the identification of a generic variable associated with stress and performance.

Adaptive training (AT) systems improve learning efficiency through techniques such as adaptive difficulty, although these changes in difficulty can induce stress in learners. Coping strategies can be useful to help mitigate stress, but they introduce a complex landscape to study in the given context. The effects of adaptive and maladaptive coping strategies have been studied extensively in terms of academic performance, employee health, etc. However, the two types have never been studied together in an AT protocol to determine their effects on performance and psychophysiological functioning in the military domain. In this study, we manipulated adaptive versus maladaptive coping strategies in an AT protocol for a naval radio frequency detection task. Between these coping strategies, we observed improvements in physiological functioning (heart rate), but not performance (accuracy and timeliness).

A critical metaphor for the development, implementation and penetration of autonomous machine systems into the world of human work is presented. Most especially, the 'Isles of Autonomy' concept is articulated which argues that the expropriation of human pre-eminence will be marked by a series of threshold events, some of which are, even now becoming evident. In particular, it indicates that there will be a watershed event in which differing and distinct expressions of applied autonomous systems will spontaneously coalesce to produce an emergent, general artificial intelligence. The latter may well be unrelated to the original goals, aims and constraints of the disparate entities that have joined together. This threshold will be a harbinger of cascading unifications in which an unrestrained aggregate will assume de facto control over disparate work domains. The nature of such a development, most especially in light of associated human roles, is here evaluated. While emergent systems possess no necessary privilege, neither are their non-linear properties and behaviours directly inferable from their componential elements. The demi-sesquicentennial (75th) marking of the future of a science that is focused most especially on the predominance of human, work, is considered in light of these impending forces of change.

An overview of my career in face memory research. How do people recognise faces? I propose the islands of expertise model, which suggests that growing expertise bootstraps our abilities.

The presence of a weapon in the perpetration of a crime can impede an observer's ability to describe and/or recognise the person responsible. In the current experiment, we explore whether weapons when present at encoding of a target identity interfere with the construction of a facial composite. Participants encoded an unfamiliar target face seen either on its own or paired with a knife. Encoding duration (10 or 30 s) was also manipulated. The following day, participants recalled the face and constructed a composite of it using a holistic system (EvoFIT). Correct naming of the participants' composites was found to reduce reliably when target faces were paired with the weapon at 10 s but not at 30 s. These data suggest that the presence of a weapon reduces the effectiveness of facial composites following a short encoding duration. Implications for theory and police practice are discussed.

Driver vigilance research focuses on the safety impacts of maintaining sustained attention in underload conditions when automatic responses are adequate to the task and responses are sparse. However, urban environments have frequent risks that appear chaotically and require high levels of engagement—a very different vigilance task. Although drivers perceive a wide range of task demands as manageable, their movement patterns hint at more active engagement as they approach overload. This condition we are calling “Attentiveness,” to distinguish it from the classic exploration of vigilance in underload conditions. To understand how the built environment impacts driving automaticity and vigilance, data from the SHRP2 Naturalistic Driving Study (NDS) was used to evaluate driving patterns within urban multimodal street sections. Data for acceleration, jerk, lane position, and speed were tabulated for ten, 5-second epochs at 200 multimodal locations and compared to the behavior for the entire length of the drive. The built environment variables that demonstrated a strong correlation and impact size for a change in driver behavior included doorway density, block length, Walkscore, corridor aspect ratio (height over width), and terminated vistas. The commonality for these variables is that each one impacts the rate that novel stimuli arrive as drivers progress sequentially along their path. Acceleration, jerk, and lane position appear to be related to the rhythm of interruptions that occur along the length of the roadway.

This work is in response to the observations of Professor Smith on an earlier article of mine entitled "On the Design of Time," in the present Journal.

Accurately determining whether two images show the same person is a surprisingly difficult task, which becomes even harder if one or both faces are wearing medical face masks. Previous attempts to improve face matching accuracy have generally had limited success. One brief training program has been shown to improve masked face matching accuracy by 4.9%; however, this increase does not overcome the entire performance deficit caused by masks. Here we investigate whether combining independent identification decisions from different individuals can improve masked face matching performance through the Wisdom of the Crowds Effect (WoCE). Accuracy gains emerged reliably after combining decisions from 3 individuals, culminating in significant improvements of 11-26% among the largest crowds. Despite creating crowds of up to 80 people, half of the eventual improvement had already occurred in crowds of 6 individuals. The WoCE can entirely ameliorate the impairment face masks cause to unfamiliar face matching accuracy.

Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification.

FEATURE AT A GLANCE: Innovations in design necessarily create issues concerning negative performance transfer. A specific example of such negative transfer, associated with evolving automated vehicle ignition systems, is presented. This case study illustrates that transfer can occur at numerous levels and in differing behavioral guises. Negative transfer increases the potential for cross-technology confusion and error. Conversely, positive transfer can maximize design acceptability and utility. The potential for a dissonance between true innovation and the desire to optimize positive transfer is examined.

For the last century, the roadway design paradigm has been grounded in the physics of point masses, adjusted for human limitations. However, this is inadequate to assure pedestrian safety in complete streets where vehicle kinematics no longer control. Instead, social and psychological factors manage behavior. Unfortunately, the appropriate design critical psychological principles have yet to be elucidated. We posit that interpersonal perception governs drivers’ behavior, attentiveness and speed in streets. This is bounded by previously delineated human neurological and perceptual propensities. Using these perceptual limitations as postulates in a geometric style proof, we derive the person perception panorama (PPP): a window of interactivity around the moving driver that is continuously monitored for human presence, roughly 60 to 90 feet wide. In this area interpersonal interaction is implicit, functional, and has an impact on driver behavior. Validating evidence, additional governing principles, and an initial speed prediction formula are also included.