ObjectiveThis study identifies cybersecurity vulnerabilities and risks in robotic-assisted surgery (RAS) and proposes a cybersecurity framework and an assessment tool for RAS systems.BackgroundRAS systems are increasingly integrated into networks which raise cybersecurity concerns. These systems can enhance surgical outcomes but are potential cyberattack targets, which can affect clinician care, patient safety, and organizational operations.MethodSurveys and interviews were conducted with stakeholders (clinicians, researchers, cybersecurity professionals, and hospital administrators) to collect perspectives on RAS cybersecurity. Thematic analysis was used to develop an RAS cybersecurity framework. Then, stakeholders contributed to creating an RAS cybersecurity assessment tool using Failure Modes, Effects and Criticality Analysis (FMECA).ResultsSurvey responses ( = 84) revealed that 48.8% of respondents were familiar with RAS cybersecurity. Only 24.6% of clinical respondents were aware of their organization's cybersecurity policy. Interviews ( = 15) identified vulnerabilities such as inadequate training, limited communication between manufacturers and healthcare systems, and gaps in regulations. Failure modes focused on consequences of cyberattacks on RAS systems, with severity assessments related to patient health and technology reliability/integrity completed and outcome actions identified.ConclusionUnderstanding RAS cybersecurity challenges is still in its infancy. Key vulnerabilities include insufficient training, limited data sharing, and external threats. The framework illustrates the interconnectedness of stakeholders, while the FMECA assessment tool addresses current vulnerabilities in RAS systems.ApplicationRAS cybersecurity vulnerability and risks should be carefully considered when integrating systems into healthcare organizations, and the RAS cybersecurity assessment tool can be used by stakeholders to systematically identify and analyze potential cybersecurity failure modes.

ObjectiveWe examine how risk and automation failures in conditional driving automation (SAE Level 3) influence drivers' calibration of trust and reliance behavior in the form of system use and monitoring.BackgroundConditionally automated driving brings a challenging new role for drivers, who are permitted to engage in non-driving-related activities but must take back control in certain situations.MethodsParticipants completed three drives in a driving simulation with conditional driving automation. The first drive was with low risk and the second drive was with high risk implemented in the simulation. The third drive included either early or late automation failure.ResultsParticipants reported lower trust, took over manual control more often, and monitored more when driving under high risk than when driving under low risk. After experiencing an automation failure, trust decreased immediately but fully recovered over time. Driver's monitoring increased and decreased immediately as the failure started and ended. The timing of automation failure did not influence its impact on trust.ConclusionThe results indicate that drivers respond appropriately to risk. Trust develops dynamically in case of an automation failure, but failure timing does not influence this process. From an applied perspective, drivers would benefit from assistance in re-calibrating trust after automation failure.ApplicationBased on our findings, we argue that incorporating drivers' mental model formation process into the feedback loop of trust and reliance behavior calibration could enhance the theoretical understanding of trust calibration.

ObjectiveWe examine AI trust miscalibration-the discrepancy between an individual's trust in AI and its actual performance-among university students. We assess how the length of explanations and students' expertise shape the likelihood of alignment with AI recommendations.BackgroundThe relationship between explainability and users' trust in AI systems has been scarcely addressed in the current literature, even though AI-assisted processes increasingly affect all professions and hierarchical levels. Given that human-AI relationships are often formed during education, it is crucial to understand how individual and contextual factors influence students' assessment of AI outputs.MethodWe conducted in-class experiments with 248 students from multiple universities. Participants solved GMAT questions, then viewed an AI recommendation-sometimes correct, sometimes incorrect-with varying explanation depth and eventually could revise their initial answer; student's final answer being in line with AI recommendation operationalized our measure of "trust." We estimated logistic models with control variables, including mixed-effects specifications to account for repeated observations.ResultsExplanation complexity is associated with higher trust on average, but its relevance depends on who reads it and whether AI is correct. Students who previously answered correctly exhibited lower willingness to defer, especially when AI was incorrect; conversely, agreement and consistency effects significantly amplified trust. These behavioral patterns highlight conditions under which AI-generated explanations can foster critical engagement or conversely encourage uncritical acceptance.ConclusionOur results point to a "AI knows better" heuristic at work-especially among nonexperts-where polished presentation is easily read as reliability, encouraging uncritical agreement with incorrect recommendations; in parallel, experts benefit more from deeper rationales when AI is accurate, yet still display under-reliance of correct assistance in many cases. Overall, trust calibration is driven less by any single cue than by the alignment of student performance, AI reliability, and explanation design, with prior agreement acting as a powerful amplifier of subsequent alignment.ApplicationOur findings imply that instructional approaches should promote independent reasoning before exposure to AI, deploy concise but diagnostically informative explanations, and include brief verification steps before accepting AI recommendations, especially for nonexperts who are more prone to harmful switches. Simple monitoring tools that track helpful versus harmful changes could support a more discerning and productive use of AI tools.

ObjectiveInvestigators assessed whether a simplified runway status light (specifically runway entrance lights) may reduce the risk of runway incursions in nontowered airport environments.BackgroundComplementing the development of a low-cost aircraft surveillance system, the authors proposed the use of runway status lights at nontowered airports, an environment in which such systems have previously not been tested.MethodThirty-seven general aviation pilots were recruited to participate in three simulated scenarios with and without runway status lights. Participants were tasked with deciding when to take off from a runway. Participants' performance was assessed to determine whether simplified runway status lights impacted the risk of runway incursions, or how quickly a takeoff decision was made. The effect of the provision of system information on participants' performance was also studied. A NASA TLX questionnaire was administered to measure the perceived workload effects of runway status lights use while a survey captured participants' views on runway status light use.ResultsSimplified runway status lights reduced runway incursion risk in simulated scenarios when conflicting aircraft were relatively difficult to see. The provision of training or system information appeared desirable but not necessary, based on participants' feedback and performance. A reduction in perceived workload (physical domain) was reported in scenarios with runway status lights. Overall, participants had positive views on the implementation of runway status lights.ConclusionSimplified runway status lights may be effective at mitigating runway incursion risk.ApplicationThese findings support the continued study of runway status lights at more airports, including nontowered airports.

ObjectiveTo investigate the relationship between variations in physiological signals and mental workload (MWL) during the execution of a helicopter roll-attitude compensatory tracking task.BackgroundCurrent perceptual models in human-machine piloting have been focused on visual and vestibular cues, overlooking somatosensory and auditory inputs and their interactions. This creates a knowledge gap in understanding shared perception strategies for piloting in environments with impaired sensory channels or enhanced secondary cues.MethodsFifteen healthy participants performed an attitude-tracking task under eleven cueing modalities combining visual, degraded visual, haptic, and auditory cues. Physiological signals-cardiac activity, respiration, brain activity, skin temperature, and electrodermal activity-were analyzed in relation to self-reported MWL using statistical tests and GLMM (Generalized Linear Mixed Models).ResultsParticipants reported low perceived MWL under good visual conditions, with supplementary auditory and haptic cues helping to reduce MWL with degraded or absent visual input. Physiological signals discriminated between MWL levels and multivariate analysis showed that while combined signals revealed an evident explanation of their variance, individual differences underscored the importance of personalized modeling.ConclusionMWL assessment through physiological signals validated during a helicopter tracking task demonstrated that multimodal cueing in complex scenarios can reduce cognitive load, leading to potential safety risk mitigation.ApplicationThis research has the objective of providing a novel approach for safety enhancement and mitigating risks in rotorcraft operations by integrating visual, auditory, and somatosensory cues with physiological-based MWL assessment.

ObjectiveThe present work investigates how task modality (visual, auditory), task load level (low or high), and task load type (target-distractor similarity, display rate) influence dual-task interference in virtual reality (VR).BackgroundDual-task interference is influenced by various factors including perceptual modality, where tasks that share the same modality may yield larger performance decrements than tasks that do not. Task load (i.e., level and type) can also reduce performance in one or both tasks. However, the interaction between these factors in immersive environments like VR is still unclear.MethodParticipants performed a: (a) visual tracking task, (b) visual/auditory detection task, or (c) both concurrently in two different experiments. In Experiment 1, visual tracking load was manipulated via increasing target-distractor similarity, while detection load was manipulated via increasing display rate. In Experiment 2, detection load was manipulated by increasing target-distractor similarity.ResultsIn Experiment 1, higher detection task loads induced greater dual-task costs (DTC) in the detection task regardless of task modality, whereas tracking task DTC was not influenced by higher task loads. In Experiment 2, higher detection task loads induced greater DTC in the detection task only when it was presented visually.ConclusionThe findings suggest that tasks presented in the same modality may experience greater dual-task interference in one or both tasks depending on the task load level and type.ApplicationThese findings can inform the design of multimodal interfaces in complex multitasking environments like military operations or emergency response where minimizing dual-task interference at varying workloads is crucial.

ObjectiveTo quantitatively analyze the impact of network latency on video conferencing communication efficiency, guide network latency design standards, and evaluate mitigation strategy.BackgroundNetwork latency substantially affects the quality of video conferencing. Existing research has focused on subjective assessments. Networking designers require quantitative latency guidelines.MethodThis research involved two studies: one quantifying the effect of network latency on task performance and the other testing a mitigation strategy. Each study involved two experiments: one ( = 60) using a building block "exploratory task" and the second ( = 30) using a text communication "validation task." We followed similar test procedures across the experiments, using a different pool of participants for each of the four. For the second study, we also modified the apparatus by adding a foot pedal and light.ResultsThe completion time for the building block experiment increased with higher latency. Linear regression analysis showed that one-way network latency added approximately 2.6 s per second of delay in each communication round. We observed similar patterns in the text-based experiment. The mitigation strategy involving low-latency communication feedback on the speaker's intent substantially reduced mean task completion time under high-latency conditions.ConclusionThis research provides equations that describe the relationship between one-way network latency and communication efficiency. Implementing low-latency communication feedback on the speaker's intent can mitigate the impact of latency.ApplicationNetwork designers can refer to the conclusions of this research to specify network latency requirements. Video conferencing manufacturers can adopt the mitigation strategy to reduce the negative impacts of high network latency.

ObjectiveThis study aims to introduce a novel augmented display technology that enhances visibility of forward vehicles by projecting critical highlighting information onto the windshield, and to validate its effectiveness in improving occupants' reaction, acceptance, and workload.BackgroundThe rapid advancements in autonomous driving technology have brought significant changes to the automotive landscape; however, trust and safety concerns remain major barriers to widespread acceptance. To address these issues, enhancing occupants' reaction efficiency with workload and acceptance in autonomous vehicle operations is critical.MethodUtilizing two distinct highlighting display methods-surface and outline-within a virtual reality simulation, the research examines their effects on occupants' acceptance including perception of safety through AVAM (Autonomous vehicle acceptance model), and workload through NASA-TLX to dynamic road scenarios during autonomous driving.ResultsThe findings reveal that highlighting display significantly enhances acceptance and workload with reaction time, but their effectiveness varies. Surface highlighting was found to better reduce anxiety and increase perceived safety, while outline highlighting more effectively reduced mental demand.ConclusionThese results offer valuable insights into the dynamic interaction between advanced display technologies and autonomous vehicle operations, highlighting the potential benefits and challenges in their implementation to foster broader acceptance of autonomous vehicles.ApplicationBy intuitively projecting critical information during takeover scenarios, this technology addresses trust and safety barriers in autonomous driving, potentially enhancing prompt responses, accelerating autonomous vehicle integration, and improving the overall driving experience.

ObjectiveThis study examines the disuse of technology among older adults and develops a taxonomy to categorize various forms of disuse.BackgroundUnderstanding the prevalence and factors contributing to disuse is challenging due to the varying terminology and lack of a standard classification. This lack of clarity makes it difficult to understand the reasons for the disuse of technology, especially when studying the use of emerging assistive technology among older adults. This is problematic, as these emerging technologies offer numerous benefits, but many adults struggle to incorporate them into their daily lives, resulting in disuse.MethodWe analyzed open-ended survey responses from 78 older adults who had purchased but subsequently disused a smart speaker. We employed a reflexive thematic analysis to identify themes related to the disuse of technology.ResultsTwo overarching themes were identified. The first, "Interests and Purchase Influences," captured the initial reasons for adoption, ranging from general curiosity to meeting specific needs. The second, "Misalignment with Needs and Expectations," encompassed four subthemes: disspointment, lack of relevance, perceived risks, and impact on independence, which collectively explained why participants ultimately stopped using the device.ConclusionThese findings demonstrate how varied experiences with the same technology result in distinct disuse trajectories, highlighting the gap between adoption and disuse research. Clarifying these patterns strengthens the disuse taxonomy and lays the groundwork for future studies to quantify their impact.

ObjectiveThis work presents a comprehensive analysis of fundamental performance of crossing-based moving target selection.BackgroundAlthough the crossing interaction with static targets has been theoretically studied, there has yet to be a generalizable, controlled empirical study investigating the fundamental performance of crossing-based selection for moving targets.MethodWe conducted an experiment with stylus input to investigate how users acquire moving targets with crossing compared to pointing as a baseline.ResultsThe most noteworthy finding of our study is that crossing had overall greater advantages over pointing for moving target selection (a 12.37% reduction in task completion time and a 5.88% increase in accuracy rate for , and a comparable task time and a 4.71% increase in accuracy rate for ). However, the advantages of crossing would be insignificant when targets become larger than approximately 14.69 mm or move slower than 14.69 mm/s.ConclusionCrossing performance varied between . in (Hoffmann, 1991) can be used to model time performance of crossing-based moving target selection.ApplicationSuch results provide a theoretical foundation for crossing-based interface design with moving objects.

ObjectiveThe objective of the current study is to investigate how passive exoskeletons affect low-back passive tissues creep during prolonged stooping.BackgroundUsing exoskeletons could be a new strategy to prevent stress-relaxation deformation (creep) in low-back passive tissues induced by prolonged or repetitive stooping, but previous studies only focused on low-back active tissues.MethodTwelve healthy males completed 12 min of stooping (with and without a passive exoskeleton), while body kinematics and muscle activities were captured before and after stooping.ResultsResults indicate intact characteristics (i.e., no changes) in both active and passive tissues after enduring a 12-min stooping protocol while using the exoskeleton. However, without the exoskeleton, clear stress-relaxation deformation in low-back tissues, and changes in the load transfer mechanism between active and passive tissues after prolonged stooping, are observed, revealing a 3.19° delayed flexion-relaxation angle, a 5% maximum voluntary contraction increase in lumbar muscle activity, and a 2.8° increase in the maximum lumbar flexion angle.ConclusionThe supporting force provided by passive exoskeletons effectively limits stress-relaxation deformation in low-back passive tissues, such as ligaments, by preventing excessive elongation during prolonged stooping in a fully flexed posture, thereby reducing the possible risk of spinal instability and low back pain development.ApplicationThe study reveals the greater value of passive exoskeletons, which protect passive tissues in the low back. The research findings can serve as a valuable reference for practitioners in implementing effective countermeasures in the perspective of assistance devices to enhance occupational safety.

ObjectiveThe objective of this study is to empirically test a computational model of interruptions processes and effects, and to compare an alternative model to determine which best explains interruption and response decision making.BackgroundInterruptions in safety-critical environments (e.g., hospitals) can lead to an increased risk of error for the person being interrupted (the interruptee) but may be necessary for the person doing the interrupting (the interrupter) to maintain safety. Little research has considered the perspective of both the interrupter and interruptee.MethodWe tested a computational model of interruption and response decision processes through an experiment where participants ( = 312) worked as a nurse in a simulated clinical team. We examined how task progress, time remaining, and time pressure influenced decisions and compared the model with an alternative that allowed the effects of time pressure to be non-monotonic.ResultsUsing Bayesian hierarchical modeling, we found that a non-monotonic model best explained interruption decisions. Participants were biased toward interrupting, with time pressure only influencing decisions when it was very high. Contrastingly, the monotonic model best explained response decisions. Participants were more likely to accept interruptions as the interrupter's time pressure increased in comparison to their own.ConclusionTime pressure has a non-monotonic influence on interruption decisions, but a monotonic influence on response decisions.ApplicationFindings can inform interventions to consider the interruptions process from the perspective of both the interrupter and interruptee. Interventions could focus on training workers to more accurately assess time pressure when making interruption decisions.

ObjectiveThis research investigated effects for new traffic markings on the user behaviour of motorcycle riders.BackgroundAcross motorised vehicles, motorcycles represent the most vulnerable road users.MethodA road sign and traffic markings were installed at six trial sites. Data from video cameras at each site provided measures of rider behaviour in relation to speed, road position, brake use, and use of the traffic markings, before and after installations. Throughout this research 4652 motorcycle riders travelled through the sites. Of these 1542 riders were analysed in more detail to investigate the effects of the road safety intervention on rider behaviour.ResultsAt five sites speed was reduced by a significant margin. At four sites there were significant improvements in road position at the final traffic marking. At five of the trial sites on the apex of a bend, there were significant improvements in road position. Braking behaviour decreased at two of the trial sites. For use of the traffic markings a significant increase was observed across all the trial sites. Across the behaviour measures, the changes were still present 4 weeks later. At a comparison site no changes in behaviour were observed.ConclusionThe findings provide evidence of improved rider behaviour which are placed in reference to the Safe System principles for road safety and casualty reduction.ApplicationThis research has generated international interest for installing the road sign and traffic markings in other regions and contributes to the Scottish Government's Road Safety Framework to 2030 by reducing motorcycle casualties.

ObjectiveThis study investigates the factors influencing drivers' decisions to intervene in conditional driving automation (SAE Level 3) without system alerts or failures.BackgroundIn complex traffic environments, mismatches between drivers' perception of traffic situations and the response of automation can lead to driver-initiated disengagements, even when the system can safely manage events. While such interventions may be safety conservative, they can also disrupt system operations, compromise safety, and reduce user trust.MethodA driving simulation with 23 participants was conducted in which a conditionally automated vehicle encountered a stopped vehicle blocking its lane, with oncoming traffic present in the adjacent lane. The system was programmed to safely overtake using the opposing lane considering the distance to the oncoming traffic. Participants could either remain in automated mode or override the system.ResultsDrivers intervened in more than 20% of events, most often by pressing the brake pedal while approaching the stopped vehicle when the gap to the oncoming traffic was perceived as insufficient. In challenging overtaking gaps, discrepancies between the behavior of a leading human-driven vehicle and the system further increased intervention likelihood, with some drivers misunderstanding the system's ability to detect oncoming vehicles. Although drivers who intervened completed overtaking faster than the system, their maneuvers were marked by abrupt steering and acceleration, raising concerns about encroaching into opposing traffic.ConclusionEnhancing system feedback and better aligning automation behavior with driver expectations may reduce unnecessary disengagements.ApplicationThe findings provide guidance for designing more intuitive automated driving systems that enhance user trust and safety.

ObjectiveThe current study aimed to explore the impacts of experiencing superior behaviors-accumulating large amounts of evidence and high automation use rates-on subsequent evidence accumulation rates and adaptable (discretionary) automation use decisions in a dynamic decision-making task.BackgroundOperators prefer to choose when to engage automated support systems but seldom use them appropriately. They also do not typically collect enough evidence to optimize their decision making. This creates suboptimal performance that could benefit from training better behaviors.MethodParticipants collected evidence about movement patterns of ships while assisted by a machine learning aid. They were initially required to collect high levels of evidence and use the aid as a form of hands-on training. Then, they chose how much evidence to collect and when to engage the aid.ResultsWhen given the choice, operators collected less evidence and used the automation less often than had been required during training, but improved their performance compared to unaided trials.ConclusionProviding operators with early experience of superior behavioral strategies can improve their subsequent decisions. This is a promising direction for achieving human-automation team synergy.ApplicationsShort exposures to optimal behaviors may be a feasible training approach to improve human-automation interactions in contexts where operators want decisional freedom in their interactions.

ObjectiveThis work examined performance costs for a spatial integration task when two sources of information were presented at increasing eccentricities with an augmented-reality (AR) head-mounted display (HMD).BackgroundSeveral studies have noted that different types of tasks have varying costs associated with the spatial proximity of information that requires mental integration. Additionally, prior work has found a relatively negligible role of head movements associated with performance costs. However, currently no studies have examined the magnitude of costs for spatial integration tasks when information is separated laterally using an AR-HMD.MethodsParticipants completed a spatial integration task in which information to be integrated was separated by multiple lateral visual angles. Participants were required to judge whether XY coordinate numbers were located within a designated red zone presented on a map.ResultsA significant effect of separation distance was found on response time, with no impact on accuracy. The effect of separation on response time increased considerably in the AR-HMD format compared to prior work examining the performance costs on a wide-angle monitor. Head movements became more costly to response time once information began to enter the head field at around 32 degrees of separation.ConclusionsThe current results taken with previous work indicate a task-device interaction, in which head movements become more costly dependent upon the type of information to be integrated.ApplicationOur findings imply the need for careful evaluation of task characteristics when modeling information separation costs on a desktop display for an AR-HMD format.

ObjectiveThis study investigates students' acceptance of e-learning during the COVID-19 pandemic, examining differences between voluntary and involuntary use contexts.BackgroundDuring the COVID-19 pandemic, universities shifted to online instruction for an extended period. E-learning became mandatory to use and was met with varying degrees of acceptance by students, whose educational expectations and experiences were altered. By 2022, institutions began transitioning to optional e-learning use, creating a natural setting to examine technology acceptance under both voluntary and involuntary conditions.MethodThis study employed a two-phase approach, first validating an extended Technology Acceptance Model (TAM) incorporating seven factors derived from focus groups. Second, conducting multigroup analysis of acceptance between voluntary and involuntary users. Data was collected through surveys from 908 undergraduate students.ResultsPLS-SEM analysis revealed strong explanatory power ( = .463-.731) for the extended TAM framework. Compatibility demonstrated the strongest effect on perceived usefulness, while information quality and system quality influenced both perceived usefulness and ease of use. Multigroup analysis revealed significant contextual differences in students' acceptance. Perceived ease of use more strongly influenced behavioral intention for voluntary users, while perceived usefulness had stronger effects for involuntary users.ConclusionThe extended TAM framework significantly predicted e-learning acceptance in both voluntary and involuntary contexts. Significant differences between usage scenarios were identified, extending TAM's applicability to crisis situations.ApplicationThis study provides insights for postpandemic educational technology implementation, emphasizing system quality and alignment with learning preferences. Practitioners should consider differences in adoption contexts when working to facilitate acceptance among both voluntary and mandatory users.

ObjectiveCurrent emergency response literature rarely focuses on the intersecting experiences of people with disabilities and first responders. This study employed a person-centered Human Factors approach to assess the experiences of people with disabilities and first responders during emergencies. This research identifies environmental and societal factors that hinder emergency response outcomes.MethodsWe conducted a needs assessment of 126 individuals (95 people with disabilities and 31 first responders). The survey included Likert-style items and open-ended responses. Open-ended items were analyzed using reflexive thematic analysis. An exploratory sentiment analysis was conducted to examine the alignment between qualitative and quantitative responses.ResultsThree major themes emerged: communication barriers, insufficient training, and limited resources. People with disabilities emphasized the importance of respectful, clear, and adaptive communication, while first responders noted challenges related to time constraints, tools, and accessible communication methods. Participants from both groups emphasized the urgent need for technology and training that can provide first responders with the necessary knowledge and skills to improve outcomes for people with disabilities.ConclusionThis needs assessment offers foundational insights into barriers that impact emergency response for people with disabilities and first responders. Actionable Human Factors solutions are proposed.ApplicationThe continued presence of barriers and tensions between the needs of people with disabilities and first responders suggests that Human Factors interventions should be developed to improve communication systems and equipment, training protocols, work systems, and environmental design for accessibility for people with disabilities, while also considering the safety and time-sensitive needs of first responders.

ObjectiveWe developed a taxonomy for human-agent teams (HATs) and conducted a literature review of existing HAT testbeds using our proposed taxonomy.BackgroundWith the increasing interest in HATs, numerous research studies in this field have utilized different testbeds. Despite this, there is a lack of comprehensive understanding regarding the capabilities and limitations of the existing testbeds.MethodWe first developed a taxonomy for HATs by modifying the existing framework for classifying human teams. Our proposed taxonomy comprises ten attributes. Subsequently, using the taxonomy, we analyzed 103 testbeds identified from 235 empirical research studies. After coding each testbed, we conducted frequency analyses on each attribute to determine the distribution of the testbeds.ResultsRegarding team composition, the majority of testbeds afford single human participants paired with few agents, typically in subordinate roles. Also, in most testbeds, the leadership structure is designated, with humans assuming leadership roles, or none. The communication dynamics present an area for further exploration, especially with larger team sizes. Additionally, nearly all reviewed testbeds focus on long-term teams, overlooking dynamics in ad hoc teams, which are common in real-world settings.ConclusionOur findings underscore the importance of further research into diverse team attributes, such as team composition, leadership structure, communication structure, direction, and medium. It would facilitate a deeper understanding of complex team dynamics in HATs and lead to designing effective teams.ApplicationThe current study would be valuable for discussing future research directions when developing new testbeds or designing novel experiments leveraging existing ones.

ObjectiveTo examine how domain-switching and user characteristics may predict broad susceptibility to digital deception.BackgroundDespite successful automated filtering techniques, humans remain vulnerable to fraud, losing billions of dollars annually. Many scams are delivered by digitally mediated methods, such as phishing emails or fake social media accounts. However, research typically explores susceptibility to these deceptions independently, making it difficult to draw broad conclusions regarding susceptibility to digital deception.MethodWe recruited a representative sample to investigate how susceptibility to deception may vary across digital domains, particularly when switching between domains (i.e., domain-switching). Participants classified stimuli from five different digital domains (i.e., emails, text messages, news headlines, social media accounts, and voicemails), either randomly (i.e., domain-switching) or in separate blocks, and completed measures of cognitive reflection and digital literacy.ResultsThe results suggest that when users struggle to discriminate between deceptive and legitimate stimuli in one digital deception domain, they are likely to struggle in others. Additionally, the results suggest that while cognitive reflection and digital literacy may help insulate users from deception, domain-switching may generally hinder user performance (i.e., slower responses).ConclusionOverall, individuals appear to be consistently vulnerable to deception across digital domains and this vulnerability can be exacerbated by certain task factors (e.g., domain-switching) and user characteristics (e.g., cognitive reflection and digital literacy).ApplicationTo develop more efficacious interventions that enhance user resiliency, research should consider broad training that incorporates correlates of susceptibility (e.g., cognitive reflection and digital literacy), and more realistic task settings (e.g., domain-switching).

ObjectiveTo evaluate the feasibility of electromyography (EMG)-based human-machine interfaces (HMIs) for high-demand activities such as driving based on performance, cognitive workload, usability, and safety measures.BackgroundUpper-limb amputees face challenges in performing everyday tasks, including driving. EMG-based HMIs offer potential solutions, particularly for wrist disarticulated and trans-radial amputee, but their effectiveness in complex tasks like driving requires further investigation.MethodNineteen able-bodied participants completed a driving simulation study using an EMG-based HMI, dominant hand, and both hands. Participants performed various driving maneuvers including straight lane driving, overtaking, and 90-degree turns at intersections. Driver performance, cognitive workload (measured by blink rate and subjective measures), usability (USE questionnaire), and safety were assessed.ResultsUsing the EMG-based HMI led to higher lane offset and steering angle compared to conventional methods, but demonstrated lower steering entropy in some situations. Cognitive workload was higher for EMG-based HMI, while usability scores were lower. Safety measures were mixed, with EMG-based HMI showing better performance at intersections but lower lane offset and steering angle safety scores overall.ConclusionThe study highlights both limitations and opportunities presented by EMG-based HMIs in high-demand tasks such as driving. While the system exhibited lower performance in some conditions, it demonstrated potential for controlled driving, particularly during specific maneuvers. The higher cognitive workload and lower usability scores indicate areas for improvement.ApplicationThe findings provide valuable insights for the development of more effective EMG-based HMIs, supporting future research and clinical trials aimed at enhancing mobility and independence for individuals with upper-limb amputations.

ObjectiveThis study examined whether a powered ankle exoskeleton affected street crossing decisions and perceived mental workload of novice users without mobility limitations at a simulated traffic intersection.BackgroundExoskeletons are wearable mobility devices that can impact physical and cognitive performance. Exoskeleton commercialization for the public necessitates evaluations into how these systems influence novices' cognitive reasoning and directed attention in urban environments.MethodsParticipants ( = 20) made street crossing decisions with and without the exoskeleton. Participants walked through a simulated city using a self-paced treadmill and decided whether to cross the street at prespecified distances from the intersection. Cognitive workload perception was measured using the NASA-TLX survey.ResultsNo significant effects of the exoskeleton on street crossing decisions were observed. Rather, data indicated significant reductions in decisions to cross as distance from the intersection increased and with vehicle presence at the intersection. Cognitive workload scores marginally worsened when wearing the exoskeleton.ConclusionStreet crossing decisions were unaffected, but exoskeletons can influence perceived mental workload. These results highlight the importance of designing wearable systems that align with both physical and cognitive task demands. Future studies should incorporate different exoskeletons, tasks, and user groups to determine how these factors influence task performance.ApplicationUnderstanding the interaction between exoskeletons and novice user cognitions can support the development of exoskeletons that provide sufficient physical support without impeding the mental processes needed for their safe and efficient operation. Researchers can also utilize similar procedures to evaluate alternate exoskeleton designs for urban mobility decision making.

ObjectiveThis study empirically investigates the embodiment of occupational exoskeletons (OEs) through repeated use.BackgroundOEs are wearable devices designed to assist operators' movements. Their embodiment- the phenomenon by which they come to be perceived as an integral part of oneself - remains underexplored, thus limiting our understanding of OE adoption. We operationalize embodiment through readiness-to-hand (using the device with minimal conscious attention) and sense of ownership (perceiving the device as part of oneself).MethodStudy 1 is a laboratory study using a within-subject design to examine the evolution of embodiment through two single-item scales over repeated training sessions with an upper-limb exoskeleton in a sample of 14 participants. Study 2 is a field study using a cross-sectional design to investigate differences in OE embodiment across 27 operators with varying experience of OE use. Embodiment was assessed using the same measures as in Study 1.ResultsStudy 1 showed that repeated use shifted attention from the device to the task. Additionally, repeated use led to a progressive integration of the exoskeleton within oneself. Study 2 provided similar results, showing that experienced users focused more on the task when using their OEs and exhibited a greater integration of OEs into the self than novice users.ConclusionRepeated OE use is linked to the cognitive disappearance of the exoskeleton and merging of self and device.ApplicationUnderstanding embodiment can guide the development of OEs. Integrating embodiment assessments can optimize implementation strategies and strengthen our understanding of users' adoption and rejection.

ObjectiveWe explored transfer of learning from brief practice with different input devices in the Navy's Performance Based Measures Battery (PBM), a psychomotor subset of the Aviation Selection Test Battery (ASTB).BackgroundThe PBM is a set of computerized tests used as a part of the ASTB to select aviators in the U.S. military. Official practice is not available, leading candidates to practice with unofficial re-creations and with or without access to the stick and throttle used on the PBM.MethodOur between-subjects study with 152 cadets from the U.S. Military Academy evaluated the impact of mouse/keyboard or stick/throttle practice on the psychomotor portions of the PBM compared to a control group that was only presented with an informational video.ResultsThe results showed that practice with either input device resulted in improved performance relative to control on the PBM's two-dimensional airplane tracking task (ATT). For the simpler vertical tracking task (VTT), the mouse/keyboard group showed significantly worse performance than either stick/throttle practice or control groups, indicating a transfer cost from practicing with an alternative input device.ConclusionThe results suggest that becoming familiar with the unique dynamics of the ATT may be more important than practicing with the appropriate input device. Conversely, device-specific motor learning appears to be a more impactful determinant of performance for the simpler VTT. This indicates that transfer effects from alternative input devices depend in part on properties of the task.ApplicationThis research can inform practice policies for psychomotor test selection.

BackgroundShift rotation is a popular means of aiding offshore oil and gas extraction (OGE) workers in mitigating the health and safety impacts of night shift work. However, they can disrupt workers' circadian rhythms, resulting in poor sleep quality, fatigue, and performance postrotation.ObjectiveTo determine the impacts of forward (day-to-night) and backward (night-to-day) rotations on offshore OGE workers' sleepiness, fatigue, performance levels, and subsequent circadian adaptation over time.Methods70 oil and gas workers from two offshore platforms in the Gulf of Mexico were followed for seven days, starting the day before the shift rotation. Subjective and performance-based measures of fatigue, as well as actigraphy, were collected daily from day and night workers undergoing shift rotation and compared to those on their fixed shift schedules.ResultsForward rotations negatively affected perceived sleepiness, sleep efficiency, total sleep time (measured by actigraphy), and increased reaction time on the Psychomotor Vigilance Task compared to workers assigned to fixed day shifts. The only observed impact of the backward rotation on fixed night shift workers was decreased total sleep time.DiscussionWorkers assigned to the forward rotations took longer to adapt to the shift rotation, providing insights into how fatigue risk management strategies can be tailored to better address the needs of vulnerable shift workers.ApplicationThe findings indicate that rotating shift work is detrimental to offshore workers, and it is recommended that the amount of rotating shift work during a worker's offshore assignment be minimized, especially when transitioning from day to night.

ObjectiveTo examine the effect of dual tasking on hand dominance during a bimanual visuomotor task.BackgroundMany operators need to perform separate tasks with each hand. Yet, there is no comprehensive study examining whether the right-hand visuomotor advantage found in right handers remains stable, increases or attenuates when another task is performed concurrently with the other hand.MethodsTwenty-eight right-handed participants (mean age = 22) performed 2D visuomotor tracking under either unimanual (one target, one hand) or bimanual conditions (two targets, one for each hand). Various gaze contingencies and visual displays were tested. Tracking performance of each hand was evaluated through the mean cursor-target distance.ResultsA clear right-hand advantage was found under all unimanual conditions. Under bimanual conditions, tracking accuracy decreased for both hands albeit more extensively for the left hand than the right when gaze was free, thus amplifying the above right-hand advantage. Prioritization of the right hand was associated with a gaze preference toward this hand. However, this increase in manual asymmetry was greatly alleviated when participants were instructed to fixate straight ahead, a benefit obtained at no cost in terms of overall tracking performance.ConclusionsDuring bimanual/dual tracking, there is a natural tendency for right handers to prioritize their right hand. However, this effect is strongly reduced by fixating straight ahead.ApplicationPerforming separate tasks with the right and left hands is common when piloting an aircraft. Fixating straight ahead may be useful for pilots that seek to divide more equally the negative impact of dual/bimanual tasking.

ObjectiveThis study aimed to conduct a systematic literature review of gestural interaction research by tracing its evolution from a focus on functionality and performance toward a human-centered paradigm, and to develop a theoretical framework that structures the understanding of gestural interaction processes.BackgroundDespite extensive research, no comprehensive review has yet been conducted on gestural interaction from a human-centered perspective, highlighting the need for a structured synthesis to inform design and evaluation practices.MethodWe first developed a conceptual Person-Action-Target-Environment (PATE) model for gestural interaction. Guided by this model, we conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Our review included 197 articles to identify key topics, measurement indicators, and influencing factors.ResultsThe review identified three phases in gesture research: an early focus on functionality, followed by performance-oriented studies, and a recent emphasis on user experience. Four key dimensions emerged in the measurement of gestural interaction: efficiency, ease of use, subjective experience, and tolerance degree. At least ten categories (40 factors in total) were found to influence gestural interaction, with factors related to person, interacting target and actions being extensively explored.ConclusionThis review identifies trends, measures, and influencing factors in gestural interaction research, and utilizes the PATE model to effectively structure the analysis and understanding of gestural interaction processes.ApplicationThis review provides insights and tools for researchers and designers aiming to enhance user experience in gestural interaction technologies.

ObjectiveWe conducted a literature review investigating the validity of eye tracking metrics appropriately representing trust in automation.BackgroundAs researchers grow interested in measuring trust in automation, there has been a need to find a reliable and accurate measurement tool. Many articles have measured automation trust using eye tracking, assuming that as trust increases, visual attention from eye tracking metrics decreases. Eye tracking is an attractive potential measure for its nonintrusive and objective nature.MethodIn this systematic literature review, we looked at studies that have tested the relationship between eye tracking and trust to determine its validity and reliability.ResultsAcross 22 articles that investigated the relationship between trust and eye tracking, only about half found a negative significant relationship, whereas the other half found no relationship at all.ConclusionThe relationship between automation trust and eye tracking is inconsistent and unreliable. A wide variety of trust and eye tracking metrics were used, but only about half of the papers found any kind of relationship. The relationship did not appear robust enough to prevail when different eye tracking and trust metrics were applied in various study designs.ApplicationAn effective eye tracking-trust relationship would be useful in various applications (e.g., autonomous driving). Further, this relationship is crucial when there is a clear distinction between attention allocated to automated components of a system (e.g., car display) and unrelated displays to allow for an easy separation of a location associated with high trust versus low trust.

ObjectiveWe examined whether allowing operators to self-select automation transparency level (adaptable transparency) could improve accuracy of automation use compared to nonadaptable (fixed) low and high transparency. We examined factors underlying higher transparency selection (decision risk, perceived difficulty).BackgroundIncreased fixed transparency typically improves automation use accuracy but can increase bias toward agreeing with automated advice. Adaptable transparency may further improve automation use if it increases the perceived expected value of high transparency information.MethodsAcross two studies, participants completed an uninhabited vehicle (UV) management task where they selected the optimal UV to complete missions. Automation advised the optimal UV but was not always correct. Automation transparency (fixed low, fixed high, adaptable) and decision risk were manipulated within-subjects.ResultsWith adaptable transparency, participants selected higher transparency on 41% of missions and were more likely to select it for missions perceived as more difficult. Decision risk did not impact transparency selection. Increased fixed transparency (low to high) did not benefit automation use accuracy, but reduced decision times. Adaptable transparency did not improve automation use compared to fixed transparency.ConclusionWe found no evidence that adaptable transparency improved automation use. Despite a lack of fixed transparency effects in the current study, an aggregated analysis of our work to date using the UV management paradigm indicated that higher fixed transparency improves automation use accuracy, reduces decision time and perceived workload, and increases trust in automation.ApplicationThe current study contributes to the emerging evidence-base regarding optimal automation transparency design in the modern workplace.