Workload and Performance: Associations, Insensitivities, and Dissociations

The role of visual conditions and aircraft type on different aspects of pilot workload

OBJECTIVE

The objective of our work was to assess the impact of flight conditions by aircraft type on the workload estimated using NASA-Task Load Index (NASA-TLX).

BACKGROUND

Learning about subjective workload is important for assessing the impact of a pilot's work environment on their performance in the cockpit. This is an important element of flight safety and includes the prevention of aviation accidents.

METHODS

The study included 146 military pilots that fly the following aircrafts: flying fast-jet (21), fixed-wing (24), and rotary-wing (101). The NASA-TLX questionnaire was used to assess workload and pilots were asked to determine the level of workload resulting from flying under the following conditions: daytime flight (VFR), night-vision flight performed under Night Visual Flight Rules (NVFR), and night-vision flight using night-vision goggles (NVGs).

RESULTS

The highest level of workload was consistently attributed to flights performed under NVG conditions. NVFR conditions were rated as the most burdensome, while VFR conditions were rated as the least burdensome. Fast-jet pilots rated their mental performance and effort workload as significantly higher than pilots of other aircrafts.

CONCLUSION

Pilots' perceived workload is influenced by both flight conditions and the type of aircraft they fly. Workload knowledge is important for flight safety and should be taken into account during training and flight-task planning.

APPLICATION

The results of our study can be useful both in flight training and in work on the effectiveness of the human-machine interface. Awareness of one's own limitations due to the work environment can help improve flight safety.

Measuring cognitive workload in the nuclear control room: a review

Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human-technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human-system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human-system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human-system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human-system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload.

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Return-to-Manual Performance can be Predicted Before Automation Fails

OBJECTIVE

This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control.

BACKGROUND

Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects.

METHOD

Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation.

RESULTS

For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance.

CONCLUSIONS

RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust.

APPLICATIONS

Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool

OBJECTIVE

This study developed and evaluated a mental workload-based adaptive automation (MWL-AA) that monitors surgeon cognitive load and assist during cognitively demanding tasks and assists surgeons in robotic-assisted surgery (RAS).

BACKGROUND

The introduction of RAS makes operators overwhelmed. The need for precise, continuous assessment of human mental workload (MWL) states is important to identify when the interventions should be delivered to moderate operators' MWL.

METHOD

The MWL-AA presented in this study was a semi-autonomous suction tool. The first experiment recruited ten participants to perform surgical tasks under different MWL levels. The physiological responses were captured and used to develop a real-time multi-sensing model for MWL detection. The second experiment evaluated the effectiveness of the MWL-AA, where nine brand-new surgical trainees performed the surgical task with and without the MWL-AA. Mixed effect models were used to compare task performance, objective- and subjective-measured MWL.

RESULTS

The proposed system predicted high MWL hemorrhage conditions with an accuracy of 77.9%. For the MWL-AA evaluation, the surgeons' gaze behaviors and brain activities suggested lower perceived MWL with MWL-AA than without. This was further supported by lower self-reported MWL and better task performance in the task condition with MWL-AA.

CONCLUSION

A MWL-AA systems can reduce surgeons' workload and improve performance in a high-stress hemorrhaging scenario. Findings highlight the potential of utilizing MWL-AA to enhance the collaboration between the autonomous system and surgeons. Developing a robust and personalized MWL-AA is the first step that can be used do develop additional use cases in future studies.

APPLICATION

The proposed framework can be expanded and applied to more complex environments to improve human-robot collaboration.

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration

Objective. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work environments, especially a collaborative environment where humans and machines work in close proximity, often with physical contact. In a physical human robot collaboration (pHRC), the robot performs complex motion sequences. Any unexpected robot behavior or faulty interaction might raise safety concerns. Error-related potentials, naturally generated by the brain when a human partner perceives an error, have been extensively employed in BCI as implicit human feedback to adapt robot behavior to facilitate a safe and intuitive interaction. However, the integration of BCI technology with error-related potential for robot control demands failure-free integration of highly uncertain electroencephalography (EEG) signals, particularly influenced by the physical and cognitive state of the user. As a higher workload on the user compromises their access to cognitive resources needed for error awareness, it is crucial to study how mental workload variations impact the error awareness as it might raise safety concerns in pHRC. In this study, we aim to study how cognitive workload affects the error awareness of a human user engaged in a pHRC.Approach. We designed a blasting task with an abrasive industrial robot and manipulated the mental workload with a secondary arithmetic task of varying difficulty. EEG data, perceived workload, task and physical performance were recorded from 24 participants moving the robot arm. The error condition was achieved by the unexpected stopping of the robot in 33% of trials.Main results. We observed a diminished amplitude for the prediction error negativity (PEN) and error positivity (Pe), indicating reduced error awareness with increasing mental workload. We further observed an increased frontal theta power and increasing trend in the central alpha and central beta power after the unexpected robot stopping compared to when the robot stopped correctly at the target. We also demonstrate that a popular convolution neural network model, EEGNet, could predict the amplitudes of PEN and Pe from the EEG data prior to the error.Significance. This prediction model could be instrumental in developing an online prediction model that could forewarn the system and operators of the diminished error awareness of the user, alluding to a potential safety breach in error-related potential-based BCI system for pHRC. Therefore, our work paves the way for embracing BCI technology in pHRC to optimally adapt the robot behavior for personalized user experience using real-time brain activity, enriching the quality of the interaction.

Individual differences, ADHD diagnosis, and driving performance: effects of traffic density and distraction type

The present study examined the impact of individual differences, attention, and memory deficits on distracted driving. Drivers with ADHD are more susceptible to distraction which results in more frequent collisions, violations, and licence suspensions. Consequently, the present investigation had 36 participants complete preliminary questionnaires, memory tasks, workload indices, and four, 4-min simulated driving scenarios to evaluate such impact. It was hypothesised ADHD diagnosis, type of cellular distraction, and traffic density would each differentially and substantively impact driving performance. Results indicated traffic density and distraction type significantly affected the objective driving facets measured, as well as subjective and secondary task performance. ADHD diagnosis directly impacted secondary task performance. Results further showed significant interactions between distraction type and traffic density on both brake pressure and steering wheel angle negatively impacting lateral and horizontal vehicle control. Altogether, these findings provide substantial empirical evidence for the deleterious effect of cellphone use on driving performance.Practitioner summary: This study examined how ADHD diagnosis, traffic density, and distraction type affect driver behaviour. Participants completed driving behaviour questionnaires, memory tasks, workload indices, and driving scenarios. Results showed that ADHD diagnosis impacted secondary task performance, while traffic and distractions significantly impacted driving performance as well secondary task performance and workload.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

OBJECTIVE

(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.

BACKGROUND

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.

METHODS

Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.

RESULTS

While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.

CONCLUSION

Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Adopting Stimulus Detection Tasks for Cognitive Workload Assessment: Some Considerations

OBJECTIVE

This article tackles the issue of correct data interpretation when using stimulus detection tasks for determining the operator's workload.

BACKGROUND

Stimulus detection tasks are a relative simple and inexpensive means of measuring the operator's state. While stimulus detection tasks may be better geared to measure conditions of high workload, adopting this approach for the assessment of low workload may be more problematic.

METHOD

This mini-review details the use of common stimulus detection tasks and their contributions to the Human Factors practice. It also borrows from the conceptual framework of the inverted-U shape model to discuss the issue of data interpretation.

RESULTS

The evidence being discussed here highlights a clear limitation of stimulus detection task paradigms.

CONCLUSION

There is an inherent risk in using a unidimensional tool like stimulus detection tasks as the primary source of information for determining the operator's psychophysiological state.

APPLICATION

Two recommendations are put forward to Human Factors researchers and practitioners dealing with the interpretation conundrum of dealing with stimulus detection tasks.

Sharing imagery and analysis tools in a simulated submarine control room

We examined the impact of sharing periscope imagery and analysis tools in eighteen five-member teams (Sonar, Periscope, 2xTrack Motion Analysts, Track Manager) who undertook simulated submarine patrol tasks. Compared to a baseline condition, sharing imagery to team members increased perceived individual workload, with no improvement to team performance (tactical picture accuracy). When both imagery and analysis tools were shared, perceived workload increased and tactical picture compilation was more accurate. Despite this improved tactical picture for the imagery and analysis tools condition, there was no advantage to mission completion (rendezvous/close contact detection) or situation awareness. In contrast to the increased subjective workload, individuals in teams provided with shared imagery (with or without tools) had a lower physiological response (heart rate, electrodermal) to task demands compared to the baseline condition. Sharing imagery and analysis tools likely benefited tactical picture compilation by enabling dynamic task redistribution and multiple streams of concurrent data analysis.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition : Trial-varying Wald model

Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.

Performance shaping factors for future sustainable energy management: A new integrated approach

The current literature suggests that a lack of integration between engineering for performance shaping factors (PSFs) and workplace energy management (WEM) is a significant barrier to improving energy management practices (EMP) and power plant efficiency. The study identified three research objectives in response to this research gap: (1) conduct a systematic literature review to analyze current studies; (2) develop a novel integrative model capable of predicting EMP; and (3) test the novel model's validity and reliability through an empirical study in thermal power plants. In this study, a group of academic and energy experts designed research instruments to achieve the study's objectives, which were then pilot-tested. Partial least square structural equation modeling was utilized to analyze the data in this study. The study successfully developed a new model for future sustainable energy management in power plants and a new model integrating the PSFs and WEM to predict power plant energy performance, aiming to enhance communication between operators and EMP in power plants. The model exhibited exceptional explanatory and predictive abilities, yielding a strong fit. Furthermore, the incorporation of success factors associated with PSFs positively influenced the EMP. The data set followed a normal distribution, confirming the model's reliability and validity. Significantly, this study achieved a breakthrough by being the first to integrate success factors for PSFs and WEM in thermal power plants, thus effectively addressing an unexplored area of research. However, the inconsistencies in the current studies emphasize the necessity for additional investigations into the strategy of PSFs in EMP within power plants.

A comparison of the psychological effects of robot motion in physical and virtual environments

A mixed-methods approach was used to assess the fidelity of virtual environments as ergonomic assessment tools for human-robot interaction. Participants performed a visual search task in the physical environment while a nearby collaborative robot repeatedly extended its arm toward them. This scenario was reconstructed in two virtual environments with different levels of graphical detail. Measures of presence, task performance, workload, and anxiety were taken to determine the effect of robot motion in all three environments. Task performance decreased in response to robot motion in the physical environment, while workload and anxiety increased. This simple effect of motion was consistent across environments for measures of task performance and anxiety. However, people performed faster overall in virtual reality, and the effect of motion on workload was greatly reduced in virtual reality. Results in the virtual environments suggest that people were distracted by the sound of the robot, and that presence was affected by manipulations of immersion and coherence.

Mental workload variations during different cognitive office tasks with social media interruptions

Interruption at work by social media (SM) is a pervasive phenomenon. This study investigated the impact of SM interruptions and task cognitive levels on mental workload (MWL) and physiological indexes. Each subject performed six simulated computer tasks differentiated by two factors: task cognitive level and performing condition. MWL was reflected through three categories of data: perceived mental workload, physiological indexes, and primary task performance. The results revealed significant effects of SM interruptions on heart rate, low-frequency/high-frequency (LF/HF) ratio, and skin conductance. ANOVA results showed there were main effects of task cognitive level on LF/HF and skin conductance. These effects during interrupted tasks were more profound. In addition, participants experienced higher MWL and recorded lower primary task performance in the knowledge-based task than the rule- and skill-based tasks. Our findings can guide managers and employees regarding appropriate use of SM in the workplace and better managing interruption and workload.Practitioner Summary: Office workers suffer from increased overall mental workload due to unpredictable interruptions while working. This study shows that participants' mental workload increased when receiving SM interruptions, which was more profound during complex tasks. This highlights the importance of SM interruptions management for employees' health, performance, and mobile application developers.Abbreviations: ANOVA: analysis of variance; DSSQ: dundee stress state questionnaire; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalographic; HPA: hypothalamus-pituitaryadrenocortical; HR: heart rate; HRV: heart rate variability; LF/HF: low frequency/high frequency; MSDs: musculoskeletal disorders; MWL: mental workload; NN: normal to normal; RMS: root means square; RR: time duration between two successive R peaks; RT: response time; SC: skin conductance; SDNN: standard deviation of normal to normal; SM: social media; TCL: task cognitive level; TPC: task performing condition; WMC: working memory capacity.

How and why humans trust: A meta-analysis and elaborated model

Trust exerts an impact on essentially all forms of social relationships. It affects individuals in deciding whether and how they will or will not interact with other people. Equally, trust also influences the stance of entire nations in their mutual dealings. In consequence, understanding the factors that influence the decision to trust, or not to trust, is crucial to the full spectrum of social dealings. Here, we report the most comprehensive extant meta-analysis of experimental findings relating to such human-to-human trust. Our analysis provides a quantitative evaluation of the factors that influence interpersonal trust, the initial propensity to trust, as well as an assessment of the general trusting of others. Over 2,000 relevant studies were initially identified for potential inclusion in the meta-analysis. Of these, (n = 338) passed all screening criteria and provided therefrom a total of (n = 2,185) effect sizes for analysis. The identified dependent variables were trustworthiness, propensity to trust, general trust, and the trust that supervisors and subordinates express in each other. Correlational results demonstrated that a large range of trustor, trustee, and shared, contextual factors impact each of trustworthiness, the propensity to trust, and trust within working relationships. The emphasis in the present work on contextual factors being one of several trust dimensions herein originated. Experimental results established that the reputation of the trustee and the shared closeness of trustor and trustee were the most predictive factors of trustworthiness outcome. From these collective findings, we propose an elaborated, overarching descriptive theory of trust in which special note is taken of the theory’s application to the growing human need to trust in non-human entities. The latter include diverse forms of automation, robots, artificially intelligent entities, as well as specific implementations such as driverless vehicles to name but a few. Future directions as to the momentary dynamics of trust development, its sustenance and its dissipation are also evaluated.

Workload transition rate matters: Evidence from growth curve modeling

This research examined three specific gaps in the workload transition literature: (1) the impact of workload transition rate, (2) the applicability of current theoretical explanations, and (3) the variability of performance overall and over time. Sixty Naval flight students multitasked in an unmanned aerial vehicle control testbed and workload transitioned at three rates: slow, medium, or fast. Response time and accuracy were analyzed via growth curve modeling. Slow transitions had the largest decline in performance over time. Medium transitions had some of the slowest, but most accurate and consistent performance. Fast transitions had some of the fastest, but least accurate performance. However, all performance trends significantly varied, suggesting multiple theoretical explanations may apply and performance may also depend on the individual. Design guidance on how to maximize performance goals with transition rate is provided, but future research needs to study the theoretical explanations and impact of individual differences further.

Exploring the Peak-End Effects in Air Traffic Controllers' Mental Workload Ratings

OBJECTIVE

This study examined whether professional air traffic controllers (ATCos) were subject to peak-end effects in reporting their mental workload after performing an air traffic control task, and in predicting their mental workload in future scenarios.

BACKGROUND

In affective experience studies, people's evaluation of a period of experience is strongly influenced by the most intense (peak) point and the endpoint. However, whether the effects exist in mental workload evaluations made by professional operators is still not known.

METHOD

In Study 1, 20 ATCos performed air traffic control scenarios on high-fidelity radar simulators and reported their mental workload. We used a 2 (high peak, low peak) × 2 (high end, low end) within-subject design. In Study 2, another group of 43 ATCos completed a survey asking them to predict their mental workload given the same air traffic control scenarios.

RESULTS

In Study 1, ATCos reported higher mental workload after completing the high-peak and the high-end scenarios. In contrast, in Study 2, ATCos predicted the peak workload effect but not the end workload effect when asked to predict their experience in dealing with the same scenarios.

CONCLUSION

Peak and end effects exist in subjective mental workload evaluation, but experts only had meta-cognitive awareness of the peak effect, and not the end effect.

APPLICATION

Researchers and practitioners that use subjective workload estimates for work design decisions need to be aware of the potential impact of peak and end task demand effects on subjective mental workload ratings provided by expert operators.

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing a continuous brain rate, an index of cognitive activation, and does not require human declarative knowledge. The aim is to induce models automatically from data, supporting replicability, generalisability and applicability across fields and contexts. This specific method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data, aimed at fitting a novel brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had, on average, a test Mean Absolute Percentage Error of around 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. These findings point to the existence of quasi-stable blocks of automatically learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Additionally, across-subject models, induced with data from an increasing number of participants, thus trained with data containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable and does not rely on ad hoc human crafted models.

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

A mirror in the sky: the effects of map format and user expertise on navigation performance and mental workload

A novel map display concept named Mirror in the Sky (MitS) has been introduced to improve performance and reduce workload in navigation tasks. However, this display will be novel to most users and as such, an evaluation of MitS in comparison with more conventional map formats is warranted. This study investigated the effects of map display format (MitS vs. north-up and track-up maps) and user expertise on mental workload (MWL) and performance, using both soldiers (experts) and civilians (novices) as participants. Participants followed a prescribed route to a destination in a virtual environment (route following task) while also performing a secondary task (detection response task). Soldiers generally performed better than civilians. Soldiers reported a higher MWL with MitS than with the north-up map, whereas civilians reported a higher MWL with MitS than with the track-up map. Regardless of user expertise, there were performance and workload challenges with MitS, despite its potential. Practitioner summary: A new map display concept called Mirror in the Sky (MitS) was compared with two conventional map formats: a north-up and track-up map. The experiment tested soldier and civilian users in a route following task. Both groups got further into the route and had fewer obstacle collisions with north-up and track-up maps than they did with the MitS map. MWL measures generally indicated higher workload with MitS. Abbreviations: MitS: mirror in the sky; FFOV: forward field of view; AR: augmented reality; MWL: mental workload; VR: virtual reality; HF: human factors; HR: heart rate; HRV: heart rate variability; DRT: detection response task; DRDC: defence research and development Canada; VE: virtual environment; RT: response time; ANOVA: analysis of variance.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

OBJECTIVE

To identify physiological correlates to stress in intensive care unit nurses.

BACKGROUND

Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap.

METHOD

Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4).

RESULTS

Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008).

DISCUSSION

The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses.

APPLICATION

Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

An evidence-based, structured, expert approach to selecting essential indicators of primary care quality

BACKGROUND

The purpose of this article is to illustrate the application of an evidence-based, structured performance measurement methodology to identify, prioritize, and (when appropriate) generate new measures of health care quality, using primary care as a case example. Primary health care is central to the health care system and health of the American public; thus, ensuring high quality is essential. Due to its complexity, ensuring high-quality primary care requires measurement frameworks that can assess the quality of the infrastructure, workforce configurations, and processes available. This paper describes the use of the Productivity Measurement and Enhancement System (ProMES) to compile a targeted set of such measures, prioritized according to their contribution and value to primary care.

METHODS

We adapted ProMES to select and rank existing primary care measures according to value to the primary care clinic. Nine subject matter experts (SMEs) consisting of clinicians, hospital leaders and national policymakers participated in facilitated expert elicitation sessions to identify objectives of performance, corresponding measures, and priority rankings.

RESULTS

The SMEs identified three fundamental objectives: access, patient-health care team partnerships, and technical quality. The SMEs also selected sixteen performance indicators from the 44 pre-vetted, currently existing measures from three different data sources for primary care. One indicator, Team 2-Day Post Discharge Contact Ratio, was selected as an indicator of both team partnerships and technical quality. Indicators were prioritized according to value using the contingency functions developed by the SMEs.

CONCLUSION

Our article provides an actionable guide to applying ProMES, which can be adapted to the needs of various industries, including measure selection and modification from existing data sources, and proposing new measures. Future work should address both logistical considerations (e.g., data capture, common data/programming language) and lingering measurement challenges, such as operationalizating measures to be meaningful and interpretable across health care settings.

Physical and cognitive demands associated with police in-vehicle technology use: an on-road case study

Motor vehicle crashes are a leading cause of police officers' deaths in line of duty. These crashes have been mainly attributed to officers' driving distraction caused by the use of in-vehicle technologies while driving. This paper presents a 3-h ride-along study of 20 police officers to assess the physical and cognitive demands associated with using in-vehicle technologies. The findings suggested that the mobile computer terminal (MCT) was the most frequently used in-vehicle system for the officers. In addition, officers perceived the MCT to significantly increase their visual, cognitive, and physical demands compared to other in-vehicle technologies. Evidence from electromyography and eye-tracking measures suggested that officers with more experience as a patrol officer and those who were working in more congested areas experienced higher cognitive workload. Furthermore, it was found that as the ride-along duration increased, there were indications of muscle fatigue in medial deltoid and triceps brachii muscles. Practitioner summary: This study assessed the impact of police in-vehicle technology use in an on-road case study. The findings provide new data and knowledge for police agencies and vehicle manufacturers to develop administrative measures and in-vehicle technology innovations to improve police officers' health and safety.

Cognitive load mitigates the executive but not the arousal vigilance decrement

Previous research has shown opposite effects of dual tasking on the vigilance decrement phenomenon. We examined the executive (i.e., detecting infrequent critical signals) and arousal (i.e., sustaining a fast reaction to stimuli without much control on responses) vigilance decrements as a function of task load. Ninety-six participants performed either a single signal-detection (i.e., executive vigilance) task, a single reaction time (i.e., arousal vigilance) task, or a dual vigilance task with the same stimuli and procedure. All participants self-reported their fatigue' state along the session. Exploratory analyses included data from a previous study with a triple task condition. Task load significantly modulated the executive but not the arousal vigilance decrement. Interestingly, the largest increase in mental fatigue was observed in the single executive vigilance task condition. We discuss limitations of classic vigilance theories to account for the vigilance decrement and changes in mental fatigue as a function of task load.

An Investigation of Speech Features, Plant System Alarms, and Operator-System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work

OBJECTIVE

To investigate speech features, human-machine alarms, and operator-system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios.

BACKGROUND

Theories and models of cognitive workload are critical for the design and evaluation of human-machine systems. Unfortunately, there are very few nonintrusive cognitive workload measures available for realistic dynamic human-machine interaction.

METHOD

The study was conducted in a full-scope control room research simulator of an advanced nuclear reactor. Six crews, each consisting of three operators, participated in 12 scenarios. The operators rated their workload every second minute. Machine learning algorithms were trained to estimate operators' workload based on crew communication, operator-system interaction, and system alarms.

RESULTS

Random Forest (RF) utilizing speech and system features achieved an accuracy of 67% on test data. Utilizing speech features only, the accuracy achieved was 63%. The most important speech features were pitch, amplitude, and articulation rate. A 61% accuracy was achieved when alarms and operator-system interaction features were used. The most important features were the number of alarms and amount of operator-system interaction. Accuracy for algorithms trained for each operator ranged from 39% to 98%, with an average of 72%. For a majority of analyses performed, RF and extreme gradient boosting (XGB) outperformed other algorithms.

CONCLUSION

The results demonstrate that the features investigated and machine learning models developed provide a potential for the dynamic nonintrusive measurement of cognitive workload.

APPLICATION

The approach presented can be developed for nonintrusive workload measurement in real-world human-machine applications, simulator-based training, and research.

Reference values and subscale patterns for the task load index (TLX): a meta-analytic review

The Task Load Index (TLX) is the predominant instrument for self-reporting workload. On the basis of a meta-analytic review of 556 studies, this paper supplies reference values for TLX and its six subscales across domains, technologies, regions, and real-life/lab settings. Across domains, TLX spans mean values from 35 for leisure to 56 for manual labour. TLX tends to be driven upward by the subscales of mental demand and effort and downward by the subscales of physical demand and frustration. For technologies, handheld devices are associated with lower TLX, possibly because they are simpler and more task-specific. TLX also varies across regions in that it is higher for studies in Asia than in Europe and North America. This variation is only partly explained by co-variation in domains. Furthermore, TLX is higher and its subscales more inter-correlated when it is studied in real-life rather than lab settings. Practitioner summary: Practitioners can use the reference values supplied in this paper to benchmark their TLX measurements against those from the corpus of TLX research. Furthermore, the reported subscale patterns add to the diagnostic power of the TLX instrument. Abbreviations: TLX: task load index; MD: mental demand; PD: physical demand; TD: temporal demand; EF: effort; PE: performance; FR: frustration.

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

Cognitive workload is assumed to influence performance due to resource competition. However, there is a lack of evidence for a direct relationship between changes in workload within an individual over time and changes in that individual's performance. We collected performance data using a multiple object-tracking task in which we measured workload objectively in real-time using a modified detection response task. Using a multi-level Bayesian model controlling for task difficulty and past performance, we found strong evidence that workload both during and preceding a tracking trial was predictive of performance, such that higher workload led to poorer performance. These negative workload-performance relationships were remarkably consistent across individuals. Importantly, we demonstrate that fluctuations in workload independent from the task demands accounted for significant performance variation. The outcomes have implications for designing real-time adaptive systems to proactively mitigate human performance decrements, but also highlight the pervasive influence of cognitive workload more generally.

The ANT Executive Control Index: No Evidence for Temporal Decrement

OBJECTIVE

This study tested whether indices of executive control, alertness, and orienting measured with Attention Network Test (ANT) are vulnerable to temporal decrement in performance.

BACKGROUND

Developing the resource theory of sustained attention requires identifying neurocognitive processes vulnerable to decrement. Executive control processes may be prone to impairment in fatigue states. Such processes are also highlighted in alternative theories. Determining the role of executive control in vigilance can both advance theory and contribute to practical countermeasures for decrement in human factors contexts.

METHOD

In Study 1, 80 participants performed the standard ANT for an extended duration of about 55 to 60 min. Study 2 (160 participants) introduced manipulations of trial blocking and stimulus degradation intended to increase resource depletion. Reaction time and accuracy measures were analyzed. Subjective stress and workload were assessed in both studies.

RESULTS

In both studies, the ANT induced levels of subjective workload and task disengagement consistent with previous sustained attention studies. No systematic decrement in any performance measure was observed.

CONCLUSION

Executive control assessed by the ANT is not highly vulnerable to temporal decrement, even when task demands are elevated. Future work should differentiate executive control processes; proactive control may be more implicated in sustained attention decrement than in reactive control.

APPLICATION

Designing systems and interfaces to reduce executive control demands may be generally beneficial but will not directly mitigate temporal performance decrement. Enhancing design guidelines and neuroergonomic methods for monitoring operator attention requires further work to identify key neurocognitive processes for decrement.

Where We Come from and Where We Are Going: A Systematic Review of Human Factors Research in Driving Automation

During the last decade, research has brought forth a large amount of studies that investigated driving automation from a human factor perspective. Due to the multitude of possibilities for the study design with regard to the investigated constructs, data collection methods, and evaluated parameters, at present, the pool of findings is heterogeneous and nontransparent. This literature review applied a structured approach, where five reviewers investigated n = 161 scientific papers of relevant journals and conferences focusing on driving automation between 2010 and 2018. The aim was to present an overview of the status quo of existing methodological approaches and investigated constructs to help scientists in conducting research with established methods and advanced study setups. Results show that most studies focused on safety aspects, followed by trust and acceptance, which were mainly collected through self-report measures. Driving/Take-Over performance also marked a significant portion of the published papers; however, a wide range of different parameters were investigated by researchers. Based on our insights, we propose a set of recommendations for future studies. Amongst others, this includes validation of existing results on real roads, studying long-term effects on trust and acceptance (and of course other constructs), or triangulation of self-reported and behavioral data. We furthermore emphasize the need to establish a standardized set of parameters for recurring use cases to increase comparability. To assure a holistic contemplation of automated driving, we moreover encourage researchers to investigate other constructs that go beyond safety.

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

BACKGROUND

Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.

OBJECTIVE

The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.

METHODS

In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.

RESULTS

Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual's mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.

CONCLUSION

This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

Specifying advantages of multi-modal cueing: Quantifying improvements with augmented tactile information

This work examines how tactile cues, encoded with azimuth and distance information, compare with visual and speech cues on performance and mental workload in a target detection task. Two experiments are reported using a simulated environment in which targets were presented at varying azimuth and distance locations. In the first experiment, participants engaged targets both while stationary and while in motion using tactile, visual, or speech cues. A no cueing control was included. In the second multi-modal experiment, participants completed the same task using cue pairings. Performance metrics consisted of hits, misses due to non-detection, misses due to inaccurate engagement, false alarms, response time, navigation errors as well as subjective ratings of mental workload scores were also collected. Results demonstrate the superiority of tactile cues as a means to communicate target location information either as a single modality or when paired with the two other cue types.

Profiling a Spectrum of Mental Job Demands and their Linkages to Employee Outcomes

Working life is becoming more mentally demanding and intense due to technological acceleration. The present study explored employees' experiences of different mental job demands (MJDs) and their outcomes (job burnout, job performance, and meaning of work). We focused on intra- and inter-individual variations and possible harmful combinations of MJDs, which we explored via latent profile analysis (LPA). To identify harmful combinations of MJDs, we also investigated how the profiles of MJDs related to the outcomes of interest. The study was based on a diverse sample of Finnish employees (n = 4,583). LPA showed that both intra-individual and inter-individual variation characterized MJDs as we identified five latent profiles of MJDs. The most harmful profile, which predicted the most negative outcomes (particularly job burnout), was characterized by employees' scoring high on all MJDs. A profile characterized by low learning demands and moderate level of other MJDs was also a harmful combination in terms of outcomes. In contrast, a profile characterized by moderate level of learning demands and low level of other MJDs did not relate to negative outcomes. Altogether, the findings suggest that different MJDs may co-occur implying risks to employee well-being and performance. However, MJDs simultaneously form a complex spectrum that may differ within and between individuals.

Measurement and identification of mental workload during simulated computer tasks with multimodal methods and machine learning

This study attempted to multimodally measure mental workload and validate indicators for estimating mental workload. A simulated computer work composed of mental arithmetic tasks with different levels of difficulty was designed and used in the experiment to measure physiological signals (heart rate, heart rate variability, electromyography, electrodermal activity, and respiration), subjective ratings of mental workload (the NASA Task Load Index), and task performance. The indices from electrodermal activity and respiration had a significant increment as task difficulty increased. There were no significant differences between the average heart rate and the low-frequency/high-frequency ratio among tasks. The classification of mental workload using combined indices as inputs showed that classification models combining physiological signals and task performance can reach satisfying accuracy at 96.4% and an accuracy of 78.3% when only using physiological indices as inputs. The present study also showed that ECG and EDA signals have good discriminating power for mental workload detection. Practitioner summary: The methods used in this study could be applied to office workers, and the findings provide preliminary support and theoretical exploration for follow-up early mental workload detection systems, whose implementation in the real world could beneficially impact worker health and company efficiency. Abbreviations: NASA-TLX: the national aeronautics and space administration-task load index; ECG: electrocardiographic; EDA: electrodermal activity; EEG: electroencephalogram; LDA: linear discriminant analysis; SVM: support vector machine; KNN: k-nearest neighbor; ANNs: artificial neural networks; EMG: electromyography; PPG: photoplethysmography; SD: standard deviation; BMI: body mass index; DSSQ: dundee stress state questionnaire; ANOVA: analysis of variance; SC: skin conductance; RMS: root mean square; AVHR: the average heart rate; HR: heart rate; LF/HF: the ratio between the low frequencies band and the high frequency band; PSD: power spectral density; MF: median frequency; HRV: heart rate variability; BPNN: backpropagation neural network.

Quantifying the qualities of language

We here quantify the qualities of language. Specifically, we derive numerical values, and associated variability scores for statements of general probability as well as for anchor terms commonly expressed on Likert-type scales. Our results show significant inter-responder variability in the understanding of such terms. We also demonstrate scale compression such that average estimates are poorly aligned to the a priori expectations of equal response spacing in commonly used verbal response instruments. Terms further show intriguing propensities such that people agree more about statements of agreement, but manifestly disagree more about statements of disagreement. The valence of these terms is thus systematically related to their degree of common understanding. The accuracy of language, especially when used in conjunction with modern advanced technologies, proves vital for effective communication. Our work demonstrates that the tool of language still remains a fairly blunt one, but one that can be sharpened by systematic quantitative evaluation, as we demonstrate here.

Understanding individualistic response patterns when assessing expert operators on nuclear power plant control tasks

We evaluated the performance of three highly practiced participants on three task types that comprised a simulated nuclear power plant control operation. Multiple subjective, physiological, and objective performance measures were collected on these three highly-practiced individuals. Results indicated ceiling effects in terms of performance accuracy, yet each individual adopted a unique response strategy across the respective sub-tasks. Their maximised accuracy was achieved at the expense of longer response times across differing sub-tasks. The measures which proved diagnostic and predictive of performance capacity were explored. The current conclusion presents us with an invidious problem in that performance and workload associations, insensitivities, and dissociations may be unique to each individual operator, and may well depend also upon the overall task in context. Such findings push our science away from seeking nomothetic assertions and toward individuated concerns. In consequence, the age of the idiographic may well be upon us. Practitioner summary: The importance and relevance of nuclear power control is self-evident. Concerns here have centred around the safety of the technology and its operators. Our work informs practitioners in this industry, and in Ergonomics in general, of the response of highly trained individuals in these safety-critical, operational domains. We show that even experts engage in personal and individual strategies, an observation critical to the assessment of this specific workplace, and potentially all others. Abbreviations: NPP: nuclear power plant; ROs: reactor operators; MCR: main control room; LOA: levels of automation; EOP: emergency operating procedure; OP: operating procedures; ISA: instantaneous self-assessment; DSSQ: Dundee stres state questionnaire.

Neuroergonomics Applications of Electroencephalography in Physical Activities: A Systematic Review

Recent years have seen increased interest in neuroergonomics, which investigates the brain activities of people engaged in diverse physical and cognitive activities at work and in everyday life. The present work extends upon prior assessments of the state of this art. However, here we narrow our focus specifically to studies that use electroencephalography (EEG) to measure brain activity, correlates, and effects during physical activity. The review uses systematically selected, openly published works derived from a guided search through peer-reviewed journals and conference proceedings. Identified studies were then categorized by the type of physical activity and evaluated considering methodological and chronological aspects via statistical and content-based analyses. From the identified works (n = 110), a specific number (n = 38) focused on less mobile muscular activities, while an additional group (n = 22) featured both physical and cognitive tasks. The remainder (n = 50) investigated various physical exercises and sporting activities and thus were here identified as a miscellaneous grouping. Most of the physical activities were isometric exertions, moving parts of upper and lower limbs, or walking and cycling. These primary categories were sub-categorized based on movement patterns, the use of the event-related potentials (ERP) technique, the use of recording methods along with EEG and considering mental effects. Further information on subjects' gender, EEG recording devices, data processing, and artifact correction methods and citations was extracted. Due to the heterogeneous nature of the findings from various studies, statistical analyses were not performed. These were thus included in a descriptive fashion. Finally, contemporary research gaps were pointed out, and future research prospects to address those gaps were discussed.

Sustained Attention to Science: A Tribute to the Life and Scholarship of Joel Warm

OBJECTIVE

To provide an evaluative synthesis of the life and scientific contributions of the late Joel Warm.

BACKGROUND

As the doyen of vigilance research, Joel Warm expanded our understanding and horizons concerning this critical response capacity. However, he also made widespread and profound contributions to many other areas of perception and applied psychology, as we elucidate here.

METHOD

Using archival sources, personal histories, and analysis of extant literature documenting Warm's own productivity, we articulate his life in science.

RESULTS

Our synthesis illustrates the continued, broad, influential, and expanding impact that one individual can exert on diverse fields of study. Whole bodies of understanding of human behavior have been illuminated by his exemplary career.

APPLICATION

By understanding his path to success in applied experimental psychology, we anticipate that others will be motivated, inspired, and guided to replicate and even outstrip a lifetime of such seminal and influential contributions. The presence of individuals such as Warm serves as a primary motive in enhancing Humans Factors/Ergonomics Science.

Tell Them How They Did: Feedback on Operator Performance Helps Calibrate Perceived Ease of Use in Automated Driving

The development of automated driving will profit from an agreed-upon methodology to evaluate human–machine interfaces. The present study examines the role of feedback on interaction performance provided directly to participants when interacting with driving automation (i.e., perceived ease of use). In addition, the development of ratings itself over time and use case specificity were examined. In a driving simulator study, N = 55 participants completed several transitions between Society of Automotive Engineers (SAE) level 0, level 2, and level 3 automated driving. One half of the participants received feedback on their interaction performance immediately after each use case, while the other half did not. As expected, the results revealed that participants judged the interactions to become easier over time. However, a use case specificity was present, as transitions to L0 did not show effects over time. The role of feedback also depended on the respective use case. We observed more conservative evaluations when feedback was provided than when it was not. The present study supports the application of perceived ease of use as a diagnostic measure in interaction with automated driving. Evaluations of interfaces can benefit from supporting feedback to obtain more conservative results.