Cardiac-Activity Measures for Assessing Airport Ramp-Tower Controller's Workload

Heart rate (HR) and heart rate variability (HRV) potentially offer objective, continuous, and non-intrusive measures of human-operator's mental workload. Such measurement capability is attractive for workload assessment in complex laboratory simulations or safety-critical field testing. The present study compares mean HR and HRV data with self-reported subjective workload ratings collected during a high-fidelity human-in-the-loop simulation of airport ramp traffic control operations, which involve complex cognitive and coordination tasks. Mean HR was found to be weakly sensitive to the workload ratings, while HRV was not sensitive or even contradictory to the assumptions. Until more knowledge on stress response mechanisms of the autonomic nervous system is obtained, it is recommended that these cardiac-activity measures be used with other workload assessment tools, such as subjective measures

Vagal Recovery From Cognitive Challenge Moderates Age-Related Deficits in Executive Functioning

Decline in executive functioning (EF) is a hallmark of cognitive aging. We have previously reported that faster vagal recovery from cognitive challenge is associated with better EF. This study examined the association between vagal recovery from cognitive challenge and age-related differences in EF among 817 participants in the Midlife in the U.S. study (aged 35–86). Cardiac vagal control was measured as high-frequency heart rate variability. Vagal recovery moderated the association between age and EF (β = .811, p = .004). Secondary analyses revealed that older participants (aged 65–86) with faster vagal recovery had superior EF compared to their peers who had slower vagal recovery. In contrast, among younger (aged 35–54) and middle-aged (aged 55–64) participants, vagal recovery was not associated with EF. We conclude that faster vagal recovery from cognitive challenge is associated with reduced deficits in EF among older, but not younger individuals

Inside Out: Detecting Learners' Confusion to Improve Interactive Digital Learning Environments

Confusion is an emotion that is likely to occur while learning complex information. This emotion can be beneficial to learners in that it can foster engagement, leading to deeper understanding. However, if learners fail to resolve confusion, its effect can be detrimental to learning. Such detrimental learning experiences are particularly concerning within digital learning environments (DLEs), where a teacher is not physically present to monitor learner engagement and adapt the learning experience accordingly. However, with better information about a learner's emotion and behavior, it is possible to improve the design of interactive DLEs (IDLEs) not only in promoting productive confusion but also in preventing overwhelming confusion. This article reviews different methodological approaches for detecting confusion, such as self-report and behavioral and physiological measures, and discusses their implications within the theoretical framework of a zone of optimal confusion. The specificities of several methodologies and their potential application in IDLEs are discussed

Examining Women\u27s Psychophysiological Responses Under Increasingly Obvious Sexism

When women experience sexism, it may at first be subtle and difficult to label only becoming clearer over time. Sexism is often ambiguous in nature and experienced over an extended period; therefore, studying sexism as it occurs in daily life is crucial to extending our understanding of how women cope with discrimination. Past research has shown that women may experience maladaptive physiological responses when exposed to various forms of sexism. The current study investigated women’s cardiovascular reactivity and recovery responses to prolonged, increasingly obvious sexism. Women evaluated resumes in a mock search committee meeting with two male confederates whose statements about the female candidate increased in the clarity of sexism throughout the discussion period. Heart Rate (HR) and Respiratory Sinus Arrhythmia (RSA) reactivity, recovery, self-reported anger and anxiety, group identification, and perceived sexism were measured in the study. Results demonstrated that women’s physiological reactivity changed throughout the discussion period in response to the increasingly clarity of sexism. When exposed to sexism, women’s heart rate reactivity systematically increased and respiratory sinus arrhythmia reactivity systematically decreased (RSA suppression) as sexism increased from not expressed, to ambiguous, to clear. In contrast, women in the comparison condition (i.e., not exposed to the sexist committee members) did not display increasing physiological reactivity as the clarity of sexism increased. These patterns of physiological reactivity and their correlations with anger, anxiety, gender identification, and perceived sexism are discussed and provide insight into potential motivational and emotional states of participants throughout the study. Results supported the approach of examining physiological reactivity over time and provided strong justification for further investigation into other cardiovascular markers (e.g., cardiac output, total peripheral resistance)

Physiological Characteristics and Nonparametric Test for Master-Slave Driving Task’s Mental Workload Evaluation in Mountain Area Highway at Night

With the rapid development of advanced mobile intelligent terminals, driving tasks are diverse, and new traffic safety problems occur. We propose a new research on physiological characteristics and nonparametric tests for the master-slave driving task, especially for evaluation of drivers’ mental workload in mountain area highway in nighttime scenario. First, we establish the experimental platform based driving simulator and design the master-slave driving task. Second, based on the physiological data and subjective evaluation for mental workload, we use statistical methods to composite the physical changes evolution analysis in a driving simulator. Finally, we finished nonparametric test of the drivers’ psychological load and road test. The results show that in compassion with the daytime scenario, drivers should pay much effort to driving skills and risk identification in the nighttime scenario. Thus, in the same driving condition, drivers should bear the higher level of mental workload, and it has been subjected to even greater pressures and intensity of emotions. Document type: Articl

A field study of mental workload: conventional bus drivers versus bus rapid transit drivers

Editing Services Awareness English-speaking Publish your Policy Brief rapidly today and inspire change for tomorrow. Banner advert for Australian Journal of Psychology, now open access Full Article Figures & data References Citations Metrics Reprints & Permissions Get access Abstract Road traffic accidents are increasing worldwide and cause a high number of fatalities and injuries. Mental Work Load (MWL) is a contributing factor in road safety. The primary aim of this work was to study important MWL factors and then compare conventional and BRT (Bus Rapid Transit) drivers' MWL. This study evaluated bus drivers' MWL using the Driving Activity Load Index (DALI) questionnaire conducted with 123 bus drivers in Tehran. The results revealed significant differences between conventional and BRT drivers' mental workload. Moreover, data modelling showed that some organisational and environmental factors such as bus type, working hours per day, road maze, and route traffic volume contribute to drivers' mental workload. These findings suggest some essential customised factors that may help measure and offer practical solutions for decreasing the level of bus drivers' MWL in real-world road driving. Practitioner summary Mental workload is affected by several contributing factors. Depending on the working context, some of these contributing factors have a more significant influence on the level of the experienced MWL. Therefore, the main factors influencing the MWL of BRT and conventional bus drivers were assessed in their real-life environment. Abbreviations: MWL: mental work load; BRT: bus rapid transit; CB: conventional bus; DALI: driving activity load index; NASA-TLX: NASA task load index; SWAT: subjective workload assessment technique; EEG: electroencephalography electrocardiogram; fNIRS: functional magnetic resonance imaging; ITS: intelligent transportation systems; AVL: automated vehicle locatio

Design and Evaluation of an Adaptive Virtual Reality-Based Training System

Successful operation of military aviation depends on effective pilot training. The current training capabilities of the United States Air Force might not be sufficient to meet the demand for new pilots. To help resolve this issue, this study focused on developing a prototype of an adaptive virtual reality (VR) training system. The system was built leveraging the three key elements of an adaptive training system including the trainee’s performance measures, adaptive logic, and adaptive variables. The prototype was based on a procedure for an F-16 cockpit and included adaptive feedback, display features, and various difficulty levels to help trainees maintain an optimal level of cognitive workload while completing their training. After conducting a pilot study with 14 participants, a trend favoring the use of adaptive training was identified. Results suggest that adaptive training could improve performance and reduce workload as compared to the traditional non-adaptive VR-based training. Further work is required to further validate the findings with a larger sample size. Implementation of adaptive VR training has the potential to reduce training time and cost. The results from this study can assist in developing future adaptive VR-training systems

Assessing the Effectiveness of Workload Measures in the Nuclear Domain

An operator\u27s performance and mental workload when interacting with a complex system, such as the main control room (MCR) of a nuclear power plant (NPP), are major concerns when seeking to accomplish safe and successful operations. The impact of performance on operator workload is one of the most widely researched areas in human factors science with over five hundred workload articles published since the 1960s (Brannick, Salas, & Prince, 1997; Meshkati & Hancock, 2011). Researchers have used specific workload measures across domains to assess the effects of taskload. However, research has not sufficiently assessed the psychometric properties, such as reliability, validity, and sensitivity, which delineates and limits the roles of these measures in workload assessment (Nygren, 1991). As a result, there is no sufficiently effective measure for indicating changes in workload for distinct tasks across multiple domains (Abich, 2013). Abich (2013) was the most recent to systematically test the subjective and objective workload measures for determining the universality and sensitivity of each alone or in combination. This systematic approach assessed taskload changes within three tasks in the context of a military intelligence, surveillance, and reconnaissance (ISR) missions. The purpose for the present experiment was to determine if certain workload measures are sufficiently effective across domains by taking the findings from one domain (military) and testing whether those results hold true in a different domain, that of nuclear. Results showed that only two measures (NASA-TLX frustration and fNIR) were sufficiently effective at indicating workload changes between the three task types in the nuclear domain, but many measures were statistically significant. The results of this research effort combined with the results from Abich (2013) highlight an alarming problem. The ability of subjective and physiological measures to indicate changes in workload varies across tasks (Abich, 2013) and across domain. A single measure is not able to measure the complex construct of workload across different tasks within the same domain or across domains. This research effort highlights the importance of proper methodology. As researchers, we have to identify the appropriate workload measure for all tasks regardless of the domain by investigating the effectiveness of each measure. The findings of the present study suggest that responsible science include evaluating workload measures before use, not relying on prior research or theory. In other words, results indicate that it is only acceptable to use a measure based on prior findings if research has tested that measure on the exact task and manipulations within that specific domain

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question