Real-time detection of workload changes using heart rate variability

The Clinical Significance and Application of Heart Rate Variability in Dialysis Patients: A Narrative Review

Autonomic nervous system (ANS) dysfunction is prevalent in end-stage kidney disease (ESKD) patients, carrying significant risks for morbidity and mortality. Heart rate variability (HRV) is a simple and non-invasive method to evaluate ANS functions and predict prognoses in specific patient populations. Since there is a lack of a clear understanding of the clinical significance of HRV in predicting prognoses in ESKD patients, an updated review on this topic is urgently warranted. The clinical significance of HRV in dialysis patients includes its associations with metabolic syndrome, nutritional status, intradialytic hypotension, vascular access failure, major adverse cardiovascular events, and mortality. These findings underscore the essential role of the autonomic reserve, which might denote the elevation of ANS activity as a response to external stimulus. Patients with a higher level of sympathetic activity at the resting stage, but who are unable to adequately elevate their sympathetic activity under stress might be susceptible to a worse outcome in critical circumstances. Further applications of HRV include HRV biofeedback, risk classification, and real-time HRV monitoring. Overall, HRV is an optimal tool for predicting prognoses in dialysis patients. Further study is encouraged in order to gain a clearer understanding of the clinical significance and application of HRV, and thereby enhance the care of ESKD patients.

Prospects for Augmenting Team Interactions with Real-Time Coordination-Based Measures in Human-Autonomy Teams

Complex work in teams requires coordination across team members and their technology as well as the ability to change and adapt over time to achieve effective performance. To support such complex interactions, recent efforts have worked toward the design of adaptive human-autonomy teaming systems that can provide feedback in or near real time to achieve the desired individual or team results. However, while significant advancements have been made to better model and understand the dynamics of team interaction and its relationship with task performance, appropriate measures of team coordination and computational methods to detect changes in coordination have not yet been widely investigated. Having the capacity to measure coordination in real time is quite promising as it provides the opportunity to provide adaptive feedback that may influence and regulate teams' coordination patterns and, ultimately, drive effective team performance. A critical requirement to reach this potential is having the theoretical and empirical foundation from which to do so. Therefore, the first goal of the paper is to review approaches to coordination dynamics, identify current research gaps, and draw insights from other areas, such as social interaction, relationship science, and psychotherapy. The second goal is to collate extant work on feedback and advance ideas for adaptive feedback systems that have potential to influence coordination in a way that can enhance the effectiveness of team interactions. In addressing these two goals, this work lays the foundation as well as plans for the future of human-autonomy teams that augment team interactions using coordination-based measures.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

Driver distraction and its effects on partially automated driving performance: A driving simulator study among young-experienced drivers

Drivers of partially automated vehicles (PAVs) are relieved from parts of the driving tasks allocated to the automated driver. Ironically, these drivers are obligated to continuously monitor the driving task at all times and keep their attention on the roadway. This reduction in the driving task's demands and cognitive workload may encourage drivers to engage with non-driving related tasks (NDRT), which may impair drivers' awareness of the road environment and, as a result, compromise safety. This study examined how engagement with a visual-manual NDRT affects the driving performance of PAV drivers. Thirty-seven participants were randomly assigned to one of two experimental conditions in a driving simulator. Each consisted of two simulated drives. The first experimental condition included one drive under manual driving conditions and another under partially automated driving conditions (i.e., L2). Both drives had no NDRT involved. The second experimental condition included one drive under L2 without an NDRT and one drive under L2, including engagement with an NDRT. Participants' eye movements and heart rate were recorded throughout the experiment. Across various measures, the findings showed that under L2 driving conditions, engagement with an NDRT impairs driving performance in two primary aspects: (1) drivers were less aware of road hazards, and (2) their mental workload was higher when they engaged with an NDRT. In addition, the findings reveal that for drivers engaged with an NDRT, the attentional time-sharing strategy between the NDRT and the roadway monitoring task affected the probability of identifying a hazard. This study shows the adverse effects of engagement with an NDRT under L2 driving conditions on driving performance. Future studies should examine different interventions to mitigate these effects, assuring that drivers are constantly aware of the roadway environment.

Mental workload and occupational burnout among the faculty members and administrative staff of Yazd Public Health School

BACKGROUND

Administrative staff may be exposed to a great deal of the mental workload (MWL) due to the long working hours and the responsibility of responding to large numbers of clients. Occupational burnout (OB) is one of the issues that can be affected by MWL. The aim of the present study was to investigate the relationship between mental MWL and OB, as well as the internal interactions between OB dimensions.

MATERIALS AND METHODS

This cross-sectional and descriptive (correlation) study was conducted among faculty members and administrative staff of public health school of Yazd province. Samples were collected through the simple random sampling. NASA task load index and Maslach questionnaire were used for the assessment of MWL and OB, respectively. Frequency and percentage were used for the descriptive analysis. Spearman, Mann-Whitney U, and Kruskal-Wallis H tests were used for inferential statistics.

RESULTS

Ultimately, 29 individuals from faculty members and 82 individuals from administrative staff participated in this study. Most of the participants (73.9%) had experienced many MWL instances in performing their jobs duties. No significant relationship between MWL with OB and MWL with any of the demographic characteristics was found, as well (P > 0.05). In case of OB, a significant relationship was discovered between depersonalization with gender and personal accomplishment with work section (P < 0.05).

CONCLUSIONS

MWL cannot be considered as a contributing factor of OB of the administrative staff because no significant relationship has been noted between them. Reducing working hours, selection of suitable staff, and allowing a few minutes to rest on a daily basis represent the suggested solutions for reducing the MWL of staff.

Windowed multiscale synchrony: modeling time-varying and scale-localized interpersonal coordination dynamics

Social interactions are pervasive in human life with varying forms of interpersonal coordination emerging and spanning different modalities (e.g. behaviors, speech/language, and neurophysiology). However, during social interactions, as in any dynamical system, patterns of coordination form and dissipate at different scales. Historically, researchers have used aggregate measures to capture coordination over time. While those measures (e.g. mean relative phase, cross-correlation, coherence) have provided a wealth of information about coordination in social settings, some evidence suggests that multiscale coordination may change over the time course of a typical empirical observation. To address this gap, we demonstrate an underutilized method, windowed multiscale synchrony, that moves beyond quantifying aggregate measures of coordination by focusing on how the relative strength of coordination changes over time and the scales that comprise social interaction. This method involves using a wavelet transform to decompose time series into component frequencies (i.e. scales), preserving temporal information and then quantifying phase synchronization at each of these scales. We apply this method to both simulated and empirical interpersonal physiological and neuromechanical data. We anticipate that demonstrating this method will stimulate new insights on the mechanisms and functions of synchrony in interpersonal contexts using neurophysiological and behavioral measures.

Statistical process control charts for better analysis of sequential data in psychology: The case of illusion of control experiments

Illusion of control (IOC) is a bias in the judgment of personal success with implications to learning theories and health policies; some important questions in the investigation of IOC may be related to traditional measures in the field, namely self-assessment using Likert scales about the sense of control. Statistical process control (SPC) and Shewhart charts are methods developed to monitor and control industrial processes, never applied in psychological studies before. The present two studies investigated the use of the technique of Shewhart charts in the analysis of IOC. The purpose was to explore the use of SPC and Shewhart charts in the analysis of data sequences from psychological experiments; the objective was to analyze the results of reaction time (RT) data sequences plotted in SPC charts, in comparison with self-assessment judgments from an IOC task. Participants were 63 undergraduate students (Study 1) and 103 mine workers (Study 2) instructed to try to control a traffic light on a computer by pressing or not the keyboard. Higher probabilities of the successful outcome generated judgments of illusion and shifts (due to cognitive activity) in the charts of RT; lower probabilities resulted in null illusion and RT presented a random and stable profile. Patterns for different groups emerged in Shewhart charts. SPC can contribute to the analysis of the behavior of sequences of data in psychological studies, so that the charts indicate changes and patterns not detected by traditional ANOVA and other linear models.

Effect of cognitive load on drivers' State and task performance during automated driving: Introducing a novel method for determining stabilisation time following take-over of control

This research paper explores the impact of cognitive load on drivers' physiological state and driving performance during an automated driving to manual control transition scenario, using a driving simulator. Whilst driving in the automated mode, cognitive load was manipulated using the "N-Back" task, which participants engaged with via a visual display. Results suggest that non-optimal levels of workload during the automated driving conditions impair driving performance, especially lateral control of the vehicle, and the magnitude of this impairment varied with increasing cognitive load. In addition to these findings, the present paper introduces a novel method for determining stabilisation times of both driver state and driving performance indicators following a transition of vehicle control. Using this method we demonstrate that mean and standard deviation of lane position impairments were found to take longer to stabilise following transition to manual driving following a higher level of cognitive load during the automated driving period, taking up to 22 s for driving performance to normalise after take-over. In addition, heart rate parameters take between 20 and 30 s to stabilise following a planned take-over request. Finally, this paper demonstrates how the magnitude of cognitive load can be estimated in context of automated driving using physiological measures, captured by consumer electronic devices. We discuss the impact our findings have on the design of SAE Level 3 systems. Relevant suggestions are provided to the research community and automakers working on future implementation of vehicles capable of conditional automation.

Influence of Increasing Task Complexity and Use of Informational Assistance Systems on Mental Workload

(1) Background: Cognitive aspects and complexity in modern manual mixed model assembly are increasing. To reduce mental workload (MWL), informational assistance systems are introduced. The influence of complexity and used assistance system on MWL should be investigated to further improve the implementation of such assistance systems. (2) Methods: Using a simulated close to real-life assembly task a 2 × 3 design was chosen, with two levels of assembly complexity (within subjects) and three different assistance systems (paper, Augmented Reality (AR)-glasses, tablet-between subjects). MWL was measured using either physiological response (electrocardiogram (ECG) and eye-tracking) or performance indicators. (3) Results: An influence of task complexity on MWL can be shown. Additionally, usability based differences between the used assistance systems become more evident with reference to the results of area of interest analysis. (4) Conclusions: Using a multi-modal measurement approach, it is possible to detect complexity-based differences in MWL. Additional research on validity and alignment is needed to further use these for (neuro-) ergonomic considerations and recommendations.

Safety evaluation of freeway interchange merging areas based on driver workload theory

Prior safety evaluations of interchange merging areas have mostly focused on traffic conflicts and operating speeds, without considering how these factors can influence the driver workload. Researches regarding the level of driver workload have largely concentrated on urban roads, tunnel sections, and basic freeway segments, without considering the impact of merging traffic flows on safety. Therefore, this study has investigated how merging vehicles can impact through-driver workload and safety. Three independent field experiments were conducted on freeways, involving 18 drivers and 17 interchanges. The results showed that the vehicles merging onto freeway impact driver workload and driving performance, generating potential risk that cannot be ignored. Merging into the mainline traffic flow increases the through-driver workload to a higher level or even exceeds the safety threshold, despite there being better geometrical conditions of interchanges than those of basic freeway segments. When the volume of merging vehicles exceeds 564 pcu/h or the traffic saturation is above 0.485, driver workload rises above the safety thresholds and the driving risk is elevated, which potentially would lead to crashes. This study offers insights for more effective segment division of operating speed prediction, and dynamic risk management with regard to interchange safety.

Effect of Mental Workload on Breathing Pattern and Heart Rate for a Working Memory Task: A Pilot Study

Mental workload or cognitive load is the total amount of mental resources required while doing a task. Apart from qualitative measures, various physiological signals are being used for assessment of mental workload. However, very limited research has been done on assessment of cognitive load from respiratory signals. In the present study, we have tried to analyze the cognitive load mainly based on respiratory features. n-back memory test has been modified to impart low and high cognitive load. The peripheral blood volume signal (PPG) collected while executing the task is used to reconstruct the breathing pattern signal. A number of morphological as well as statistical features are calculated from this reconstructed signal. Finally a classifier is used for classifying the low and high cognitive load. Results show that a classification accuracy of 76.8% is obtained while using respiratory features only. A maximum accuracy of 81.80% is obtained if we combine time domain PPG features with respiratory features. The features finally selected can also be used to study the habituation effect.

A Systematic Review of Physiological Measures of Mental Workload

Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.

Persistence of repeated self-reported illusion of control as a product of action and outcome association in productive and preventive scenarios

Individuals interpret themselves as causal agents when executing an action to achieve an outcome, even when action and outcome are independent. How can illusion of control be managed? Once established, does it decay? This study aimed to analyze the effects of valence, probability of the outcome [p(O)] and probability of the actions performed by the participant [p(A)], on the magnitude of judgments of control and corresponding associative measures (including Rescorla-Wagner's, Probabilistic Contrast, and Cheng's Power Probabilistic Contrast models). A traffic light was presented on a computer screen to 81 participants who tried to control the green or red lights by pressing the spacebar, after instructions describing a productive or a preventive scenario. There were 4 blocks of 50 trials under all of 4 different p(O)s in random order (0.10, 0.30, 0.70, and 0.90). Judgments were assessed in a bidimensional scale. The 2 × 4 × 4 mixed experimental design was analyzed through General Linear Models, including factor group (between-subject valence), and block and p(O) (within subjects). There was a small effect of group and a large and direct effect of p(O) on judgments. Illusion was reported by 66% of the sample and was positive in the productive group. The oscillation of p(O) produced stronger illusions; decreasing p(O)s produced nil or negative illusions. Only Rescorla-Wagner's could model causality properly. The reasons why p(A) and the other models could not generate significant results are discussed. The results help to comprehend the importance of keeping moderate illusions in productive and preventive scenarios.

Intra-Individual Variability in Vagal Control Is Associated With Response Inhibition Under Stress

Dynamic intra-individual variability (IIV) in cardiac vagal control across multiple situations is believed to contribute to adaptive cognition under stress; however, a dearth of research has empirically tested this notion. To this end, we examined 25 U.S. Army Soldiers (all male, mean age = 30.73, standard deviation (SD) = 7.71) whose high-frequency heart rate variability (HF-HRV) was measured during a resting baseline and during three conditions of a shooting task (training, low stress, high stress). Response inhibition was measured as the correct rejection (CR) of friendly targets during the low and high stress conditions. We tested the association between the SD of HF-HRV across all four task conditions (IIV in vagal control) and changes in response inhibition between low and high stress. Greater differences in vagal control between conditions (larger IIV) were associated with higher tonic vagal control during rest, and stronger stress-related decreases in response inhibition. These results suggest that flexibility in vagal control is supported by tonic vagal control, but this flexibility also uniquely relates to adaptive cognition under stress. Findings are consistent with neurobehavioral and dynamical systems theories of vagal function.

Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

Feature Weight Driven Interactive Mutual Information Modeling for Heterogeneous Bio-Signal Fusion to Estimate Mental Workload

Many people suffer from high mental workload which may threaten human health and cause serious accidents. Mental workload estimation is especially important for particular people such as pilots, soldiers, crew and surgeons to guarantee the safety and security. Different physiological signals have been used to estimate mental workload based on the n-back task which is capable of inducing different mental workload levels. This paper explores a feature weight driven signal fusion method and proposes interactive mutual information modeling (IMIM) to increase the mental workload classification accuracy. We used EEG and ECG signals to validate the effectiveness of the proposed method for heterogeneous bio-signal fusion. The experiment of mental workload estimation consisted of signal recording, artifact removal, feature extraction, feature weight calculation, and classification. Ten subjects were invited to take part in easy, medium and hard tasks for the collection of EEG and ECG signals in different mental workload levels. Therefore, heterogeneous physiological signals of different mental workload states were available for classification. Experiments reveal that ECG can be utilized as a supplement of EEG to optimize the fusion model and improve mental workload estimation. Classification results show that the proposed bio-signal fusion method IMIM can increase the classification accuracy in both feature level and classifier level fusion. This study indicates that multi-modal signal fusion is promising to identify the mental workload levels and the fusion strategy has potential application of mental workload estimation in cognitive activities during daily life.

Distraction or cognitive overload? Using modulations of the autonomic nervous system to discriminate the possible negative effects of advanced assistance system

The interaction with Advanced Driver Assistance Systems has several positive implications for road safety, but also some potential downsides such as mental workload and automation complacency. Malleable attentional resources allocation theory describes two possible processes that can generate workload in interaction with advanced assisting devices. The purpose of the present study is to determine if specific analysis of the different modalities of autonomic control of nervous system can be used to discriminate different potential workload processes generated during assisted-driving tasks and automation complacency situations. Thirty-five drivers were tested in a virtual scenario while using head-up advanced warning assistance system. Repeated MANOVA were used to examine changes in autonomic activity across a combination of different user interactions generated by the advanced assistance system: (1) expected take-over request without anticipatory warning; (2) expected take-over request with two-second anticipatory warning; (3) unexpected take-over request with misleading warning; (4) unexpected take-over request without warning. Results shows that analysis of autonomic modulations can discriminate two different resources allocation processes, related to different behavioral performances. The user's interaction that required divided attention under expected situations produced performance enhancement and reciprocally-coupled parasympathetic inhibition with sympathetic activity. At the same time, supervising interactions that generated automation complacency were described specifically by uncoupled sympathetic activation. Safety implications for automated assistance systems developments are considered.

An EEG-based mental workload estimator trained on working memory task can work well under simulated multi-attribute task

Mental workload (MW)-based adaptive system has been found to be an effective approach to enhance the performance of human-machine interaction and to avoid human error caused by overload. However, MW estimated from the spontaneously generated electroencephalogram (EEG) was found to be task-specific. In existing studies, EEG-based MW classifier can work well under the task used to train the classifier (within-task) but crash completely when used to classify MW of a task that is similar to but not included in the training data (cross-task). The possible causes have been considered to be the task-specific EEG patterns, the mismatched workload across tasks and the temporal effects. In this study, cross-task performance-based feature selection (FS) and regression model were tried to cope with these challenges, in order to make EEG-based MW estimator trained on working memory tasks work well under a complex simulated multi-attribute task (MAT). The results show that the performance of regression model trained on working memory task and tested on multi-attribute task with the feature subset picked-out were significantly improved (correlation coefficient (COR): 0.740 ± 0.147 and 0.598 ± 0.161 for FS data and validation data respectively) when compared to the performance in the same condition with all features (chance level). It can be inferred that there do exist some MW-related EEG features can be picked out and there are something in common between MW of a relatively simple task and a complex task. This study provides a promising approach to measure MW across tasks.

Remote detection of mental workload changes using cardiac parameters assessed with a low-cost webcam

We introduce a new framework for detecting mental workload changes using video frames obtained from a low-cost webcam. Image processing in addition to a continuous wavelet transform filtering method were developed and applied to remove major artifacts and trends on raw webcam photoplethysmographic signals. The measurements are performed on human faces. To induce stress, we have employed a computerized and interactive Stroop color word test on a set composed by twelve participants. The electrodermal activity of the participants was recorded and compared to the mental workload curve assessed by merging two parameters derived from the pulse rate variability and photoplethysmographic amplitude fluctuations, which reflect peripheral vasoconstriction changes. The results exhibit strong correlation between the two measurement techniques. This study offers further support for the applicability of mental workload detection by remote and low-cost means, providing an alternative to conventional contact techniques.

Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques

Identifying the abnormal changes of mental workload (MWL) over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety-critical human-machine systems. It is known that various neuroimaging technologies can be used to identify the MWL variations. In order to classify MWL into a few discrete levels using representative MWL indicators and small-sized training samples, a novel EEG-based approach by combining locally linear embedding (LLE), support vector clustering (SVC) and support vector data description (SVDD) techniques is proposed and evaluated by using the experimentally measured data. The MWL indicators from different cortical regions are first elicited by using the LLE technique. Then, the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations. It is shown that the clusters can be interpreted as the binary class MWL. Furthermore, a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators. By combining the two schemes, a SVC-SVDD framework is proposed, where the clear-cut (smaller) cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped (larger) cluster into two classes. Finally, three-class MWL levels (low, normal and high) can be identified automatically. The experimental data analysis results are compared with those of several existing methods. It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies.

Operator functional state classification using least-square support vector machine based recursive feature elimination technique

This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized.

National culture moderates the influence of mental effort on subjective and cardiovascular measures

Subjective measures of mental effort have been shown to be relatively insensitive in Indonesian participants. An open question is whether this insensitivity reflects how mental effort is experienced or how it is reported. We compared the performance, subjective workload ratings, heart rate and heart-rate variability (HRV) of 31 Dutch and 30 Indonesian participants under single- and dual-task conditions. Indonesians performed faster but less accurately and used a narrower range of subjective workload ratings than did the Dutch. Dutch participants showed a decrease in HRV both in the mid-frequency (MF) and high-frequency bands and an increase in heart rate during task performance compared with the resting period. Indonesians showed this pattern in the MF band only. The decrease of HRV in the MF band in both groups suggests that the relative insensitivity of subjective mental effort scales among Indonesians has to do with how workload is reported rather than with how it is experienced.

PRACTITIONER SUMMARY

The sensitivity of the subjective measures of mental workload has been shown to depend on culture. Here, we show that heart-rate variability reacts similarly to workload in Eastern as in Western participants. This suggests that culture influences more how invested mental effort is reported than how it is experienced psychophysiologically.