Cognitive Workload Assessment: A Comprehensive Review

We aimed to provide an assessment of the impact of workload manipulations on various cardiac measurements. We further sought to determine the most effective measurement approaches of cognitive workload as well as quantify the conditions under which these measures are most effective for interpretation. Cognitive workload affects human performance, particularly when load is relatively high (overload) or low (underload). Despite ongoing interest in assessing cognitive workload through cardiac measures, it is currently unclear which cardiac-based assessments best indicate cognitive workload. Although several quantitative studies and qualitative reviews have sought to provide guidance, no meta-analytic integration of cardiac assessment(s) of cognitive workload exists to date. We used Morris and DeShon's meta-analytic procedures to quantify the changes in cardiac measures due to task load conditions. Sample-weighted Cohen's d values suggest that several metrics of cardiac activity demonstrate sensitivity in response to cognitive workload manipulations. Heart rate variability measures show sensitivity to task load, conditions of event rate, and task duration. Authors of future work should seek to quantify the utility of leveraging multiple metrics to understand workload. Results suggest that assessment of cognitive workload can be done using various cardiac activity indicators. Further, given the number of valid and reliable measures available, researchers and practitioners should base their selection of a psychophysiological measure on the experimental and practical concerns inherent to their task/protocol. Findings bear implications for future assessment of cognitive workload within basic and applied settings. Future research should seek to validate conditions under which measurements are best interpreted, including but not limited to individual differences.

Cognitive workload assessment plays an important role to examine the mental status of brain for emergency situations faced by air traffic controllers, military personals and many more. It is also significant for brain disorder diagnosis and psychological health monitoring. Mental workload induced by various factors needs to be critically examined for better decisions. This paper presents an automated model for cognitive workload assessment to provide accurate categorization on intensity of workload induced by multitasking situations. Statistical wavelets corresponding to brain frequencies act as features for categorization task. The brain analysis needs technical expertise to identify the relevant information but, due to lack of sufficient number of experts, feature selection model helps to identify the relevant features responsible to categorize cognitive workload. Teaching Learning based Optimization (TLBO) method has been modified using `Logical Operators' for binary feature selection problem in the current study and evaluated based on different parameters. Results on Logical TLBO reached upto 100% for binary and multiclass classification of cognitive workload. The results also show the proposed method is converging in minimum iteration for multiclass cognitive workload. PROMETHEE based multi-criteria decision-making approach has adopted for ranking of algorithm to select the best algorithm when multiple alternatives are available. Based on study of current literature, this approach appears to be first time implemented to provide ranking of optimization algorithms. The PROMETHEE method also suggests that the Logical TLBO is best among all the other compared approaches.

In the field of neuroeconomics, the assessment of cognitive workload is a crucial issue with significant implications for real-world applications. Previous research has made progress in task-based germane cognitive load classification, but decentralized studies focusing on task-independent assessment have often produced less than optimal results. In this study, we present a stacked graph attention convolutional networks (SGATCNs) model to tackle the challenges related to task-independent cognitive workload assessment using EEG spatial information. The model employs the differential entropy (DE) and power spectral density (PSD) features of each EEG channel across four frequency bands (delta, theta, alpha, and beta) as node information. For the construction of the network structure, phase-locked values (PLVs), phase-lag indices (PLIs), Pearson correlation coefficients (PCCs), and mutual information (MI) are utilized and evaluated to generate a functional brain network. Specifically, the model aggregates spatial information on the dynamic map by stacking the graph attention layers and utilizes the convolution module to extract the frequency domain information from between the networks under each frequency band. We conducted a cognitive workload experiment with 15 subjects and selected three representative psychological experimental task paradigms (N-back, mental arithmetic, and Sternberg) to induce different levels of cognitive workload (low, medium, and high). Our framework achieved an average accuracy of 65.11% in recognizing the task-independent cognitive workload across the three scenarios.

The topic of brain-computer interface is gaining increasing attention annually, yet there is limited research on confirming the reliability of Chinese indigenous chips in EEG signal acquisition. To demonstrate the progressiveness of Chinese indigenous chips, this article compares the performance of two ADC, ADS1299 and LHE7909, in EEG applications, with ADS1299 already proven reliable in multiple studies. Both chips demonstrate reliability in capturing high-quality EEG signals with the measured amplitude error below 2%. They show remarkably similar characteristics in the performance of SSVEP, oddball experiments and cognitive workload assessments on five participants from both genders and various age groups. LHE7909 even excels in capturing subtle EEG features such as the third harmonic components evoked in SSVEP in response to 13Hz stimulation, particularly in cognitive workload assessment, though tending to generate slightly more low-frequency noise. When it comes to P300 detection, LHE7909 shows a positive wave at approximately 340ms after the stimuli, in contrast to the ADS1299 which has a 360ms latency. These findings underscore the potential advantages of Chinese indigenous chips in EEG research.

The battlefield of the future will require the warfighter to multitask in numerous ways, seriously taxing the cognitive and perceptual capabilities of even the most advanced warrior. A principal concern in developing a better understanding of how current and proposed computational technologies can supplement and augment human performance in this and other environments is determining when such assistance is required. This challenge can be parsed into 2 components: determining what set of measurements accurately reflects cognitive state, and identifying techniques for synthesizing this set of measurements into a single collective cognitive state variable. The primary thesis of this proposal is that automatic human behavioral responses serve as inherent probes for cognitive state. Further, the human perception-action system is uniquely designed to capture, process, integrate, and act on an extraordinarily diverse range of information freely available in the natural environment. Together, this system and the surrounding environment which acts on it-and on which the system acts-form a dynamic coupling. Under normal conditions these couplings remain intact. When stressed, these couplings become degraded. Based on this understanding, the authors propose a unique suite of Cognitive Workload Assessment (CWA) tools, based on real-time measurements of postural control that can serve as both a stand-alone indicator of cognitive state as well as a cueing filter for engaging other CWA sensor suites that are currently under development.

High-fidelity clinical simulations can be used by clinicians to acquire technical (physical ability and knowledge) and non-technical (cognitive and social processes) skills. Excessive cognitive workload contributes to medical errors because of the impact on both technical and non-technical skills. Many studies measure cognitive workload with psychometric instruments that limit the assessment of cognitive workload to a single time period and may involve response bias. Using eye tracking to measure task-evoked pupillary responses allows the measurement of changes in pupil diameter related to the cognitive workload associated with a specific activity. Incorporating eye tracking with high-fidelity clinical simulations provides a reliable and continuous assessment of cognitive workload. The purpose of this literature review is to summarize the use of eye-tracking technology to measure cognitive workload of healthcare providers to generate evidence-based guidelines for measuring cognitive workload during high-fidelity clinical simulations. What this manuscript adds to the body of literature is a summary of best practices related to the different methods of measuring cognitive workload, benefits and limitations of using eye tracking, and high-fidelity clinical simulation design considerations for successful integration of eye tracking.

Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance.

Purpose Despite the importance of having knowledge about a child’s cognitive functioning, less than one-third of children with cerebral palsy (CP) are formally assessed. Consequently, the cognitive strengths of many children with severe CP may be underestimated or go overlooked. This case study aimed to test accommodations to cognitive assessment administration procedures to enable switch access. Methods A 9-year-old boy with dyskinetic CP tested a suite of cognitive assessments with accommodations for single switch access and measures of user experience. The cognitive assessment included: receptive vocabulary; non-verbal reasoning; sustained attention; executive functions of problem solving and shifting; and visual perception skills. Results The participant’s ability to independently undertake assessment on the receptive vocabulary, non-verbal reasoning and the sustained attention measures indicates that accommodations made for single switch access were appropriate. Assessment took 1–2 h longer than expected for a typically developing child via standardised administration procedures, but was considerably faster than expected if undertaken via low-tech partner assisted scanning. Accessibility barriers continued to be present for the executive function and visual perception measures. Overall, the user experience was positive, both in regards to usability and cognitive load. Conclusions This case study provides emerging data for usability and accessibility of accommodations to a battery of cognitive assessment tasks. Further research is needed to devise appropriate accommodations for executive function and visual perception measures and to determine whether the accommodations are accessible more generally for children with motor and/or speech impairments. Implications for rehabilitation Accommodations can be successfully made to receptive vocabulary, non-verbal reasoning and sustained attention assessment administration procedures for switch technologies. Cognitive assessment with switch accommodations takes considerably longer to complete than standardised administration estimates for a typically developing child. Assessment may need to be scheduled over more than one session. User experience, including usability and cognitive load, of accommodations was positive.

This study investigates cognitive workload assessment in aviation emergency scenarios using electroencephalography (EEG). Thirty-seven male cadet pilots performed single-engine failure tasks on the Chengdu Tianfu-Shuangliu route using a high-fidelity Airbus A320 simulator. EEG signals were recorded using a 32-channel Emotiv EPOC Flex system, from which 448 features were extracted across time-domain, frequency-domain, and nonlinear domains. Statistical screening yielded 379 significant features for analysis. Results demonstrated that electrophysiological changes were most pronounced in the central electrode Cz region, where high workload conditions were characterized by increased top five important features. Among five machine learning algorithms evaluated, gradient boosting-based methods significantly outperformed conventional machine learning approaches, with XGBoost achieving optimal performance at 80.1% accuracy. This study confirms the feasibility of real-time pilot cognitive workload monitoring using EEG-based approaches and demonstrates the superior performance of gradient boosting algorithms in physiological signal classification.

We introduce a novel posture guidance office chair and evaluate the effectiveness of vibrotactile and visual feedback methods for guiding seated postures. For visually dominant office work such as typing on the computer, it is possible that delivering posture feedback visually may overload the visual sense while haptic feedback may be a viable alternative. We performed two experiments to compare vibrotactile and visual feedback--posture compliance and dual-task cognitive workload assessment. In the first experiment, our results showed no statistically significant difference in effectiveness between using vibrotactile and visual feedback to obtain postural compliance to a reference posture. In the second experiment, participants experienced typing performance and response time degradations from both types of feedback. However the differences in performance degradation were not statistically significant between the two feedback methods. We conclude that vibrotactile and visual feedback are similarly effective for guiding quasistatic postures in routine tasks such as seated office work.

With the increasing complexity of human-computer interaction systems, the cognitive load caused by interface information overload has become a key bottleneck affecting user experience and operational efficiency. Therefore, this paper proposes an interactive interface adaptive design model based on dynamic cognitive load evaluation and constructs a closed-loop optimization framework of “perception-evaluation-decision-execution”. Therefore, this paper proposes an interactive interface adaptive design model based on the dynamic evaluation of cognitive load, and constructs a closed-loop optimization framework of “perception evaluation decision execution”. First, a dynamic multimodal cognitive load assessment model is designed, which integrates behavioral, eye-movement, and physiological signals via a cross-modal attention mechanism, combined with time series modeling and uncertainty estimation, to achieve continuous and accurate perception of cognitive load. The root mean square error of prediction is 0.592. Second, a cognitive-driven interface adaptive decision-making framework is constructed, which uses the results of cognitive load assessment and task context as the basis for decision-making. On this basis, a cognitive-constrained reinforcement learning optimization algorithm is proposed. By introducing an upper-limit constraint on cognitive load and a strategy clipping mechanism, interaction efficiency is guaranteed and decision stability is improved. Experimental results show that the proposed method reduces task completion time by 22.7%, increases user satisfaction by 41.9%, decreases cognitive load by 25.0%, and keeps total response delay within 81 milliseconds. This study provides a systematic solution for the construction of intelligent adaptive interface with both evaluation accuracy and decision stability.

Patients with schizophrenia exhibit impairments in working memory that often appear in attenuated form in persons at high risk for the illness. The authors hypothesized that deviations in task-related brain activation and deactivation would occur in persons with an at-risk mental state performing a working memory task that entailed the maintenance and manipulation of letters. Participants at ultra high risk for developing psychosis (N=60), identified using the Comprehensive Assessment of At-Risk Mental States, and healthy comparison subjects (N=38) 14 to 29 years of age underwent functional MRI while performing a verbal working memory task. Group differences in brain activation were identified using analysis of covariance. The two groups did not show significant differences in speed or accuracy of performance, even after accounting for differences in education. Irrespective of task condition, at-risk participants exhibited significantly less activation than healthy comparison subjects in the left anterior insula. During letter manipulation, at-risk persons exhibited greater task-related deactivation within the default-mode network than comparison subjects. Region-of-interest analysis in the at-risk group revealed significantly greater right dorsolateral prefrontal cortex activation during manipulation of letters. Despite comparable behavioral performance, at-risk participants performing a verbal working memory task exhibited altered brain activation compared with healthy subjects. These findings demonstrate an altered pattern of brain activation in at-risk persons that contains elements of reduced function as well as compensation.

Compared to cardiac parameters and skin conductivities, the photoplethysmogram (PPG) recorded at fingertips and other parts near to peripheral nerve ends have been recently revealed to be yet another sensitive measure for cognitive load assessment. However, there is so far no research on measuring adolescents' cognitive load using physiological signals. A comprehensive study on the effects of PPG morphometrics over a cohort covering both adolescent and adult students is also absent. In this study, we analyze the morphological features of PPG on cognitive load assessment and compare them between adolescent and adult students. Experiments on two-level arithmetic tasks show that the PPG morphometrics reached the same level of significance on the effect of task difficulty/period as heart rate, and different morphological behaviors were also shown between adolescent and adult students during the cognitive task effects, which may imply their physiological differences across age. Physiological signals recorded by wearable devices are also found to be effective in measuring cognitive load.

The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer’s multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.

Cusp catastrophe models for cognitive workload and fatigue in teams