Cognitive Load Research Articles

Minimize Mental Workload and Fatigue of Horizontal Directional Drilling Worker Using Rating Scale Mental Effort and Swedish Occupational Fatigue Inventory Methods.

Horizontal Directional Drilling (HDD) is a drilling technique that does not damage the groundsurface; the HDD process involves a stage that requires high concentration and physical skills and isoften carried out in challenging working conditions, causing mental Workload and fatigue in HDDworkers. HDD workers should be given improvements in their work methods to be safe whenworking due to being too tired or losing focus. HDDs must be monitored in real-time, which requiresworkers to get good results without errors so that workers experience workload pressure andmental fatigue. This study aims to measure and minimize mental Workload and fatigue in HDDworkers and determine improvement proposals based on the why-why diagram. Mental workloadmeasurement using the RSME method and fatigue using SOFI and why diagrams are used to findcause and effect in mental Workload and fatigue experienced by HDD workers. Based on the analysisof RSME, the scale value obtained based on six indicators of WL, WD, WP, WME, WA, and WF is 92.05,which is in the category of enormous business carried out, and the KiK indicator or fatigue gets thehighest scale value of 106.52 with the business category given is very large so that HDD workersexperience a mental workload. Mental workload and fatigue correlate. Based on the results of SOFI,the sleepiness dimension received a score of 21.83 with a high level of fatigue category. Therefore,the proposed improvement to the causal results of the Why Diagram is adding the workforce,adjusting work shifts, and monitoring and evaluating HDD workers.

Feasibility study on virtual reality-based training for skin cancer screening: Bridging the gap in dermatological education

AbstractDespite growing interest in the inclusion of virtual reality (VR) in medical education, its full potential for immersive and interactive training remains underutilized, particularly in dermatology. As part of a multidisciplinary project, we tested the feasibility of integrating a VR training scenario for a complete skin cancer screening into the curriculum with 58 medical students. The evaluation focused on simulation usability, cognitive task load, immersion, change in perceived competence, suitability, and cybersickness using established and adapted questionnaires. Participants reported rather high levels of system usability and immersion and medium cognitive task load. Self-assessed competence in performing a skin cancer screening increased significantly after the training simulation.Prior skin cancer screening experience correlated positively with self-assessed competence and theoretical knowledge, which themselves were neither related to gender nor age. Age correlated negatively with perceived usability and immersion, enjoyment of learning during the simulation and assessed appropriateness of the simulation to learn a dermatological full-body examination while being positively correlated with perceived cybersickness. Gender was significantly associated with the need for technical help during the simulation and openness to new technologies.As a blueprint of a feasibility evaluation, this study can contribute to further refinement of the presented and relatable VR applications in medical curricula.

Designing a Data Dashboard for Health Departments and Overdose Fatality Review Teams

IntroductionOverdose Fatality Review (OFR) teams within health departments rely on data from multiple isolated sources to study the opioid crisis. Recently, we linked electronic health record data with state agency data to form a regional Substance Misuse Data Commons (SMDC) using privacy-preserving record linkage. Our goal in this study was to use human factors design principles to design a data dashboard for OFR teams using the linked datasets that will overcome current barriers within OFR workflows. MethodsWe utilized NASA task load surveys, semi-structured interviews, and design sessions with end users to identify data needs for an optimal dashboard design. We assessed current workloads, data collection processes, and desired future state. We subsequently performed iterative design sessions for the generation and evaluation of low-fidelity prototypes. To overcome issues with privacy and security, we used synthetic data in a cloud-based platform to represent the SMDC for simulation. ResultsEleven OFR organizers participated. Pre-dashboard surveys on existing workflow showed high mental workload associated with data aggregation and case review, identifying a need for more accessible, comprehensive data. In our low-fidelity dashboard demo with synthetic data, iterative design adjustments were made in data visualization, storyline organizations, and theme-based data aggregation across pre-hospital and hospital data. ConclusionsWe refined the data dashboard prototype into a high-fidelity version, set for further usability and human factors evaluation. We addressed privacy and security concerns through synthetic data use while the real-world data is maintained in a HIPAA-secure Azure cloud environment with access for approved users.

Effects of Homophone Priming on the Decision-Making in Perceptual Color Recognition of Mandarin Speakers

This study delves into the nuanced interactions between language, cognition, and perception by examining the impact of homophones on decision-making and perceptual color recognition among native Mandarin speakers. Drawing on theories of linguistic relativity and cognitive processing, this research hypothesized that homophonic primes would subtly influence both decision-making processes and color recognition tasks. Participants were assigned at random to surveys where they were unknowingly exposed to stimuli with and without homophonic primes before engaging in color recognition tasks involving subtle shade differentiations. Contrary to initial expectations, statistical analysis revealed no significant differences in decision-making accuracy between conditions, suggesting that homophones may not overtly affect conscious decision-making processes in our bilingual cohort. However, intriguing variations emerged in the perceptual color recognition task, where participants exposed to homophonic primes showed tendencies to perceive color shades more similarly compared to those in the control condition. This finding implies a potential cognitive load effect or subtle priming influence on perceptual categorization tied to linguistic structures. The implications of these findings challenge traditional views on language's role in shaping cognitive processes, highlighting the complexity of how linguistic structures such as homophones may operate at subconscious levels. The study contributes to ongoing debates in cognitive science and linguistics by underscoring the need for nuanced approaches to understanding cross-linguistic cognitive mechanisms. Future research should further explore the Whorfian hypothesis within bilingual contexts, considering additional variables such as language proficiency and cultural influences to elucidate the intricate relationship between language and cognition.

Enhancing user experience and trust in advanced LLM-based conversational agents

This study explores the enhancement of user experience (UX) and trust in advanced Large Language Model (LLM)-based conversational agents such as ChatGPT. The research involves a controlled experiment comparing participants using an LLM interface with those using a traditional messaging app with a human consultant. The results indicate that LLM-based agents offer higher satisfaction and lower cognitive load, demonstrating the potential for LLMs to revolutionize various applications from customer service to healthcare consultancy and shopping assistance. Despite these positive findings, the study also highlights significant concerns regarding transparency and data security. Participants expressed a need for clearer understanding of how LLMs process information and make decisions. The perceived opacity of these processes can hinder user trust, especially in sensitive applications such as healthcare. Additionally, robust data protection measures are crucial to ensure user privacy and foster trust in these systems. To address these issues, future research and development should focus on enhancing the transparency of LLM operations and strengthening data security protocols. Providing users with clear explanations of how their data is used and how decisions are made can build greater trust. Moreover, specialized applications may require tailored solutions to meet specific user expectations and regulatory requirements. In conclusion, while LLM-based conversational agents have demonstrated substantial advantages in improving user experience, addressing transparency and security concerns is essential for their broader acceptance and effective deployment. By focusing on these areas, developers can create more trustworthy and user-friendly AI systems, paving the way for their integration into diverse fields and everyday use.

Cognitive Load Theory: A Bibliometric Study over a Decade and Pedagogical Implications

Sweller's cognitive load theory has profoundly influenced educational practices since the 1990s. This paper offers a concise introduction to cognitive load theory and presents an analysis of the current state of domestic research on this topic from 2013 to 2022, using the Web of Science Core Collection and the bibliometric software CiteSpace. Looking ahead, cognitive load theory holds the potential to underpin the design of instructional strategies in second language learning for Chinese English learners, thereby improving the efficiency of classroom teaching.

CMAA–AHP: combinatorial multicriteria acceptability analysis with the analytic hierarchy process

AbstractCombinatorial multi-criteria acceptability analysis (CMAA) is a framework for supporting multicriteria group decisions that provides both a detailed analysis of the effects of individual decision-maker inputs as well as interactive guidance for a consensus-building process. The analytical hierarchy process (AHP) is a widely-used model of decision-maker evaluations that is based on pairwise comparisons. The goal of this work is to show how CMAA can be integrated with AHP in order to make its benefits available to AHP users. We use a minimal input format for AHP which avoids a problem with inconsistency and also reduces the cognitive load on the decision-makers. We extend the CMAA method by introducing new judgement and preference sensitivity variables, which provide helpful insights for the facilitator of the group decision. An example illustrates the combined CMAA–AHP method and its ability to deliver consensus in a very small number of iterations. Monte Carlo simulation is used to study the convergence behavior of the method for a range of problem dimensions. It was found that the mean number of steps to reach consensus grows linearly with the number of alternatives and criteria. We consider two previously published group decisions that use the standard AHP approach of averaging decision-maker judgements and preferences. In both cases, CMAA–AHP delivers the same rankings based on the original input. However, the new method also provides insight into each decision and would have been able to guide each group to consensus within a small number of resolution steps.

Augmented Reality in Extra-Vehicular Activities: Optimizing Alert Detection and Cognitive Workload

This study investigates the impact of augmented reality (AR) system complexity (active vs. passive) and lighting conditions (normal, dark, glare) on task performance, cognitive workload, and alert detection during simulated extravehicular activities (EVAs). Fifty-eight undergraduate students participated, using AR systems to complete tasks. Key metrics included task completion rate, completion time, alert detection, and cognitive workload assessed by the NASA Task Load Index (NASA-TLX). Results showed no significant differences in task performance or cognitive workload across lighting conditions and AR types. Notably, less than 15% of participants noticed any alerts, suggesting AR alerts may lack sufficient salience. The study’s lack of significant findings is attributed to the small sample size and potential insensitivity of measures. Future research should involve larger samples and refined methodologies to better understand AR’s impact on astronaut performance and safety during EVAs.

Assessment of cognitive workload using simultaneous EEG and fNIRS: A comparison of feature combinations

The assessment of cognitive workload is a critical component in evaluating mental activity. It is essential in psychology, especially in professions with heightened cognitive demands, such as aviation and surgery. Even marginal improvements in workload assessment can substantially enhance safety and overall performance. This study aimed to improve the accuracy of cognitive workload assessment by employing a combined EEG+fNIRS approach with a minimal number of features. The Tunable Q-factor Wavelet Transform (TQWT) was applied to signals from EEG and fNIRS to extract relevant features. TQWT was chosen for its ability to adapt to the oscillatory behavior of the signal, and the features extracted from the transform facilitated the differentiation between different signal classes. This study introduced six proposed approaches, with the first focusing on EEG signals, the second on fNIRS signals, and the subsequent three to six approaches involving combinations of these two signals. Features evaluation highlighted the pivotal role of channels related to the frontal region and sub-bands associated with alpha and theta rhythms in EEG signals. Additionally, the very low-frequency range and the primary undecomposed signal in fNIRS signals contributed significantly to cognitive workload classification. The classification results demonstrated the efficacy of combining EEG and fNIRS signals in cognitive workload detection, providing higher accuracy than single-signal approaches. Compared to other feature combination approaches, the proposed approach based on Canonical Correlation Analysis (CCA) with a linear SVM classifier achieved superior accuracy across all investigated cognitive workload levels, with accuracies of 99.6 %, 99.4 %, 98.9 %, and 97.5 % for the 0-back vs. 3-back, 0-back vs. 2-back, 2-back vs. 3-back, and 0-back vs. 2-back vs. 3-back comparisons, respectively.

THE IMPACT OF TECHNOLOGY ON INFORMATION PROCESSING AND COGNITIVE LOAD IN DISTANCE EDUCATION

The rise of distance education (DE) has expanded access to learning opportunities, but the unique characteristics of online environments can pose challenges to effective information processing and knowledge acquisition. Cognitive load theory (CLT) provides a valuable framework for understanding how the design of instructional materials and activities can impact learners' cognitive load, influencing their ability to learn and retain information. We discuss the implications of these findings for instructional design in DE, highlighting the importance of creating learner-centered, well-structured, and engaging online learning experiences that optimize cognitive resources and promote meaningful learning.

Cortical and subcortical substrates of working memory in the right hemisphere: A connectome-based lesion-symptom mapping study

Working Memory (WM) is a cognitive system whose crucial role is to temporarily hold and manipulate information. Early studies suggest that verbal WM is typically associated with left hemisphere (LH) brain regions, while the processing of visuospatial information in WM more specifically depends on the right hemisphere (RH). However, recent evidence suggests a more complex network involving both hemispheres' prefrontal and posterior parietal cortices in these processes. Unfortunately, previous lesion studies often examined only one modality (either verbal, or visuospatial) or one hemisphere, which limits the possible conclusions regarding non-lateralized hemispheric involvement. Using connectome-based lesion-symptom mapping on a large sample of patients with left (LBD) and right (RBD) focal brain damage, we examined whether gray matter damage and white matter disconnections predict deficits of WM updating in an N-back task. Patients were examined with two WM tasks that differed regarding modality (verbal, spatial) and cognitive load (1-back, 2-back). Behavioral outcomes indicated that RBD patients showed significant deficits in WM updating, regardless of task modality or load. This observation was supported by whole-brain voxel-based analysis, revealing associations between WM deficits and gray matter clusters in the RH. Specifically, damage to the right lateral frontal cortex including the brain region homologous to Broca's area was associated with verbal WM deficits, while damage to the right inferior parietal lobe and posterior temporal cortex predicted spatial WM deficits. Additionally, white matter analyses identified severely impacted tracts in the RH, predicting deficits in both verbal and spatial WM. Our findings suggest that the mental manipulation of both verbal and visuospatial information in WM updating relies on the integrity of the RH, irrespective of the specific type of information held in mind.

The effects of different interruption conditions on mental workload: an experimental study based on multimodal measurements

Interruptions in the working environment cause extra mental workload for the operators, and this phenomenon has garnered significant research attention. This study designed four interruption conditions based on the perceptual and cognitive perspectives of human information processing, using a 2(perceptual primary task and cognitive primary task)*2(perceptual interruption task and cognitive interruption task) factorial design. Multimodal measurement methods were used to evaluate mental workload in different interruption conditions. The results show that when the primary task and the interruption task are different load types, they generate a higher mental workload than the same load type. It can be attributed to the fact that perceptual tasks and cognitive tasks increase mental workload during switching. In addition, based on the multimodal index data, the prediction model of interruption recovery delay time and the classification model of interruption conditions are established, which provides a basis for rational scheduling of work and preventing mental overload.

Decoding working-memory load during n-back task performance from high channel fNIRS data

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

A Study of Key Factors Influencing the Usability of Smartphone Graphical User Interfaces for Older Adults

This study investigates the key factors influencing the usability of smartphone graphical user interfaces (GUIs) among elderly users in Shanxi Province, China. A total of 1,111 healthy individuals aged 60-75 years participated in the study. Quantitative analysis methods, including descriptive statistics, Pearson correlation, regression analysis, and mediation and moderation analysis, were employed to examine the interactions between user characteristics (age, education, experience, health status) and GUI design factors (interface layout, text and icon design, feedback mechanisms, information presentation) about usability metrics (cognitive load, interaction barriers, user experience). The analysis revealed that both user characteristics and GUI design factors significantly impact usability outcomes. Specifically, GUI design factors were found to mediate the relationship between user characteristics and usability, while user characteristics moderated the effects of GUI design on usability. These findings underscore the necessity of tailored interface design to enhance user experience and promote digital inclusion among elderly populations. Practical recommendations include the use of larger and more readable text, simplified iconography, and clear, immediate feedback to users. Such adjustments can significantly improve usability and foster greater digital inclusion. This research contributes valuable insights to both theoretical understanding and practical applications in the design of age-appropriate mobile interfaces.

STEM activities for boosting pupils’ computational thinking and reducing their cognitive load: Roles of argumentation scaffolding and mental rotation

In order to promote students’ computational thinking (CT) involving authentic complex problem-solving competence, there has been encouragement to conduct STEM activities in K-12 schools. STEM activities usually require collaborative learning. Argumentation scaffolding is one of the effective strategies for collaborative learning, and is widely used in science education but less so in the STEM curriculum. Hence, this study intended to introduce argumentation scaffolding into STEM activities while also taking into account mental rotation as a non-trivial cognitive competence affecting spatial task performance. In this study, a 2 × 2 quasi-experiment was conducted with 60 fifth graders, evenly assigned to an experimental and a control group. It was found that (1) argumentation scaffolding and mental rotation significantly boosted pupils’ CT; (2) argumentation scaffolding exerted significant effects on cognitive load; and (3) there was a significant interaction between argumentation scaffolding and mental rotation which affected CT. In sum, the argumentation scaffolding significantly improved students’ CT and reduced their cognitive load. However, mental rotation only had a substantial effect on students’ CT. This study sheds light on how to effectively integrate argumentation scaffolding into STEM activities. In tandem, mental rotation plays a crucial role in STEM activities.

Predicting cognitive load with EEG using Riemannian geometry-based features

Objective. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.Approach. Performance is evaluated by using the Minimum Distance to Riemannian Mean model trained on CL classification. The baseline performance is established using spatial covariance matrices of the signal as features. Various novel features are explored and analyzed in depth, including spatial covariance and correlation matrices computed on the EEG signal and its first-order derivative. Furthermore, each RPA step effect on the performance is investigated, and the generalization performance of RPA is compared against a few different generalization methods.Main results. Performances are greatly improved by using the spatial covariance matrix of the first-order derivative of the signal as features. Furthermore, this work highlights both the importance and efficiency of RPA for CL prediction: it achieves good generalizability with little amounts of calibration data and largely outperforms all the comparison methods.Significance. CL prediction using RPA for generalizability across subjects is an approach worth exploring further, especially for real-world applications where calibration time is limited. Furthermore, the feature exploration uncovers new, promising features that can be used and further experimented within any Riemannian geometry setting.

Easy-fix attentional focus manipulation boosts the intuitive and deliberate use of base-rate information.

In addressing human reasoning biases, "easy-fix" attentional focus interventions have shown that we can prompt reasoners to align responses with logico-mathematical principles. The current study aimed to test the impact of such interventions on both intuitive and deliberate responses on base-rate items. Using a two-response paradigm, participants provided initial intuitive responses under time constraints and cognitive load, followed by deliberate responses. During the intervention, we used attentional focus manipulations with base-rate items that aimed to redirect participants' attention toward the "logical" base-rate cue (i.e., the logical intervention) or toward the "heuristic" descriptive cue (i.e., the heuristic intervention). The results indicate that the logical intervention led to improved alignment with logico-mathematical principles in both intuitive and deliberate responses, albeit with a modest effect size. Conversely, the heuristic intervention had no discernible impact on accuracy. This indicates that our attentional focus manipulation is more effective at getting reasoners to respect rather than to override base-rates.

Effect of ketone monoester supplementation on elite operators' mountaineering training.

Special Operations Forces (SOF) often conduct operations in physiologically stressful environments such as severe heat, cold, or hypoxia, which can induce decreases in a variety of cognitive abilities. Given the promising empirical demonstration of the efficacy of exogenous ketone monoester (KME) supplementation in attenuating cognitive performance decrease during hypoxia at rest in a laboratory setting, we conducted a real-world, field experiment examining KME's efficacy during high-altitude mountaineering, an austere environment in which US SOF have conducted increasing numbers of operations over the past two decades. Specifically, 34 students and cadre at the US Army 10th Special Forces Group Special Operations Advanced Mountaineering School (SOAMS) participated in a randomized, double-blind, placebo (PLA)-controlled crossover trial (KME vs. PLA) over 2days of tactical mountain operations training. The participants ascended from 7,500 ft in altitude (basecamp) to 12,460 ft on 1 day and 13,627 ft the other day (in randomized order), while performing various training activities inducing high physical and cognitive loads over 8-12h, and consumed six doses of KME or PLA 2-3h apart throughout each training day. While KME increased blood ketone levels and decreased glucose levels, there were no clear indications that the elevated ketone level enhanced physical or cognitive performance. KME also produced a greater incidence of heartburn, nausea, and vomiting. In these elite operators, high-altitude mountaineering had a limited impact on cognitive performance, and KME supplementation did not demonstrate any benefit.

Heart rate variability as a dynamic marker of surgeons' stress during vascular surgery.

A surgeon experiences elevated stress levels when operating. Acute stress is linked to cognitive overload, worsening surgical performance. Chronic stress poses a significant risk to a surgeon's health. Identifying intraoperative stress may allow for preventative strategies that reduce surgeons' stress and subsequently improve patient outcomes. The aim of this study was to assess the feasibility of using heart rate variability as a marker of stress during vascular surgery. A total of 11 senior surgeons were evaluated performing three different vascular surgery procedures. Heart rate variability metrics (low-frequency to high-frequency ratio and standard deviation of the normal-normal interval) were determined from single-lead ECG traces at predetermined procedural performance points. State-Trait Anxiety Inventory-6, a validated stress tool, was used to assess surgeon-reported stress. Subjective reports of procedural difficulty were also collected. One-way ANOVA compared heart rate variability at key performance points with baseline. Pearson's coefficient assessed correlation between heart rate variability and subjective stress. Data were collected for six carotid endarterectomies, six open abdominal aortic aneurysm repairs, and five lower limb bypasses. Heart rate variability metrics indicating markedly greater stress were observed at key performance points across all procedures. Peaks in stress were consistent across different surgeons performing the same procedure. A significant correlation was observed between heart rate variability metrics and subjective State-Trait Anxiety Inventory-6 stress reports (r = 0.768, P =<0.001). The most difficult procedural steps reported corresponded with heart rate variability metrics displaying the greatest stress. Heart rate variability may be a viable approach to assess intraoperative stress and cognitive load during vascular surgery and could be used to evaluate whether a theatre intervention (for example timeout) could reduce stress in areas of surgical difficulty.

How do long combination vehicles perform in real traffic? A study using Naturalistic Driving Data

This paper evaluates the performance of two different types of long combination vehicles (A-double and DuoCAT) using naturalistic driving data across four scenarios: lane changes, manoeuvring through roundabouts, turning in intersections, and negotiating tight curves. Four different performance-based standards measures are used to assess the stability and tracking performance of the vehicles: rearward amplification, high-speed transient offtracking, low-speed swept path, and high-speed steady-state offtracking. Also, the steering reversal rate metric is employed to estimate the cognitive workload of the drivers in low-speed scenarios. In the majority of the identified cases of the four scenarios, both combination types have a good performance. The A-double shows slightly better stability in high-speed lane changes, while the DuoCAT has slightly better manoeuvrability at low-speed scenarios like roundabouts and intersections.