Levels Of Cognitive Load Research Articles

Evaluating Burnout in the Emergency Departments: Methods, Results, and Lessons Learned

Healthcare professionals practicing in the Emergency Department (ED) play a critical role in providing care in our societies. They interact with an assortment of sub-systems of the hospital and collaborate with various specialties across inpatient and outpatient settings. Research shows that >60% of ED physicians report burnout and, thus, it is important to understand the key factors contributing to their burnout. Over a ten-week period we used a mixed-method, theory-based, participatory, and data-driven approach based on survey, focus groups, and contextual inquiries to collect and analyze data and prioritize improvement efforts. Key areas of improvement were: i) workflows and patient monitoring that add to high cognitive load and stress levels; ii) low staffing, particularly among sitters, nurses, technicians, secretaries, and security staff; iii) suboptimal therapeutic atmosphere for psychiatric patients; iv) disrespectful and violent patients and visitors; v) communication issues, especially between the ED and other departments; and vi) frequent problems with the telecommunication system. We learned that EDs must take time to rigorously evaluate contributing factors to burnout while engaging their people who best know how to change systems to achieve positive and sustainable results.

Make it or draw it? Investigating the communicative trade-offs between sketches and prototypes

Abstract Design representations play a crucial role in facilitating communication between individuals in design. Sketches and physical prototypes are frequently used to communicate design concepts in early-stage design. However, we lack an understanding of the communicative benefits each representation provides and how these benefits relate to the effort and resources required to create each representation. A mixed-methods study was conducted with 44 participants to identify whether sketches and physical prototypes led to different levels of cognitive load perceived by a communicator and listener and the characteristics that shape their cognitive load during communication. Results showed that listeners perceived higher levels of mental and physical demands when understanding ideas as low-fidelity physical prototypes, as compared to sketches. No significant differences were found in the cognitive load levels of communicators between the two conditions. Qualitative analyses of post-task semi-structured interviews identified five themes relating to verbal explanations and visual representations that shape designers’ cognitive load when understanding and communicating ideas through design representations. Results indicate that designers should be aware of the specific objectives they seek to accomplish when selecting the design representation used to communicate. This work contributes to the knowledge base needed for designers to use design representations more effectively as tools for communication.

ADABase: A Multimodal Dataset for Cognitive Load Estimation.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Pupillometric and blink measures of diverse task loads: Implications for working memory models.

Inconsistent observations of pupillary response and blink change in response to different specific tasks raise questions regarding the relationship between eye measures, task types and working memory (WM) models. On the one hand, studies have provided mixed evidence from eye measures about tasks: pupil size has mostly been reported to increase with increasing task demand while this expected change was not observed in some studies, and blink rate has exhibited different trends in different tasks. On the other hand, a WM model has been developed to integrate a component to reconcile recent findings that the human motor system plays an important role in cognition and learning. However, how different tasks correlate with WM components has not been experimentally examined using eye activity measurements. The current study uses a four-dimensional task load framework to bridge eye measures, task types and WM models. Twenty participants (10 males, 10 females; Age: M=25.8, SD=7.17) above 18 years old volunteered. All participants had normal or corrected to normal vision with contact lenses and had no eye diseases causing obvious excessive blinking. We examined the ability of pupil size and blink rate to index low and high levels of cognitive, perceptual, physical and communicative task load. A network of the four load types and WM components was built and analysed to verify the necessity of integrating a physical task-related component into the WM model. Results demonstrate that pupil size can index cognitive load and communicative load but not perceptual or physical load. Blink rate can index the level of cognitive load but is best at discriminating perceptual tasks from other types of tasks. Furthermore, pupil size measurement of the four task types was explained better during structural and factor analysis by a WM model that integrates a movement-related component. This research provides new insights into the relationship between eye measures, task type and WM models and provides a comprehensive understanding from which to predict pupil size and blink behaviours in more complex and practical tasks.

A comprehensive decoding of cognitive load

The extent of neurophysiological activation, such as brain activities, eye movement, and skin conductance, can vary as a joint function of cognitive load. These functions are the basis of models that describe human behavior and neural mechanisms for diagnosing and treating cognitive disorders, such as Alzheimer’s disease, mild cognitive impairment, and stroke-related cognitive dysfunction. Such models can enhance our understanding of the disease processes and enable crucial applications like predicting cognitive trajectories for early diagnosis. However, despite the success of these models in predicting early-stage cognitive impairment and decline, their practical use is limited in clinics because most of the models focus on utilizing one or two factors of the neurophysiological activation to achieve prediction. Still, little is known about the mechanisms through which the task difficulty and cognitive demands affect the expression of neurophysiological activation and whether there is an expression difference under cognitive task demands. The purpose of this paper is to provide a comprehensive examination of the neurophysiological expression difference and mechanisms under various cognitive loads. We designed an experimental protocol and developed a data processing framework to explicitly examine brain activity and eye movement under various levels of cognitive task difficulty and find that (1) eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance; (2) brain activity has specific patterns related to the various levels of cognitive load and exerts its influence on predicting the dynamics of cognitive processes. These findings suggest that neuroimaging studies comparing task-related neurophysiological activation must be examined and interpreted in a holistic view of neural mechanisms. • Manipulating task difficulty imposes dynamics of cognitive demands that sensing devices can capture and learn through machine learning models. • Eye movement is a readout of cognitive processes, but it is a joint function of task difficulty, brain activity, and skin conductance. • Brain activity has specific patterns related to the various levels of cognitive load and influences predicting the dynamics of cognitive processes. • The relationship between brain activity patterns, eye movement, and dynamics of cognitive processes provides evidence for further development of the early diagnosis of cognitive disorder diseases.

Neural Correlates of Cognitive Load While Playing an Emergency Simulation Game: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Functional near-infrared spectroscopy (fNIRS) provides reliable results for determining cognitive load based on averaged cortical blood flow during multiple repetitions of short cognitive tasks. At the same time, it remains unclear how to use this technique for assessing cognitive load during prolonged single-trial activity. In this study, we used a computer-based emergency simulation game for inducing different levels of cognitive load. We propose a novel approach to measure cognitive load using specific time slots, determined based on simulation log-data interpreted in light of Barrouillet's time-based resource-sharing model. To validate this approach, we compared cortical activity in dorsolateral prefrontal cortex (DLPFC) and left inferior frontal gyrus (IFG) regions measured at four specific time slots during a simulation. We found significant associations between cognitive load and neuronal activity within the DLPFC depending on the chosen time slot, whereas no such dependencies were found for the IFG. These results illustrate how knowledge of task structure could be used advantageously for the identification of cognitive load. Although requiring further investigation in terms of reliability and generalizability, the presented approach can be considered promising evidence that fNIRS might be suitable for more general reliable assessments of cognitive load during prolonged single-trial activities and for real-time adaptations in simulation-based learning environments.

The Impact of Cognitive Load on Students’ Academic Writing: An Authorship Verification Investigation

Automatic authorship verification is known to be a challenging machine learning task. In this paper, we examine the efficacy of an enhanced common n-gram profile-based approach to assist educational institutions to validate students' essays and assignments through their writing styles. We investigated the impact that essays with different cognitive load requirements have in students' writing styles, which may or may not impact authorship verification methods. A total of 46 undergraduate students completed six essays in a laboratory study. Although results showed small and mixed effects of the tasks differing in cognitive load on the different writing product metrics, students' essays and assignments texts contained features that remained stable across essays requiring different levels of cognitive load. These results suggest that our approach could be successfully used in authorship verification, potentially helping to address issues related to academic integrity in higher education settings.

Recent experience and performance during a critical in‐flight event

AbstractThe aim of this study was to test, amongst less experienced pilots, the relationship between the recency of flight experience and performance during a critical in‐flight event. It was hypothesised that, in response to an engine failure, recent flight experience would be associated with a superior level of aircraft control, decreased cognitive load, and a successful landing at an alternate destination. Pilots completed a simulated flight during which an engine failure occurred. The weather conditions and proximity to the alternate were designed to enable a power‐off landing. The results revealed a relationship between recent flight experience and landing at the alternate, although no relationship was evident with aircraft control. Objective, rather than subjective levels of cognitive load tended to covary with landing successfully at the alternate. The outcomes provide support for the role of recent flight experience in enabling successful responses to critical in‐flight events, particularly amongst less experienced pilots.

Digital Storytelling in an Online EFL Course: Influences on Speaking, Vocabulary, and Cognitive Load

Digital Storytelling (DS) is an alternative instructional tool for language teaching to make the relevant process more engaging through the use of technology and to facilitate effective language learning. This single case study explored the impact of DS in an online EFL lesson on speaking and vocabulary achievement in consideration to cognitive load effect. The study utilized mixed-design analysis strategies as quantitative tests (pre-and post-vocabulary test, speaking evaluation rubric, cognitive load scale) with 33 Turkish EFL learners studying at tertiary level who participated in an online English course and interviews with 21 volunteers. Quantitative findings revealed increased vocabulary achievement after DS use and improved English-speaking skills in terms of spoken grammar, vocabulary use, fluency and coherence, and pronunciation after repeated DS use in three stories, despite high level of cognitive load due to technical incompetency of the participants. In addition, the results obtained from the qualitative data match with the results of the quantitative data in terms of positive evaluations on the impact of DS for vocabulary use, error correction, sentence formation, pronunciation, and self-study. DS can also be effectively used in online EFL education by eliminating the drawbacks of students’ digital incompetency at tertiary level. The results of this study are expected to shed light on the practitioners aiming to use DS in online language learning for teaching speaking and improving vocabulary achievement.

Media multitasking, counterarguing, and brand attitude: Testing the mediation effects of advertising attention and cognitive load

Inhibiting counterarguing is a critical approach to persuasion. Multitasking effects have been closely associated with the idea of reducing counterarguing vis cognitive overload. Those cognitively overloaded are less likely to counterargue, and persuasion is more likely to occur. Direct evidence of the occurrence of cognitive overload is, however, limited. This study tested cognitive overload as the explanation. This study also tested an alternative explanation: media multitasking reduces counterarguing and enhances persuasion by lowering attention. A one-factor (single medium and multitasking) between-subjects experiment was conducted. The results show that multitaskers and single-medium users had a similar level of cognitive load. Multitaskers paid less attention to the ads, which inhibited counterarguing and enhanced subsequent brand attitude compared to single-medium users. This study initiated the effort to examine the underlying mechanism of the counterarguing inhibition hypothesis, but the results failed to support it. The results supported the proposed lower advertising attention explanation based on the Elaboration Likelihood Model (ELM) and intentional exposure theory.

Direct instruction, worked examples and problem solving: The impact of instructional strategies on cognitive load

ABSTRACT Research often examines cognitive load as it relates to direct instruction, worked examples and problem-solving combined as an integrated whole. The present study examines these strategies in isolation to see their effect on cognitive load. Using learning materials covering the basics of critical thinking to undergraduate law students (n = 160) at a Russian university, the current study isolated direct ‘example-free’ instruction, worked examples with no instructional explanations and problem-solving free from any form of instruction to examine their effects on cognitive load. Results show that students’ levels of cognitive load differ by condition. Clearly separating instructional strategies in this manner allows for the examination of how learners process information at a particular phase of instruction, and ultimately a more precise and accurate explanation of how specific instructional strategies contribute to cognitive load.

Neural resources shift under Methylphenidate: A computational approach to examine anxiety-cognition interplay

The reciprocal interplay between anxiety and cognition is well documented. Anxiety negatively impacts cognition, while cognitive engagement can down-regulate anxiety. The brain mechanisms and dynamics underlying such interplay are not fully understood. To study this question, we experimentally and orthogonally manipulated anxiety (using a threat of shock paradigm) and cognition (using methylphenidate; MPH). The effects of these manipulations on the brain and behavior were evaluated in 50 healthy participants (25 MPH, 25 placebo), using an n-back working memory fMRI task (with low and high load conditions). Behaviorally, improved response accuracy was observed as a main effect of the drug across all conditions. We employed two approaches to understand the neural mechanisms underlying MPH-based cognitive enhancement in safe and threat conditions. First, we performed a hypothesis-driven computational analysis using a mathematical framework to examine how MPH putatively affects cognitive enhancement in the face of induced anxiety across two levels of cognitive load. Second, we performed an exploratory data analysis using Topological Data Analysis (TDA)-based Mapper to examine changes in spatiotemporal brain activity across the entire cortex. Both approaches provided converging evidence that MPH facilitated greater differential engagement of neural resources (brain activity) across low and high working memory load conditions. Furthermore, load-based differential management of neural resources reflects enhanced efficiency that is most powerful during higher load and induced anxiety conditions. Overall, our results provide novel insights regarding brain mechanisms that facilitate cognitive enhancement under MPH and, in future research, may be used to help mitigate anxiety-related cognitive underperformance.

Types of Major League Baseball Broadcast Information and Their Impacts on Audience Experience

Baseball is a sport that involves a large number of statistics, which are often displayed during broadcast events to show the players’ performance levels. With the advent of big data, the amount and types of data used in broadcasts have increased yearly. However, the use of complex information challenges the audience’s ability to process it. This study considered data types used during broadcasts as the basis for an in-depth exploration of audiences’ experience resulting from the application of visualization. The study also examined the relationship between the contents of broadcast information and audiences’ sports participation, entertainment experience, and cognitive load. Baseball fans with varying levels of experience with handling different types of information were surveyed to understand the variations in their entertainment experiences and cognitive load levels when they watched a baseball game. The results indicated that fans with low participation levels had insufficient viewing experience, such that the use of visualized statistical information did not facilitate their understanding of the game, nor did they gain more pleasure or meaning from the game through the visualized information. Fans with high participation levels already possessed a wealth of baseball knowledge and experience, so providing visualized information did not significantly elevate their viewing experiences either. Moreover, the visualized information caused them to experience varying amounts of additional cognitive load. These results provide a reference that can be used to design sports broadcasts tailored to different information types and fan characteristics, thus improving fans’ viewing experience of sports broadcasts.

Studying interpreters’ stress in crisis communication: evidence from multimodal technology of eye-tracking, heart rate and galvanic skin response

ABSTRACT This article uses risk communication theory and cognitive load theory to analyse the stress experienced by interpreters involved in crisis communication within Covid-19 medical scenarios. It considers the nature of stress both from psychological (mental) and physiological perspectives, exploring the relationship between the level of cognitive load, interpreters’ stress, and the quality of interpreting in crisis communication. This research identifies the strategies used by interpreters when operating in pandemic working environments and compares their cognitive load and physiological stress changes within and outside contexts of crisis communication. We hypothesize that interpreters experience greater psychological stress and an increased cognitive load which adversely affect their interpreting in crises compared to normal situations. To test this hypothesis, an experiment combined eye-tracking technology with Heart Rate and Galvanic Skin Response technology. 25 novice interpreters interpreted simulated medical scenarios for a Covid-19 patient and a diabetes patient respectively. This is one of the first studies to apply the multimodal approach of eye-tracking, HR, and GSR technology to record the physiological stress and mental status of interpreters. We advocate more systematic interdisciplinary research concerning interpreters’ stress in crisis communication, and outline recommendations for future crisis interpreting training and for individual professionals involved in crisis management.

Classification of Drivers' Mental Workload Levels: Comparison of Machine Learning Methods Based on ECG and Infrared Thermal Signals.

Mental workload (MW) represents the amount of brain resources required to perform concurrent tasks. The evaluation of MW is of paramount importance for Advanced Driver-Assistance Systems, given its correlation with traffic accidents risk. In the present research, two cognitive tests (Digit Span Test—DST and Ray Auditory Verbal Learning Test—RAVLT) were administered to participants while driving in a simulated environment. The tests were chosen to investigate the drivers’ response to predefined levels of cognitive load to categorize the classes of MW. Infrared (IR) thermal imaging concurrently with heart rate variability (HRV) were used to obtain features related to the psychophysiology of the subjects, in order to feed machine learning (ML) classifiers. Six categories of models have been compared basing on unimodal IR/unimodal HRV/multimodal IR + HRV features. The best classifier performances were reached by the multimodal IR + HRV features-based classifiers (DST: accuracy = 73.1%, sensitivity = 0.71, specificity = 0.69; RAVLT: accuracy = 75.0%, average sensitivity = 0.75, average specificity = 0.87). The unimodal IR features based classifiers revealed high performances as well (DST: accuracy = 73.1%, sensitivity = 0.73, specificity = 0.73; RAVLT: accuracy = 71.1%, average sensitivity = 0.71, average specificity = 0.85). These results demonstrated the possibility to assess drivers’ MW levels with high accuracy, also using a completely non-contact and non-invasive technique alone, representing a key advancement with respect to the state of the art in traffic accident prevention.

Asymmetric Influence of Dual-Task Interference on Anticipatory Postural Adjustments in One-Leg Stance.

This study investigated the differences of anticipatory postural adjustments (APAs) in a one-leg stance (OLS) that appear according to lower-extremity dominance and dual-task interference. Thirteen young, healthy, male volunteers performed the OLS task under the following six conditions: (1) dominant leg (DL), single-task; (2) DL, dual-task, with a low level of cognitive load (DT1/2); (3) DL, dual-task, with a high level of cognitive load (DT + 1); (4) non-dominant leg (NDL), single-task; (5) NDL, DT1/2; and (6) NDL, DT + 1. In order to measure the subjects’ APA, we used the medial–lateral displacement of their centers of pressure and gravity from the force plate and the time-series data of joint angular motions, recorded using a 3D motion analysis system. In the NDL under the dual-task condition, the onset of APA was delayed and the amplitude declined, which resulted in an increase in the duration of the APA period. The number of components identified by principal component analysis differed according to the dominant foot, and the change caused by cognitive load was found only in the NDL. As the cognitive load increased, the variance of the principal component decreased. These findings show that dual-task interference asymmetrically influences APA according to limb dominance, which reorganizes the coordination strategy of joints’ angular motion.

Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine

ABSTRACT Cognitive load level identification is an interesting challenge in the field of brain-computer-interface. The sole objective of this work is to classify different cognitive load levels from multichannel electroencephalogram (EEG) which is computationally though-provoking task. This proposed work utilized discrete wavelet transform (DWT) to decompose the EEG signal for extracting the non-stationary features of task-wise EEG signals. Furthermore, a support vector machine (SVM) implemented to classify the task from the DWT-based extracted features. . The proposed methodology has been implemented on a renowned EEG dataset that captured three levels of cognitive load from the n-back test. In this work, two different approaches: i) Low vs High cognitive load (0-back vs [2-back+3-back]) and ii) Low vs Medium vs High (0-back vs 2-back vs 3-back) are investigated for the performance measurement. The linear SVM achieved the highest average classification accuracy that is 77.20 ± 6.63 and 87.89 ± 7.3 for 3-class and 2-class approaches, respectively.

Assessment of instantaneous cognitive load imposed by educational multimedia using electroencephalography signals.

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.

Influence of Speech and Cognitive Load on Balance and Timed up and Go.

The interaction between oral and/or mental cognitive tasks and postural control and mobility remains unclear. The aim of this study was to analyse the influence of speech production and cognitive load levels on static balance and timed up and go (TUG) during dual-task activities. Thirty healthy young subjects (25 ± 4 years old, 17 women) participated in this study. A control situation and two different cognitive arithmetic tasks were tested: counting backward in increments of 3 and 7 under oral (O) and mental (M) conditions during static balance and the TUG. We evaluated the dual-task cost (DTC) and the effect of speech production (SP) and the level of cognitive load (CL) on these variables. There was a significant increase in the centre of pressure oscillation velocity in static balance when the dual task was performed orally compared to the control situation The DTC was more pronounced for the O than for the M. The SP, but not the CL, had a significant effect on oscillation velocity. There was an increase in TUG associated with the cognitive load, but the mental or oral aspect did not seem to have an influence. Mobility is more affected by SP when the cognitive task is complex. This may be particularly important for the choice of the test and understanding postural control disorders.

Cognitive workload classification: Towards generalization through innovative pipeline interface using HMM

Cognition is an important aspect to realize one’s potential and accelerate progress towards achieving a better quality of life. The cognitive workload is characterized by the occupancy of working memory during task performance. In spite of low spatial resolution, Electroencephalogram (EEG) continues to be a substantial tool in cognitive load research. The current methods use various time and frequency domain features for the assessment of cognitive load levels using EEG. The present work intends to model the temporal dynamics of the EEG signals by proposing a pipeline interface to classify cognitive workload under a continuous engagement/attention environment. The short segments of decomposed data are assumed to be stationary and are modeled using the autoregressive method. Later, fuzzy c-means maps the AR coefficients to low dimensional space. These clustered items are scaled further in the temporal domain by finding the interplay between the successive items through the most likely sequence of hidden states using the hidden Markov model (HMM). The successive segments group, having similar HMM states form variable-length frames. The handcrafted feature set from variable-length frames is classified using a deep recurrent neural network (RNN) structure. Finally, the maximum voting scheme on the predicted RNN output enhances the classification accuracy. The performance of this method is evaluated on a publicly available, and self-collected dataset. The findings reveal that handcrafted features supported by modeling of temporal dynamics outperform state-of-the-art methods. As a result, 97.8% accuracy is observed for multi-class classification with an optimum of four electrodes.