Lower Cognitive Workload Research Articles

Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.

A Dual-Adversarial Model for Cross-time and Cross-subject Cognitive Workload Decoding.

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.

Analisis Beban Kerja Kognitif Siswa Sekolah Menengah Pertama pada Tugas Aritmetika Mental

This study aims to describe the cognitive workload of students on mental arithmetic tasks. This study is a qualitative descriptive research. Data collection was conducted on students of Class VIII-C SMPN 1 Situbondo consisting of 32 students and taken 10 students as research subjects. Data collection techniques of this study were mental arithmetic tasks, NASA-TLX questionnaires, and interviews. The data collection instruments used were mental arithmetic task questions, NASA-TLX questionnaire rating sheets, and interview guidelines. 10 research subjects were selected based on the cognitive workload and mental arithmetic of students. The results of this study showed that 6.25% were in the category of very low cognitive workload, 12.5% were in the category of low cognitive workload, 25% were in the category of moderate cognitive workload, 43.75% were in the category of high cognitive workload, and 12.5% were in the category of very high cognitive workload. Subjects with very low cognitive workload did not experience stress or difficulty when solving mental arithmetic tasks. Subjects with low cognitive workload did not experience stress or difficulty when solving mental arithmetic tasks. Subjects with moderate cognitive workload experience slight difficulties when solving mental arithmetic tasks. Subjects with a high cognitive workload experience stress to the point of making the subject feel irritated when solving mental arithmetic tasks. Subjects with very high cognitive workload experienced stress to the point of making the subject feel irritated when solving mental arithmetic tasks.

Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight

Pilots of aircraft face varying degrees of cognitive workload even during normal flight operations. Periods of low cognitive workload may be followed by periods of high cognitive workload and vice versa. During such changing demands, there exists potential for increased error on behalf of the pilots due to periods of boredom or excessive cognitive task demand. To further understand cognitive workload in aviation, the present study involved collection of electroencephalogram (EEG) data from ten (10) collegiate aviation students in a live-flight environment in a single-engine aircraft. Each pilot possessed a Federal Aviation Administration (FAA) commercial pilot certificate and either FAA class I or class II medical certificate. Each pilot flew a standardized flight profile representing an average instrument flight training sequence. For data analysis, we used four main sub-bands of the recorded EEG signals: delta, theta, alpha, and beta. Power spectral density (PSD) and log energy entropy of each sub-band across 20 electrodes were computed and subjected to two feature selection algorithms (recursive feature elimination (RFE) and lasso cross-validation (LassoCV), and a stacking ensemble machine learning algorithm composed of support vector machine, random forest, and logistic regression. Also, hyperparameter optimization and tenfold cross-validation were used to improve the model performance, reliability, and generalization. The feature selection step resulted in 15 features that can be considered an indicator of pilots' cognitive workload states. Then these features were applied to the stacking ensemble algorithm, and the highest results were achieved using the selected features by the RFE algorithm with an accuracy of 91.67% (± 0.11), a precision of 93.89% (± 0.09), recall of 91.67% (± 0.11), F-score of 91.22% (± 0.12), and the mean ROC-AUC of 0.93 (± 0.06). The achieved results indicated that the combination of PSD and log energy entropy, along with well-designed machine learning algorithms, suggest the potential for the use of EEG to discriminate periods of the low, medium, and high workload to augment aircraft system design, including flight automation features to improve aviation safety.

The Moderating Effects of Task Complexity and Age on the Relationship between Automation Use and Cognitive Workload

In recent decades, automation has become increasingly integrated into knowledge work environments, i.e., jobs that require specialized skills for task completion. Some research has shown that automation may reduce cognitive workload. However, other work reports that increasing automation does not always lower cognitive workload. These conflicting results suggest that further investigation is needed to identify factors that influence the relationship between automation use and cognitive workload. Therefore, this study aimed to investigate how two potential moderators, task complexity and age, influence that relationship. A total of 24 younger and 24 middle-aged adults performed an object identification task that resembled a baggage scanning procedure conducted by airport security personnel. The task was manipulated by varying the level of automation (4 increasing levels) that provided support in identifying objects of interest as well as the complexity of task (two types: component and coordinative). In addition, participants completed a pattern identification and sorting memory task, which helped emulate a knowledge work environment. Performance (i.e., object identification accuracy, average completion time, pattern identification accuracy, and sorting memory task accuracy) and subjective workload (NASA-RTLX score) measures were recorded. Participants also rated the usability of the automation. Overall, results showed that while the use of automation was associated with reduced cognitive workload, both types of task complexity negatively affected this relationship such that increased complexity was associated with a decrease in accuracy. Age did not moderate the relationship between automation and cognitive workload, but there were qualitative differences in terms of how the two age groups perceived the utility and usability of the automated systems. Knowledge generated from this research has implications for the design of future human-automation systems and can be used to inform interface design that is tailored to the needs and preferences of different users and use cases.

Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context

A critical challenge in human-autonomy teaming is for human players to comprehend their nonhuman teammates (agents). Transparency in agents' behaviors is the key for such comprehension, which may be obtained by embedding a self-explanation ability into the agent to explain its own behaviors. Previous studies have relied on searching for the executed functions and logics to generate explanations for behaviors of goal-following logic-based agents. With the increasing number of functions and logics, current methods, such as component and process-based methods, have become impractical. This article proposes a new method exploiting the agent's artificial situation awareness states for generating explanations that involves several techniques: A Bayesian network, fuzzy theory, and Hamming distance. Our new method is evaluated in a collaborative driving context, in which a significant number of accidents recently occurred around the globe due to the lack of understanding of the autopilot agents. Using an autonomous driving simulator called Carla, two typical scenarios in collaborative driving, namely, traffic light and overtaking situations, are used. The findings show that the new method potentially reduces the search space in generating explanations and exhibits better computational performance and a lower cognitive workload. This work is important to calibrate human trust and to enhance comprehension of the agent.

Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.

Intuitive, Efficient and Ergonomic Tele-Nursing Robot Interfaces: Design Evaluation and Evolution

Tele-nursing robots provide a safe approach for patient-caring in quarantine areas. For effective nurse–robot collaboration, ergonomic teleoperation and intuitive interfaces with low physical and cognitive workload must be developed. We propose a framework to evaluate the control interfaces to iteratively develop an intuitive, efficient, and ergonomic teleoperation interface. The framework is a hierarchical procedure that incorporates general to specific assessment and its role in design evolution. We first present pre-defined objective and subjective metrics used to evaluate three representative contemporary teleoperation interfaces. The results indicate that teleoperation via human motion mapping outperforms the gamepad and stylus interfaces. The tradeoff with using motion mapping as a teleoperation interface is the non-trivial physical fatigue. To understand the impact of heavy physical demand during motion mapping teleoperation, we propose an objective assessment of physical workload in teleoperation using electromyography. We find that physical fatigue happens in the actions that involve precise manipulation and steady posture maintenance. We further implemented teleoperation assistance in the form of shared autonomy to eliminate the fatigue-causing component in robot teleoperation via motion mapping. The experimental results show that the autonomous feature effectively reduces the physical effort while improving the efficiency and accuracy of the teleoperation interface.

COLET: A dataset for COgnitive workLoad estimation based on eye-tracking

Background and Objective: The cognitive workload is an important component in performance psychology, ergonomics, and human factors. Publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad estimation based on Eye-Tracking dataset is presented. Methods: Forty-seven (47) individuals’ eye movements were monitored as they solved puzzles involving visual search activities of varying complexity and duration. The participants’ cognitive workload level was evaluated with the subjective test of NASA-TLX and this score is used as an annotation of the activity. Extensive data analysis was performed in order to derive eye and gaze features from low-level eye recorded metrics, and a range of machine learning models were evaluated and tested regarding the estimation of the cognitive workload level. Results: The activities induced four different levels of cognitive workload. Multi tasking and time pressure have induced a higher level of cognitive workload than the one induced by single tasking and absence of time pressure. Multi tasking had a significant effect on 17 eye features while time pressure had a significant effect on 7 eye features. Both binary and multi-class identification attempts were performed by testing a variety of well-known classifiers, resulting in encouraging results towards cognitive workload levels estimation, with up to 88% correct predictions between low and high cognitive workload. Conclusions: Machine learning analysis demonstrated potential in discriminating cognitive workload levels using only eye-tracking characteristics. The proposed dataset includes a much higher sample size and a wider spectrum of eye and gaze metrics than other similar datasets, allowing for the examination of their relations with various cognitive states.

Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload.

Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators. Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators' cognitive workloads. Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. Lower cognitive workload was found when applying the algorithm's protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. The algorithm was positively evaluated for its ability to reduce operators' cognitive workloads. More evaluations of operators' SA are required. The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3–5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.

MBioTracker: Multimodal Self-Aware Bio-Monitoring Wearable System for Online Workload Detection.

Cognitive workload affects operators' performance principally in high-risk or time-demanding situations and when multitasking is required. An online cognitive workload monitoring system can provide valuable inputs to decision-making instances, such as the operator's state of mind and resulting performance. Therefore, it can allow potential adaptive support to the operator. This work presents a new design of a wearable embedded system for online cognitive workload monitoring. This new wearable system consists of, on the hardware side, a multi-channel physiological signals acquisition (respiration cycles, heart rate, skin temperature, and pulse waveform) and a low-power processing platform. Further, on the software side, our wearable embedded system includes a novel energy-aware bio-signal processing algorithm. We also use the concept of application self-awareness to enable energy-scalable embedded machine learning algorithms and methods for online subjects' cognitive workload monitoring. Our results show that this new wearable system can continuously monitor multiple bio-signals, compute their key features, and provide reliable detection of high and low cognitive workload levels with a time resolution of 1 minute and a battery lifetime of 14.58 h in our experimental conditions. It achieves a detection accuracy of 76.6% (2.6% lower than analogous offline computer-based analysis) with a sensitivity of 77.04% and a specificity of 81.75%, on a simulated drone rescue mission task. Moreover, by applying our self-aware monitoring to exploit different energy-scalable modes, we can increase battery lifetime by 51.6% (up to 22.11 hours) while incurring an insignificant accuracy loss of 1.07%.

Prevalence of Surgical Flow Disruptions Across Intra-operative High- and Low-Workload Phases in Cardiac Surgery

While the influence of surgical flow disruptions (SFDs) on surgical performance has been observed in live cardiovascular surgery and their influence on cognitive workload has been evaluated in high-fidelity simulations, the relationship between SFDs and cognitive workload of surgical providers in the live cardiac surgery operating room has yet to be explored. Recent evidence suggests that preventable surgical errors often have a cognitive basis, warranting investigation into the association between SFDs and workload. This study aimed to characterize SFDs according to auditory and cognitive domains and further to compare the frequency and nature of SFDs within periods of high team cognitive workload and low team cognitive workload. Overall, the presence of cognitive distractions was associated more with high team cognitive workload states, while the frequency of auditory distractions was significantly higher during periods of low team cognitive workload states. Future work should consider the types, frequency, and sources of SFDs as well as their impact on surgical procedures and outcomes in order to devise appropriate methods to mitigate or manage potential disruptions to surgical workflow.

Detecting mild cognitive impairment by quantifying oculometric features during variable cognitive workload: The novel rapid Oculo‐Cognitive Addition Test

AbstractBackgroundThe need exists for a device that can rapidly and accurately assess mild cognitive impairment (MCI). Because the link between oculometrics and cognition is well‐documented, the Oculo‐Cognitive Addition Test (OCAT) was developed which tracks users’ eye movements as they complete a simple mental addition test.MethodOCAT consisted of 12 trials of summing three consecutive single‐digit numbers, shown separately on three consecutive screens (Figure 1). The first, second, and third screen in each trial corresponded to low, medium, and high cognitive workload. Using an infinity‐loop pattern of 24 symmetrical positions, eye movements were captured systematically in horizontal, vertical, and diagonal directions (Figure 2).MCI was simulated by exposing subjects to 5 minutes of hypoxic gas mixture (8% O2 + balance N2). 20 subjects completed OCAT in baseline (i.e. normal room air), hypoxic, and post‐baseline (i.e. 20 minutes of room air) conditions. Raw scan‐paths were collected with an eye‐tracking device. Oculometrics pertaining to fixations and saccades were extracted. Statistical significance was assessed using repeated measures ANOVA. Effect size and clinical significance was evaluated using computed Cohen’s d.ResultOCAT completion time significantly increased from baseline to hypoxia, which corresponded to a significant decrease in cerebral oxygenation from baseline to hypoxia (Figure 3). From baseline to hypoxia, saccadic latency significantly increased while saccadic velocity significantly decreased in the diagonal direction, however, results in the horizontal and vertical directions were not significant (Figure 4). Fixation time significantly increased from baseline to hypoxia in medium and high cognitive workload, but did not significantly change in low cognitive workload (Figure 5). From baseline to hypoxia, fixation size did not significantly change in any cognitive workload, however, the fluctuation in fixation size significantly increased in medium cognitive workload. From baseline to hypoxia, fixation area significantly increased in low and high cognitive workload, but did not significantly change in medium cognitive workload.ConclusionOCAT can detect subtle changes in oculometrics that correlate with MCI. This study provides rationale for providing varying degrees of cognitive load to elucidate subtle changes in oculometrics that may otherwise go undetected in neurocognitive tests that do not increase cognitive load.

Association of Health Record Visualizations With Physicians’ Cognitive Load When Prioritizing Hospitalized Patients

Current electronic health records (EHRs) contribute to increased physician cognitive workload when completing clinical tasks. To assess the association of different design features of an EHR-based information visualization tool with the cognitive load of physicians during the clinical prioritization process. This cross-sectional study included a convenience sample of 29 attending physicians at Seattle Children's Hospital, a large tertiary academic pediatric hospital. Data collection took place from August 2017 through October 2017, and analysis occurred from August to October 2018. Physician participants used 3 prototypes with novel visualizations of simulated EHR data that highlighted 1 of 3 key patient characteristics, as follows: (1) acuity, (2) clinical problem list, and (3) clinical change. Cognitive workload was measured using the NASA Task Load Index (TLX) scale (range, 1-100, with lower scores indicating lower cognitive workload). Cognitive workload was assessed for the 2 following clinical prioritization tasks: (1) finding information for a specific patient and (2) comparing results among patients for each prototype. Participants ranked 5 hypothetical patients from having the highest to the lowest priority in each design. A total of 29 physician participants (15 [52%] men; 14 [48%] women; mean [range] age, 43 [35-58] years; mean [range] time in practice, 11 [3-30] years) completed the study. For task 1, the prototype highlighting clinical change was associated with lower median (interquartile range) NASA TLX scores compared with the prototype highlighting acuity (30.3 [15.2-41.6] vs 48.5 [18.7-59.3]; P = .02). For task 2, the prototype highlighting clinical change was associated with lower median (interquartile range) NASA TLX scores compared with the prototype highlighting the clinical problem list (29.1 [16.3-50.8] vs 43.5 [26.6-55.9]; P = .02). The prototype highlighting clinical change had the lowest TLX score in 17 of 29 rankings (59%) for task 1 (χ24 = 24.4; P < .001) and 18 of 29 rankings (62%) for task 2 (χ24 = 17.2; P = .002). In this study, well-designed EHR-based information visualizations that highlighted and featured clinically meaningful information patterns significantly reduced physician cognitive workload when prioritizing patient needs.

Cognitive load and emotional processing in psoriasis: a thermal imaging study.

Psoriasis is a chronic dermatologic disease which is frequently associated with psychological distress. Although studies suggest a relationship between this condition and difficulties in emotion regulation, behavioral and physiological evidence about this link is scarce. We measured implicit emotion regulation abilities of psoriasis patients and a healthy control group by examining the impact of distracting emotional (positive, negative or neutral) images on a working memory task ("Emotional N-Back") which could present high (2-back) or low (1-back) cognitive workload. Moreover, we used Functional Infrared Thermal Imaging to record participants' facial temperature and obtain a measure of the activation of the autonomic system. Rising of temperature over the peri-orbital areas and the nose tip are believed to reflect the activation and the de-activation of the sympathetic system, respectively. Patients scored higher than controls on the "Lack of emotional clarity" sub-scale of the Difficulties in Emotion Regulation Scale. Compared to controls, who performed much better in the low vs. high cognitive load condition, patients showed a smaller accuracy difference between the two conditions. Moreover, patients showed less sympathetic (lower peri-orbital and higher nasal tip temperature) activity (especially in the negative and neutral blocks) during the high vs. low cognitive load condition, suggesting that the former condition might be less emotionally demanding for them. Patients benefit more than controls from the load-dependent interference effect when dealing with emotional information; thus, therapeutic techniques aiming at teaching how to use cognitive strategies to downregulate emotions might be particularly appropriated for them.

Monitoring Cognitive Workload Using Vocal Tract and Voice Source Features

Monitoring cognitive workload from speech signals has received much attention from researchers in the past few years as it has the potential to improve performance and fidelity in human decision making. The bulk of the research has focused on classifying speech from talkers participating in cognitive workload experiments using simple reading tasks, memory span tests and the Stroop test, typically into three levels of low, medium and high cognitive workload. This study focuses on using parameters extracted from the vocal tract and the voice source components of the speech signal for cognitive workload monitoring. The experiment used in this study contains 98 participants, the levels were obtained by using a reading task and three Stroop tasks which were randomly ordered for each participant and an adequate rest time was used inbetween tasks to mitigate the effect of cognitive workload from one task affecting the subsequent one. Vocal tract features were obtained from the first three formants and voice source features were extracted using signal analysis on the inverse filtered speech signal. The results show that on their own, the vocal tract features outperform the voice source features. The MCR of 33.92% ± 1.05 was achieved with a SVM classifier. A weighted combination of vocal tract and voice source features classified with SWM classifier fused at the output level achieved the lowest MCR of 32.5%.

Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).

Increasing Robot Saliency in Cluttered Visual Displays

It has been shown that modulating the saliency of a dense amount of information presented as icons on a map-based interface can reduce cognitive workload and improve user performance. Further, first response teams, particularly those responding to complex events, such as Chemical, Biological, Radiological, Nuclear and Explosive device incidents will incorporate robots into their future teams to assist human team members and collect additional information. The deployment of such robots will require a human team member to supervise and task the various robots associated with the team. As the complexity of an incident increases and the number of responders with different specialties increases, for example Police, Emergency Medical Services, and Hazardous Materials, it will be harder to track the robots associated with a particular team, especially by the human team member responsible for the robots. A new algorithm, the Robot Visualization Algorithm, was developed to improve the saliency of robots for which the human team operator (e.g., Emergency Medical Services) is responsible, while generally minimizing the saliency of the robots from other teams (e.g., Police and Hazardous Materials) that are not relevant to the team operator. The presented Robot Visualization Algorithm makes the other teams’ robots more salient if their activities will impact the operator’s team. The within-subjects evaluation determined that the Robot Visualization Algorithm allowed operators to have a better awareness and lower cognitive workload than a base visualization condition. A number of proposed algorithm refinements are also discussed.

EYE TRACKING TO EXPLORE THE IMPACTS OF PHOTOREALISTIC 3D REPRESENTATIONS IN PEDSTRIAN NAVIGATION PERFORMANCE

Despite the now-ubiquitous two-dimensional (2D) maps, photorealistic three-dimensional (3D) representations of cities (e.g., Google Earth) have gained much attention by scientists and public users as another option. However, there is no consistent evidence on the influences of 3D photorealism on pedestrian navigation. Whether 3D photorealism can communicate cartographic information for navigation with higher effectiveness and efficiency and lower cognitive workload compared to the traditional symbolic 2D maps remains unknown. This study aims to explore whether the photorealistic 3D representation can facilitate processes of map reading and navigation in digital environments using a lab-based eye tracking approach. Here we show the differences of symbolic 2D maps versus photorealistic 3D representations depending on users’ eye-movement and navigation behaviour data. We found that the participants using the 3D representation were less effective, less efficient and were required higher cognitive workload than using the 2D map for map reading. However, participants using the 3D representation performed more efficiently in self-localization and orientation at the complex decision points. The empirical results can be helpful to improve the usability of pedestrian navigation maps in future designs.