Cognitive workload classification of law enforcement officers using physiological responses

Motor vehicle crashes (MVCs) are a leading cause of death for law enforcement officers (LEOs) in the U.S. LEOs and more specifically novice LEOs (nLEOs) are susceptible to high cognitive workload while driving which can lead to fatal MVCs. The objective of this study was to develop a machine learning algorithm (MLA) that can estimate cognitive workload of LEOs while performing secondary tasks in a patrol vehicle. A ride-along study was conducted with 24 nLEOs. Participants performed their normal patrol operations while their physiological responses such as heartrate, eye movement, and galvanic skin response were recorded using unobtrusive devices. Findings suggested that the random forest algorithm could predict cognitive workload with relatively high accuracy (>70%) given that it was entirely reliant on physiological signals. The developed MLA can be used to develop adaptive in-vehicle technology based on real-time estimation of cognitive workload, which can reduce the risk of MVCs in police operations.

Measuring aviator workload using EEG: an individualized approach to workload manipulation

Introduction

Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience.

Methods

Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance.

Results

Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload.

Discussion

The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.

Mental Workload Classification and Tasks Detection in Multitasking: Deep Learning Insights from EEG Study

While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual's effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load levels. We designed a convolutional neural network (CNN) to help with two distinct classification tasks. In one setting, the CNN was used to classify EEG segments based on their task load level. In another setting, the same CNN architecture was trained again to detect the presence of individual MATB-II subtasks. Results show that, while the model successfully learns to detect whether a particular subtask is active in a given segment (i.e., to differentiate between different subtasks-related EEG patterns), it struggles to differentiate between the two highest levels of task load (i.e., to distinguish MWL-related EEG patterns). We speculate that the challenge comes from two factors: first, the experiment was designed in a way that these two highest levels differed only in the quantity of work within a given timeframe; and second, the participants' effective adaptation to increased task demands, as evidenced by low error rates. Consequently, this indicates that under such conditions in multitasking, EEG may not reflect distinct enough patterns to differentiate higher levels of task load.

Exploring the Effects of Interruptions in Different Phases of Complex Decision-Making Tasks

OBJECTIVE

The study aims to examine the effects of interruptions in major phases (i.e., problem-identification, alternative-development, and evaluation-and-selection) of complex decision-making tasks.

BACKGROUND

The ability to make complex decisions is of increasing importance in workplaces. Complex decision-making involves a multistage process and is likely to be interrupted, given the ubiquitous prevalence of interruptions in workplaces today.

METHOD

Sixty participants were recruited for the experiment to complete a procurement task, which required them to define goals, search for alternatives, and consider multiple attributes of alternatives to make decisions. Participants in the three experimental conditions were interrupted to respond to messages during one of these three phases, whereas participants in the control condition were not interrupted. The impacts of interruptions on performance, mental workload, and emotional states were measured through a combination of behavioral, physiological, and subjective evaluations.

RESULTS

Only participants who were interrupted in the evaluation-and-selection phase exhibited poorer task performance, despite their positive feelings toward interruptions and confidence. Participants who were interrupted in the problem-identification phase reported higher mental workload and more negative perceptions toward interruptions. Interruptions in the alternative-development phase led to more temporal changes in arousal and valence than interruptions in other phases.

CONCLUSION

Interruptions during the evaluation-and-selection phase undermine overall performance, and there is a discrepancy between behavioral outcomes and subjective perceptions of interruption effects.

APPLICATION

Interruptions should be avoided in the evaluation-and-selection phase in complex decision-making. This phase information can be either provided by users or inferred from coarse-grained interaction activities with decision-making information systems.

Aviation and neurophysiology: A systematic review

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

The brain under cognitive workload: Neural networks underlying multitasking performance in the multi-attribute task battery

Multitasking is a common requirement in many occupations. Considerable research has demonstrated that performance declines as a result of multitasking, and that it engages multiple brain regions. Despite growing evidence suggesting that brain regions operate as networks, minimal research has investigated the cognitive brain networks implicated in multitasking. The Multi-Attribute Task Battery II (MATB) is a common method for assessing multitasking ability that simulates a pilot's operational environment inside an aircraft cockpit. The aim of the present study was to examine multitasking performance on the MATB, and the associated neural patterns underlying performance with functional magnetic resonance imaging (fMRI). Twenty-four participants completed the MATB in the fMRI scanner. Participants completed four runs of the MATB in a 2 (Task: multitasking vs. single tasking) × 2 (Difficulty: hard vs. easy) design. MATB performance was measured as a function of accuracy. We analyzed the fMRI brain scans using both static and dynamic functional connectivity to determine whether there were differences in the connectivity patterns associated with each of the four conditions. A significant interaction between Task and Difficulty was observed such that multitasking performance accuracy, which was derived from the average across tasks, was lower than single tasking in the hard, but not easy, condition. The fMRI data revealed that static and dynamic functional connectivity between the default mode and dorsal attention networks was stronger during multitasking relative to single tasking. The static functional connectivity between the default mode and left frontoparietal networks, along with the dynamic functional connectivity between the dorsal attention and left frontoparietal networks, were both more anti-correlated during multitasking relative to single tasking. Taken together, the static and dynamic functional connectivity analyses provide complementary information to reveal the interactions among cognitive networks that support multitasking performance. Targeting these networks may offer a path to enhance multitasking ability through the application of neurostimulation and neuroenhancement techniques.

Human Mental Workload: A Survey and a Novel Inclusive Definition

Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.

Evaluating mental workload during multitasking in simulated flight

BACKGROUND

Pilots must process multiple streams of information simultaneously. Mental workload is one of the main issues in man-machine interactive mode when dealing with multiple tasks. This study aimed to combine functional near-infrared spectroscopy (fNIRS) and electrocardiogram (ECG) to detect changes in mental workload during multitasking in a simulated flight.

METHODS

Twenty-six participants performed three multitasking tasks at different mental workload levels. These mental workload levels were set by varying the number of subtasks. fNIRS and ECG signals were recorded during tasks. Participants filled in the national aeronautics and space administration task load index (NASA-TLX) scale after each task. The effects of mental workload on scores of NASA-TLX, performance of tasks, heart rate (HR), heart rate variability (HRV), and the prefrontal cortex (PFC) activation were analyzed.

RESULTS

Compared to multitasking in lower mental workload conditions, participants exhibited higher scores of NASA-TLX, HR, and PFC activation when multitasking in high mental workload conditions. Their performance was worse during the high mental workload multitasking condition, as evidenced by the higher average tracking distance, smaller number of response times, and longer response time of the meter. The standard deviation of the RR intervals (SDNN) was negatively correlated with subjective mental workload in the low task load condition and PFC activation was positively correlated with HR and subjective mental workload in the medium task load condition.

CONCLUSION

HR and PFC activation can be used to detect changes in mental workload during simulated flight multitasking tasks.

Reader fatigue - Electroencephalography findings: A case study in students

BACKGROUND

Mental fatigue is usually accompanied by a sense of weariness, reduced alertness, and reduced mental performance, which can lead to accidents, decrease of productivity in workplace and several other health hazards.

OBJECTIVE

The aim of this study was to assess mental fatigue of students while reading for a prolonged duration of time by application of electroencephalography (EEG).

METHODS

Ten healthy students (27.57±3.4 years; 5 females and 5 males), participated in the study. The experimental design consisted of 5 blocks of 15-min length, in total 75 min for each participant. The experiment was done without any reading activities at the first block. In the following, participants studied the texts and corrected the mistakes. In each block EEG (beta, alpha, and theta power), and the Karolinska Sleepiness Scale (KSS) were recorded.

RESULTS

The mean of the self-assessment of sleepiness by KSS from the first to final 15 minutes were 2.3, 3.4, 4.3, 5.2, and 6.1, respectively. The average power in the theta band decreased from 1.23μV2/Hz at the first 15-min period to 1.02μV2/Hz at the last 15-min period. Also, mean power in the alpha band decreased from 0.85μV2/Hz at the first 15-min period to 0.59μV2/Hz at the last 15-min period.

CONCLUSION

The study showed that the KSS and EEG activity indicate sleepiness which were highly correlated, with both changing along with performance.

Proof-of-Concept and Test-Retest Reliability Study of Psychological and Physiological Variables of the Mental Fatigue Paradigm

This study provided a proof-of-concept and test–retest reliability of measures frequently used to assess a mental fatigue paradigm. After familiarization, 28 healthy men performed (40-min) the Rapid Visual Information Processing (RVP) test in a test–retest design, having mental fatigue sensation, motivation, emotional arousal, total mood disturbance, and electroencephalography (EEG) in the prefrontal cortex measured before and after the test. EEG was recorded during a 3-min rest so that the power spectral density of theta (3–7 Hz) and alpha (8–13 Hz) bands was calculated. Pre-to-post RVP test changes in psychological and physiological domains were compared (paired-T tests), and absolute (standard error of measurement (SEM) and minimal difference (MD)) and relative reliability (intraclass correlation coefficient (ICC)) were calculated. The RVP test induced an increase (p < 0.05) in mental fatigue sensation (120.9% (109.4; 132.4)) and total mood disturbance (3.5% (−6.3; 13.3)), and a decrease in motivation (−7.1% (−9.2; −5.1)) and emotional arousal (−16.2% (−19.1; −13.2)). Likewise, EEG theta (59.1% (33.2; 85.0); p < 0.05), but not alpha band, increased due to RVP test. All psychophysiological responses showed poor-to-moderate relative reliability. Changes in mental fatigue sensation and motivation were higher than SEM and MD, but changes in EEG theta band were higher only than SEM. Mental fatigue sensation, motivation, and EEG theta band were sensitive to distinguish a mental fatigue paradigm despite true mental fatigue effects on theta activity may be trivial.

Symptoms of convergence and accommodative insufficiency predict engagement and cognitive fatigue during complex task performance with and without automation

Deficits in the accommodative and/or vergence responses have been linked with inattentive behavioral symptoms. While using automated systems (e.g., self-driving cars, autopilot), operators (e.g., drivers, pilots, soldiers) visually monitor displays for critical changes, making deficits in the accommodative and/or vergence responses potentially hazardous for individuals remaining actively engaged in the task at hand. The purpose of this study was to determine if symptoms of accommodative-vergence deficits predict an individual's level of task engagement and cognitive fatigue while performing a flight simulation task with or without automation. Eighty-four participants performed a flight simulation task with or without automation. Prior to task completion, self-report accommodative-convergence deficit symptoms were assessed with the Convergence Insufficiency Symptom Survey (CISS). Before and after the flight simulation task participants rated their task engagement and cognitive fatigue. Electroencephalographic activity (EEG) was recorded concurrently during task performance. Results showed that higher scores on the CISS were related to increased feelings of fatigue and decreased ratings of task engagement. The CISS was also positively related to parietal-occipital fast alpha power during the last 10 min of the task for participants using automation, suggesting increased cortical idling. CISS scores did not predict performance. Results have implications for optimizing operator cognitive states over extended task performance.

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis

The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human-Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators' cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators' MWL, whilst also allowing for increased integrity and reliability of the system.

Aircraft Pilots Workload Analysis: Heart Rate Variability Objective Measures and NASA-Task Load Index Subjective Evaluation

Workload and fatigue of aircraft pilots represent an argument of great interest in the framework of human factors and a pivotal point to be considered in aviation safety. 75% of aircraft accidents are related to human errors that, in most cases, are due to high level of mental workload and fatigue. There exist several subjective or objective metrics to quantify the pilots’ workload level, with both linear and nonlinear relationships reported in the literature. The main research objective of the present work is to analyze the relationships between objective and subjective workload measurements by looking for a correlation between metrics belonging to the subjective and biometric rating methods. More particularly, the Heart Rate Variability (HRV) is used for the objective analysis, whereas the NASA-TLX questionnaire is the tool chosen for the subjective evaluation of the workload. Two different flight scenarios were considered for the studies: the take-off phase with the initial climb and the final approach phase with the landing. A Maneuver Error Index (MEI) is also introduced to evaluate the pilot flight performance according to mission requirements. Both qualitative and quantitative correlation analyses were performed among the MEI, subjective and objective measurements. Monotonic relationships were found within the HRV indexes, and a nonlinear relationship is proposed among NASA-TLX and HRV indexes. These findings suggest that the relationship between workload, biometric data, and performance indexes are characterized by intricate patterns of nonlinear relationships.

Pilots’ mental workload prediction based on timeline analysis

BACKGROUND:

The aircraft cockpit is a highly intensive human-computer interaction system, and its design directly affects flight safety.

OBJECTIVE:

To optimize the display interface design in complex flight tasks, the present study aimed to propose a dynamic conceptual framework and a timeline task analysis method for the quantization of the dynamic time effect of mental workload and the influencing factors of task types in the mental workload prediction model.

METHODS:

The multi-factor mental workload prediction model based on attention resource allocation was integrated to establish the dynamic prediction model of mental workload. The ergonomics simulation experiment was carried out by recording the data on the performance of embedded subtasks, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) subjective evaluation, and eye tracking.

RESULTS:

The results indicated that the prediction model had a good prediction accuracy and effectiveness under different simulated interfaces and complex tasks, and the real-time monitoring of pilots’ mental workload state was realized.

CONCLUSION:

In conclusion, the prediction model and the experimental method could be applied to avoid the overload of the pilot throughout the flight phase by optimizing the display interface and adjusting the flight task.

Mental workload characteristics of manipulator teleoperators with different spatial cognitive abilities

The main research on manipulator teleoperation includes robust of high-degree of freedom manipulators, sensor measurement accuracy, time delay, and mechanical structure design. Increased mental capacity requirements for complex assignments result in an increased mental workload. Spatial cognitive ability was considered to be the key factor affecting teleoperation performance. To accomplish this, we had 50 participants performed teleoperation while recorded their electroencephalogram. Electroencephalogram data of each task were divided into two periods, which correspond to the observation and large-scale transfer stages of teleoperation, respectively (period 1) and adjust the attitude of the manipulator to approach and align with the target stage (period 2). Brain topographic maps of period 1 (period 1 wavelet packet energy minus resting state wavelet packet energy) and period 2 (period 2 wavelet packet energy minus resting state wavelet packet energy) show that the frontal, central, and occipital regions are the main working areas of low spatial cognitive operators in period 1, while the frontal, central, and occipital regions are the main working areas of high spatial cognitive operators in period 1. The main changes in period 2 were frontal, central, parietal, and occipital regions. This study has implication for the analysis of electroencephalogram signal characteristics of mental workload in different populations to improve operators’ well-being and safety at teleoperation work.

Autonomic Nervous System Responses to Whole-Body Vibration and Mental Workload: A Pilot Study

Background:

Whole-body vibration (WBV) and mental workload (MWL) are common stressors among drivers who attempt to control numerous variables while driving a car, bus, or train.

Objective:

To examine the individual and combined effects of the WBV and MWL on the autonomic nervous system.

Methods:

ECG of 24 healthy male students was recorded using NeXus-4 while performing two difficulty levels of a computerized dual task and when they were exposing to WBV (intensity 0.5 m/s2; frequency 3–20 Hz). Each condition was examined for 5 min individually and combined. Inter-beat intervals were extracted from ECG records. The time-domain and frequency-domain heart rate variability parameters were then extracted from the inter-beat intervals data.

Results:

A significant (p=0.008) increase was observed in the mean RR interval while the participants were exposed to WBV; there was a significant (p=0.02) reduction in the mean RR interval while the participants were performing the MWL. WBV (p=0.02) and MWL significantly (p<0.001) increased the standard deviation of normal-to-normal intervals with a moderate-to-large effect size. All active periods increased the low-frequency component and low-frequency/high-frequency ratio. However, only the WBV significantly increased the highfrequency component. A significant (p=0.01) interaction was observed between the WBV and MWL on low-frequency component and low-frequency/high-frequency ratio.

Conclusion:

Exposure to WBV and MWL can dysregulate the autonomic nervous system. WBV stimulates both sympathetic and parasympathetic nervous system; MWL largely affects sympathetic nervous system. Both variables imbalance the sympatho-vagal control as well.

A Systematic Review of Physiological Measures of Mental Workload

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

Cardiac Measures of Cognitive Workload: A Meta-Analysis

OBJECTIVE

We aimed to provide an assessment of the impact of workload manipulations on various cardiac measurements. We further sought to determine the most effective measurement approaches of cognitive workload as well as quantify the conditions under which these measures are most effective for interpretation.

BACKGROUND

Cognitive workload affects human performance, particularly when load is relatively high (overload) or low (underload). Despite ongoing interest in assessing cognitive workload through cardiac measures, it is currently unclear which cardiac-based assessments best indicate cognitive workload. Although several quantitative studies and qualitative reviews have sought to provide guidance, no meta-analytic integration of cardiac assessment(s) of cognitive workload exists to date.

METHOD

We used Morris and DeShon's meta-analytic procedures to quantify the changes in cardiac measures due to task load conditions.

RESULTS

Sample-weighted Cohen's d values suggest that several metrics of cardiac activity demonstrate sensitivity in response to cognitive workload manipulations. Heart rate variability measures show sensitivity to task load, conditions of event rate, and task duration. Authors of future work should seek to quantify the utility of leveraging multiple metrics to understand workload.

CONCLUSION

Results suggest that assessment of cognitive workload can be done using various cardiac activity indicators. Further, given the number of valid and reliable measures available, researchers and practitioners should base their selection of a psychophysiological measure on the experimental and practical concerns inherent to their task/protocol.

APPLICATIONS

Findings bear implications for future assessment of cognitive workload within basic and applied settings. Future research should seek to validate conditions under which measurements are best interpreted, including but not limited to individual differences.

Adaptações do cérebro durante uma tarefa de longa duração: Um estudo de Potencial Relacionado a Evento

Resumo O objetivo deste estudo é investigar o efeito da demanda cognitiva prolongada na modulação do Potencial Relacionado a Evento (ERP) em um paradigma de controle inibitório. Os dados foram coletados em 19 voluntários destros, com a média de idade de 21,21 (±1,77) anos, que realizaram o paradigma do Go/NoGo durante 50 minutos, com gravação sincronizada do eletroencefalograma para obtenção dos ERPs. O efeito do tempo de realização da tarefa provocou alterações significativas nas variáveis subjetivas, de desempenho cognitivo e nas amplitudes máximas dos componentes N2 e P3. Nossos resultados sugerem que quando nosso cérebro está submetido a demandas cognitivas extensas, ocorrem adaptações para a manutenção do desempenho comportamental através da estratégia de realocação de recursos energéticos.

Measuring mental workload using physiological measures: A systematic review

Technological advances have led to physiological measurement being increasingly used to measure and predict operator states. Mental workload (MWL) in particular has been characterised using a variety of physiological sensor data. This systematic review contributes a synthesis of the literature summarising key findings to assist practitioners to select measures for use in evaluation of MWL. We also describe limitations of the methods to assist selection when being deployed in applied or laboratory settings. We detail fifty-eight peer reviewed journal articles which present original data using physiological measures to include electrocardiographic, respiratory, dermal, blood pressure and ocular. Electroencephalographic measures have been included if they are presented with another measure to constrain scope. The literature reviewed covers a range of applied and experimental studies across various domains, safety-critical applications being highly represented in the sample of applied literature reviewed. We present a summary of the six measures and provide an evidence base which includes how to deploy each measure, and characteristics that can affect or preclude the use of a measure in research. Measures can be used to discriminate differences in MWL caused by task type, task load, and in some cases task difficulty. Varying ranges of sensitivity to sudden or gradual changes in taskload are also evident across the six measures. We conclude that there is no single measure that clearly discriminates mental workload but there is a growing empirical basis with which to inform both science and practice.

Cardiovascular correlates of emotional state, cognitive workload and time-on-task effect during a realistic flight simulation

In aviation, emotion and cognitive workload can considerably increase the probability of human error. An accurate online physiological monitoring of pilot's mental state could prevent accidents. The heart rate (HR) and heart rate variability (HRV) of 21 private pilots were analysed during two realistic flight simulator scenarios. Emotion was manipulated by a social stressor and cognitive workload with the difficulty of a secondary task. Our results confirmed the sensitivity of the HR to cognitive demand and training effects, with increased HR when the task was more difficult and decreased HR with training (time-on-task). Training was also associated with an increased HRV, with increased values along the flight scenario time course. Finally, the social stressor seemed to provoke an emotional reaction that enhanced motivation and performance on the secondary task. However, this was not reflected by the cardiovascular activity.

The validity of linear and non-linear heart rate metrics as workload indicators of emergency physicians

Background

It has been shown that linear and non-linear heart rate variability (HRV) metrics are suitable to assess workload of anesthetists administering anesthesia. In pre-hospital emergency care, these parameters have not yet been evaluated. We hypothesized that heart rate (HR) and HRV metrics discriminate between differing workload levels of an emergency physician.

Methods

Electrocardiograms were obtained from 13 emergency physicians. Mean HR, ten linear and seven non-linear HRV metrics were analyzed. For each sortie, four different levels of workload were defined. Mixed-effects models and the area under the receiver operating characteristics curve (AUC) were used to test and quantify the HR and HRV metrics’ ability to discriminate between levels of workload. This was conducted for mean HR and each HRV metric as well as for groups of metrics (time domain vs. frequency domain vs. non-linear metrics).

Results

The non-linear HRV metric Permutation entropy (PeEn) discriminated best between the time before the alarm and primary patient care (AUC = 0.998, 1st rank of 18 HRV metrics). In contrast, AUC of the mean HR was low (0.558, 17th rank). In the multivariable approach, the non-linear HRV metrics provided a higher AUC (0.998) compared to the frequency domain (0.677) and to the time domain metrics (0.680).

Conclusion

Non-linear heart rate metrics and, specifically, PeEn provided good validity for the assessment of different levels of a physician’s workload in the setting of pre-hospital emergency care. In contradiction to earlier findings, the physicians’ mean HR was not a valid marker of workload.

Combat Stress Decreases Memory of Warfighters in Action

The present research aimed to analyze the effect of combat stress in the psychophysiological response and attention and memory of warfighters in a simulated combat situation. Variables of blood oxygen saturation, heart rate, blood glucose, blood lactate, body temperature, lower body muscular strength manifestation, cortical arousal, autonomic modulation, state anxiety and memory and attention through a postmission questionnaire were analyzed before and after a combat simulation in 20 male professional Spanish Army warfighters. The combat simulation produces a significant increase (p < 0.05) in explosive leg strength, rated perceived exertion, blood glucose, blood lactate, somatic anxiety, heart rate, and low frequency domain of the HRV (LF) and a significant decrease of high frequency domain of the heart rate variability (HF). The percentage of correct response in the postmission questionnaire parameters show that elements more related with a physical integrity threat are the most correctly remembered. There were significant differences in the postmission questionnaire variables when participants were divided by the cortical arousal post: sounds no response, mobile phone correct, mobile phone no response, odours correct. The correlation analysis showed positive correlations: LF post/body temperature post, HF post/correct sound, body temperature post/glucose post, CFFTpre/lactate post, CFFT post/wrong sound, glucose post/AC pre, AC post/wrong fusil, AS post/SC post and SC post/wrong olfactory; and negative correlations: LF post/correct sound, body temperature post/lactate post and glucose post/lactate post. This data suggest that combat stress actives fight-flight system of soldiers. As conclusion, Combat stress produces an increased psychophysiological response that cause a selective decrease of memory, depending on the nature, dangerous or harmless of the objects.

Assessment of the interaction of hyperbaric N2, CO2, and O2 on psychomotor performance in divers

Diving narcosis results from the complex interaction of gases, activities, and environmental conditions. We hypothesized that these interactions could be separated into their component parts. Where previous studies have tested single cognitive tasks sequentially, we varied inspired partial pressures of CO₂, N₂, and O₂ in immersed, exercising subjects while assessing multitasking performance with the Multi-Attribute Task Battery II (MATB-II) flight simulator. Cognitive performance was tested under 20 conditions of gas partial pressure and exercise in 42 male subjects meeting U.S. Navy age and fitness profiles. Inspired nitrogen (N₂) and oxygen (O₂) partial pressures were 0, 4.5, and 5.6 ATA and 0.21, 1.0, and 1.22 ATA, respectively, at rest and during 100-W immersed exercise with and without 0.075-ATA CO₂ Linear regression modeled the association of gas partial pressure with task performance while controlling for exercise, hypercapnic ventilatory response, dive training, video game frequency, and age. Subjects served as their own controls. Impairment of memory, attention, and planning, but not motor tasks, was associated with N₂ partial pressures >4.5 ATA. Sea level O₂ at 0.925 ATA partially rescued motor and memory reaction time impaired by 0.075-ATA CO₂; however, at hyperbaric pressures an unexpectedly strong interaction between CO₂, N₂, and exercise caused incapacitating narcosis with amnesia, which was augmented by O₂ Perception of narcosis was not correlated with actual scores. The relative contributions of factors associated with diving narcosis will be useful to predict the effects of gas mixtures and exercise conditions on the cognitive performance of divers. The O₂ effects are consistent with O₂ narcosis or enhanced O₂ toxicity.