Multi-Attribute Task Battery Research Articles

EEG Feature Analysis Related to Situation Awareness Assessment and Discrimination

In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety.

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.

Detecting Human Trust Calibration in Automation: A Convolutional Neural Network Approach

There is a general lack of studies that are aimed at monitoring and detecting an operator's trust calibration, even though detecting someone's adjusted trust towards automation is essential to prevent misuse and disuse of automation. The goal of this article is to propose a convolutional neural network (CNN) based framework to estimate operators’ trust levels and detect their trust calibration in automation using image features of electroencephalogram (EEG) signals preserving temporal, spectral, and spatial information. Thirteen participants performed a set of automated Air Force multiattribute task battery tasks that differed in reliability (High/Low) and credibility (High/Low) levels. The proposed framework was compared with three machine learning methods—naïve bayes, support vector machine, multilayer perceptron—in terms of accuracy, sensitivity, and specificity of trust estimation and detection of trust calibration. Results of this article showed that the proposed framework had the highest performance of both trust estimation and detection of trust calibration in automation compared to the other comparison methods. This indicates that the proposed framework using the CNN classifier with the image-based EEG features could be an applicable model for estimating multilevel trust and detecting trust calibration during human-automation interaction. Also, it can help to prevent disuse and misuse of automation by estimating operators’ trust levels and monitoring their trust calibration in automation.

The effect of applied effort on MATB-II performance

Some of the variability found in measures of mental workload (see e.g. Singleton, Fox, and Whitfield 1973; Wierwille and Connor 1983; Steel­man, McCarley, and Wickens 2011; Casner and Gore 2010) may be due to the effort applied to the task by participants, rather than by the independent variable of interest. If true, capturing and removing the variation due to ‘applied effort’ could improve the ability of studies to detect effects of interest. While introducing participants to two sub-tasks derived from Multi-Attribute Task Battery II (Santiago-Espada et al. 2011), the study investigated the influence of applied effort on MATB-II performance measures while holding other effects constant. Two groups of participants each completed easy and hard trials of MATB-II-derived sub-tasks. Treatment group of participants was offered an additional reward if they achieved a sufficiently high performance. The treatment group performed better by just under 4% in both easy and hard trials which provides a suggestion about the size of the effect of applied effort in this study. Measuring or controlling for applied effort can improve the ability of researchers to determine the effects of interventions on workload measures by reducing the amount of variability that is captured as error.

Mental Workload in Neuropsychology: An Example With the NASA-TLX in Adults With HIV.

A preliminary set of analyses are presented, where workload was examined in 32 adults infected with the human immunodeficiency virus (HIV). Like the current COVID-19 pandemic (caused by the SARS-CoV-2 virus), HIV can produce a wide variety of symptoms, including various levels of cognitive dysfunction. In fact, a recent meta-analysis estimates that of the 39 million adults infected globally with HIV, 42.6% exhibit some form of HIV-associated neurocognitive disorder. A common cognitive symptom in HIV is decline in attention and executive functioning. Though typically examined by clinicians with less precise traditional paper-and-pencil neuropsychological tests, we examined this aspect of cognitive functioning using a more psychometrically sophisticated task as we had HIV-positive adults perform a computerized tracking task in single, dual, and tri-task conditions via the Multi-Attribute Task (MAT) Battery. Also assessed was mental workload, with the NASA-Task Load Index (NASA-TLX), rarely used in neuropsychology but a standard tool in human factors and neuroergonomics research. As expected, tracking performance declined with task condition difficulty (p < 0.001). Although no direct statistical comparisons were made, MAT performance here appeared worse than the MAT performance of various other groups reported in the research literature and in our laboratory. Ratings of workload also tended to increase as a function of task condition difficulty (p < 0.001). Plotting MAT tracking performance against the Mental Demand subscale scores, large individual differences in this aspect of workload were evident in both optimal and sub-optimal tracking performance. To examine likely variables with a potential impact on Mental Demand, a variety of variables (nadir CD4 count, viral load, depression symptoms, diagnosis of AIDS, presence of opportunistic infection, general cognitive status, etc.) were examined in relation to the Mental Demand scale, with age showing a significant association (r = 0.41, p = 0.022) and a diagnosis of AIDS showing trend associations (ps ≥ 0.066). Findings suggesting a deficit in metacognition or insight are also discussed. It is argued that assessment of workload (and its various aspects or components) can provide valuable additional information in neuropsychology.

DECODING THE CORRELATION BETWEEN BRAIN AND HEART ACTIVATIONS IN THE COMBINATION OF MENTAL WORKLOAD AND PHYSICAL ACTIVITY

Decoding of the coupling among the brain and heart activations is an important research area in network physiology. We studied the coupling of brain and heart activations for 48 subjects who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. During the experiment, the physical activity of subjects was manipulated by changing the speed of a stationary bike (including no movement, 50[Formula: see text]rpm, and 70[Formula: see text]rpm) or a treadmill (including no movement, 3[Formula: see text]km/h, and 5[Formula: see text]km/h), while their physiological signals were recorded. We analyzed the complex structure of electroencephalogram (EEG) and R-R signals using fractal theory and sample entropy. The results demonstrated that the alterations of the complex structures of EEG and R-R signals are strongly correlated, which indicates the coupling between brain and heart activations. This method of analysis can be applied to evaluate the coupling between different organs.

Providing different levels of accuracy about the reliability of automation to a human operator: impact on human performance

Previous research has suggested that supervising automation can lead to a decrease in human performance, especially when automation is not totally reliable. Providing context-related information about reliability can help operators to better adjust their behaviour in a human–automation interaction context. However, previous studies have not specified the level of accuracy that this information should provide to the human operator. The objective of this study was to investigate the effects of different levels of information accuracy about an automation’s reliability on human performance. Results showed that accuracy of information about reliability improves performance when specific percentages of reliability were given to the participants. Participants had a better performance in the condition of high accuracy of information. A link between perceived reliability and trust was found: the more the trust in automation increased, the more the perceived reliability increased. Practitioner summary: The experiment dealt with how accurate information about automation’s reliability influences people’s performance when supervising an automated task. Overall, this research suggests that designing systems that provide accurate, useful information can reduce the frequency of automation bias. Trust and perceived reliability of automation are related. Abbreviations: MATB: multi attribute task battery

Development of the Smart Tools Proneness Questionnaire (STP-Q): an instrument to assess the individual propensity to use smart tools

Humans have developed a prolonged and special relationship with their tools, which themselves exhibit the propensity to become ever more intelligent across the years. A ‘smart tool’ is defined as to representing any entity, machine, or device that can complete an informational, mechanical, or electronic work. This work explains the development of the Smart Tool Proneness Questionnaire (STP-Q), which is designed to measure an individual’s propensity to use smart tools. Data collection was designed to (1) identify the psychological dimensions underlying smart tool use (2) establish the questionnaire’s reliability (3) validity, (4) propose a normalisation, and (5) provide an English translation of the French original. The work therefore implements a reliable and valid questionnaire, sensitive to inter-individual differences regarding the propensity to use smart tools. Statistical analysis reveals that the individual self-reported propensity for smart tool use rests on three factors (1) utilitarian use, (2) hedonic and social use, and (3) proneness to delegate. From a theoretical perspective, this individual propensity to use smart tools might be considered key to our species development. In practical terms, measuring an individual’s propensity to use smart tools can be of considerable benefit to the design of future smart tools in both professional and non-professional settings. Practitioner summary: The STP-Q, a self-reported measure of an individual’s propensity to use smart tools, was developed. STP-Q offers practitioners a measure of individual propensity to use smart tools along three dimensions: utilitarian use, hedonic and social use, and proneness to task delegate. Individual results can easily be interpreted from normalizations that STP-Q provides. Abbreviations: CFI: comparative fit index; GFI: goodness of fit index; IFI: incremental fit index; ISO: International Standardization Organization; IRB: institutional review board of the university of central Florida; IT: information technology; MATB: multi-attribute task battery; NMP-Q: no more phone phobia; RMSEA: root mean square error of approximation; STP-Q: smart tools proneness questionnaire; TAM: technology acceptance model; TRI: technology readiness index; UTAUT: unified theory of acceptance and use of technology; WAIS IV: Wechsler adult intelligence scale

Multi-Attribute Task Battery configuration to effectively assess pilot performance deterioration during prolonged wakefulness

Multi-Attribute Task Battery (MATB) simulates realistic and complex aviation-related tasks a pilot routinely performs during flight. However, past studies using MATB have been unable to consistently elicit the effects of performance degradation during prolonged wakefulness. We surmise that this is because the task difficulty level of the MATB software was set too low. The MATB test that was designed for this protocol includes four tasks: system monitoring (SYSMON), communication (COMM), resource management (RESMAN), and all three tasks performed simultaneously. Twenty subjects performed a “session” consisting of both the psychomotor vigilance task (PVT) and the MATB test every 2 h over 25 h of prolonged wakefulness (i.e., for thirteen sessions). The difficulty levels were set to low for both SYSMON and COMM tasks, medium for RESMAN, and high for completion of the three tasks simultaneously, based on NASA task load index. We then calculated the correlation between PVT and MATB indices. As in previous studies, all PVT performance measures were significantly degraded in the last 2–4 sessions of the task, relative to sessions earlier in the 25-hr period. MATB indices were highly correlated with PVT indices. Moreover, all MATB tasks showed significant performance degradation during prolonged wakefulness unlike previous studies. Finally, the considerably more difficult multitasking required by the three simultaneous tasks led to a more consistent and higher degree of performance degradation with prolonged wakefulness than did most of the single tasks, which were set to either low or medium difficulty level. Thus, our results support the hypothesis that previous work failed to show significant performance degradation with prolonged wakefulness during the MATB due to inadequate levels of task difficulty. Our results provide evidence that the MATB, if set to an appropriate level of difficulty, can be used as an alternative to the PVT to more accurately study the effects of prolonged wakefulness on performance of realistic aviation tasks.

Cross-Task Consistency of Electroencephalography-Based Mental Workload Indicators: Comparisons Between Power Spectral Density and Task-Irrelevant Auditory Event-Related Potentials.

Mental workload (MWL) estimators based on ongoing electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to build adaptive aiding systems for human–machine systems by estimating MWL in real time. However, extracting EEG features which are consistent in indicating MWL across different tasks is still one of the critical challenges. This study attempts to compare the cross-task consistency in indexing MWL variations between two commonly used EEG-based MWL indicators, power spectral density (PSD) of ongoing EEG and task-irrelevant auditory ERPs (tir-aERPs). The verbal N-back and the multi-attribute task battery (MATB), both with two difficulty levels, were employed in the experiment, along with task-irrelevant auditory probes. EEG was recorded from 17 subjects when they were performing the tasks. The tir-aERPs elicited by the auditory probes and the relative PSDs of ongoing EEG between two consecutive auditory probes were extracted and statistically analyzed to reveal the effects of MWL and task type. Discriminant analysis and support vector machine were employed to examine the generalization of tir-aERP and PSD features in indexing MWL variations across different tasks. The results showed that the amplitudes of tir-aERP components, N1, early P3a, late P3a, and the reorienting negativity, significantly decreased with the increasing MWL in both N-back and MATB. Task type had no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERP features. The relative PSDs in θ, α, and low β bands were also sensitive to MWL variations. However, the MWL-sensitive PSD features and their topological patterns were significantly affected by task type. The cross-task classification results based on tir-aERP features also significantly outperformed the PSD features. These results suggest that the tir-aERPs should be potentially more consistent MWL indicators across very different task types when compared to PSD. The current study may provide new insights to our understanding of the common and distinctive neuropsychological essences of MWL across different tasks.

Impact of Pilot's Expertise on Selection, Use, Trust, and Acceptance of Automation

Automation regroups a variety of advanced tools meant to improve performance and decrease human workload. This article was designed to investigate how different automation solutions, engaging different human-machine cooperation modes, interact with expertise. Aircraft pilots (i.e., experts) and nonpilots (i.e., novices) were presented with a set of simplified flight piloting tasks monitored simultaneously using the Open MATB (Open Multiattribute Task Battery) in four different automation conditions (manual, assisted, cooperative, and supervisory control). Experts' performances at the Open MATB were higher than those of novices. Experts also exhibited a lower level of workload. Apart from function delegation, where no human performance is required, automation solutions benefit experts and novices similarly. Participants were then asked to choose repeatedly between the four automation conditions. Experts and novices exhibited different strategies. Novices spread their choices over the different automation conditions, whereas experts clearly favor cooperative control, the automation solution that enhanced their expertise.

Validating the Strategic Task Overload Management (STOM) Model Using MATB II and Eye-tracking

Operators can be overloaded and struggle to make sense of and prioritize multiple tasks. Task selection in these cases is of utmost importance. We replicated an experiment using the Multi Attribute Task Battery II (MATB II) for validating a model of strategic task switching (STOM), adding eye tracking measurement, resulting in a new assessment. Task difficulty affected how operators switched tasks, while priority had little to no effect. Newly measured for STOM, eye tracking revealed a link between task difficulty and time spent performing a task but failed to meet predictions for interest and priority effects. The outcome of the validation effort as it relates to the STOM model, as well as eye tracking implications, are discussed.

Patterns of Eye Movement Influenced by Multitasking Workload: A Clustering Analysis

Although multitasking has been studied in the past few decades, there has been a lack of investigation on how individuals’ multitasking performance can be predicted using eye movement data. To address this gap, this study proposed an exploratory approach to understand the manifestation of eye movement patterns that could provide diagnostic and predictive information of multitasking performance. Nineteen participants completed Multi-Attribute Task Battery (MATB-II) experiments under both low and high workloads and their eye movement and MATB-II task performance were collected. We applied a hierarchical clustering method that classified the participants into three clusters – clusters with small, medium, and large number of fixations. Then, we compared the MATB-II performance of the three clusters. The results s howed significant differences in average reaction time to stimuli and average error count among the three clusters. Our study showed that hierarchical clustering of eye fixations can effectively predict multitasking performance.

Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems

With increasingly higher levels of automation in aerospace decision support systems, it is imperative that the human operator maintains the required level of situational awareness in different operational conditions and a central role in the decision-making process. While current aerospace systems and interfaces are limited in their adaptability, a Cognitive Human Machine System (CHMS) aims to perform dynamic, real-time system adaptation by estimating the cognitive states of the human operator. Nevertheless, to reliably drive system adaptation of current and emerging aerospace systems, there is a need to accurately and repeatably estimate cognitive states, particularly for Mental Workload (MWL), in real-time. As part of this study, two sessions were performed during a Multi-Attribute Task Battery (MATB) scenario, including a session for offline calibration and validation and a session for online validation of eleven multimodal inference models of MWL. The multimodal inference model implemented included an Adaptive Neuro Fuzzy Inference System (ANFIS), which was used in different configurations to fuse data from an Electroencephalogram (EEG) model’s output, four eye activity features and a control input feature. The online validation of the ANFIS models produced good results, while the best performing model (containing all four eye activity features and the control input feature) showed an average Mean Absolute Error (MAE) = 0.67 ± 0.18 and Correlation Coefficient (CC) = 0.71 ± 0.15. The remaining six ANFIS models included data from the EEG model’s output, which had an offset discrepancy. This resulted in an equivalent offset for the online multimodal fusion. Nonetheless, the efficacy of these ANFIS models could be confirmed by the pairwise correlation with the task level, where one model demonstrated a CC = 0.77 ± 0.06, which was the highest among all of the ANFIS models tested. Hence, this study demonstrates the suitability for online multimodal fusion of features extracted from EEG signals, eye activity and control inputs to produce an accurate and repeatable inference of MWL.

Automation Type and Reliability Impact on Visual Automation Monitoring and Human Performance

ABSTRACT We compared automation monitoring evolution of static or adaptive automation for four different reliability levels over 90 minutes. Previous studies have demonstrated degraded human performance when monitoring automation and that it is possible to mitigate this monitoring performance drop by using adaptive automation. We used the Open Multi-Attribute Task Battery to manipulate two type of automation (static automation without manual take-over sessions and adaptive automation with planned take-over sessions) and four levels of reliability. Participants performed three simultaneous tasks, one of which was automated. Our results suggest that a perfectly reliable or a totally unreliable automation led to different strategies by the participants in terms of visual allocation policy. Under static automation, the time spent looking at the automated task in the 0% reliability level increased over the duration of the experiment; however, the opposite was observed for the 100% reliability level. Although similar, the magnitude of this pattern of results was largely diminished under adaptive automation. For static automation, the reported data also showed a direct link between trust in automation and visual scanning strategies. The more the trust increased, the less the automated task was looked at.

Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study.

Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation. Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. Results indicated that trust and distrust can be two distinctive neural processes in human-automation interaction-distrust being a more complex network than trust, possibly due to the increased cognitive load. The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human-automation interface design but also in the proper use of automation in real-life situations.

Effect of Imperfect Information and Action Automation on Attentional Allocation

ABSTRACT Previous research has suggested that information and action automation stages do not imply the same consequences for human performance in the supervision of automated systems. Still, only a few studies have simultaneously investigated these stages. When information and action automation are reliable, both can support performance. However, with unreliable aids, the literature has suggested that action automation tends to be more detrimental than information automation. This study aimed to assess the contributions of imperfect information and action automation on attentional allocation and to investigate a potential monitoring inefficiency in a multitasking environment. Participants (n = 96) completed three Multi-Attribute Task Battery (MATB) tasks. A monitoring task was automated with two types of automation (action or information) of four reliabilities each (0%; 56.25%; 87.5%; 100%). Ocular behaviors and performance were assessed. Results show that reliability of information automation influenced visual resource allocation. When information automation was the most reliable, participants spent the least amount of time sampling the monitoring task. Finally, the reliability of action automation triggered no effect on performance or cumulative dwell times. Our results suggest that in complex multitasking situations where information and action automation occurred simultaneously, participants allocated fewer visual resources to automated task with increased information automation reliability. Similarly, their performance was better only with increased information automation.

The Effect of Task Complexity on Eye Movement and Multitasking Performance in Students With and Without ADHD

Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder that affects many individual’s ability to maintain and shift attention. Little is known about the connection between executive function and eye movement in individuals diagnosed with ADHD during multitasking. The objective of this research is to examine the relationship between patterns of eye movement and multitasking performance between students with and without ADHD. During the experiment, students with and without ADHD completed NASA’s Multi-Attribute Task Battery at both low and high task complexity. We found that students without ADHD showed higher error counts, higher workload ratings, and greater total number of fixations in the high task complexity than in the low task complexity. By understanding the relationship among eye movement, executive functioning, and mental workload, it is possible to gain insight in attention allocation in online learning environments and apply that knowledge to create tools for equal access to learning.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity.

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

Task-irrelevant Auditory Event-related Potentials as Mental Workload Indicators: A Between-task Comparison Study.

Real-time monitoring of mental workload (MWL) is a crucial step to build closed-loop adaptive aiding systems for human-machine systems. MWL estimators based on spontaneous electroencephalography (EEG) and event-related potentials (ERPs) have shown great potentials to achieve this goal. However, the previous studies show that the between-task robustness of these EEG/ERP-based MWL estimators is still an unsolved intractable question. This study attempts to examine the task-irrelevant auditory event-related potentials (tir-aERPs) as MWL indicators. A working memory task (verbal n-back) and a visuo-motor task (multi-attribute task battery, MATB), both with two difficulty levels (easy and hard), were used in the experiment, along with task-irrelevant auditory probes that did not need any response from the participants. EEG was recorded from ten participants when they were performing the tasks. The tir-aERPs elicited by the auditory probes were extracted and analyzed. The results show that the amplitudes of N1, early P3a (eP3a) and the late reorienting negativity (RON) significantly decreased with the increasing MWL in both n-back and MATB. Task type has no obvious influence on the amplitudes and topological layout of the MWL-sensitive tir-aERPs features. These results suggest that the tir-aERPs are potentially more constant MWL indicators across very different task types. Therefore, the tir-aERPs should be taken into consideration in future task-independent MWL monitoring studies.