Different Levels Of Cognitive Load Research Articles

The effect of reward expectation on working memory of emotional faces under different levels of cognitive load: an ERP study.

Using event-related potentials (ERPs), this study examined the impact of reward expectations on working memory of emotional faces under different levels of cognitive load in a task combining the N-back paradigm and the reward expectation paradigm. The experiment involved presenting high- or low-reward cues followed by an N-back task for emotional faces with different loads. The accuracy results showed that under a high task load, both reward and emotion effects were significantly observed. However, these effects disappeared under a low task load. Analysis of the ERP data revealed that the early P2 and VPP components exhibited greater responses to fearful faces than to neutral faces. In the later stages, the P3 and LPP components showed greater reactions to high rewards than to low rewards. Additionally, the P2 component was found to be modulated by task load in relation to rewards, the EPN component demonstrated task load modulation with respect to emotions, and the N170 component showed an interaction effect between rewards and emotions. These findings imply that load regulates the reward effect and the emotional superiority effect in the process of working memory for emotional faces. In the cognitive processing of working memory, motivation and emotion jointly influence processing. Emotional factors have a greater impact in the early stage of processing, while motivation factors have a greater impact in the late stage of processing.

The Differential Effects of Multisensory Attentional Cues on Task Performance in VR Depending on the Level of Cognitive Load and Cognitive Capacity.

As the utilization of VR is expanding across diverse fields, research on devising attentional cues that could optimize users' task performance in VR has become crucial. Since the cognitive load imposed by the context and the individual's cognitive capacity are representative factors that are known to determine task performance, we aimed to examine how the effects of multisensory attentional cues on task performance are modulated by the two factors. For this purpose, we designed a new experimental paradigm in which participants engaged in dual (N-back, visual search) tasks under different levels of cognitive load while an attentional cue (visual, tactile, or visuotactile) was presented to facilitate search performance. The results showed that multi-sensory attentional cues are generally more effective than uni-sensory cues in enhancing task performance, but the benefit of multi-sensory cues changes according to the level of cognitive load and the individual's cognitive capacity; the amount of benefit increases as the cognitive load is higher and the cognitive capacity is lower. The findings of this study provide practical implications for designing attentional cues to enhance VR task performance, considering both the complexity of the VR context and users' internal characteristics.

The effects of increased mental workload of air traffic controllers on time perception: Behavioral and physiological evidence

Research has shown that timing is modulated by mental workload, making duration judgments a measure of cognitive demand, alongside subjective assessments, and physiological measurements. Yet, it is unclear whether such findings can be extended in less controlled setups. By employing air traffic controllers in a real aviation environment, we tested whether tasks with different levels of cognitive load can affect their timing behavior. Participants completed temporal production, verbal estimation, and passage of time judgments, while actively engaging in real flight control sessions. Subjective assessments of task demands, as well as physiological responses (cardiac and electrodermal activity) were also measured. Accuracy of the produced intervals was measured at two distinct phases of the flight (during low-load cruising vs. high-load landing) and under two different task load manipulations (controlling one vs. two helicopters and speaking in native vs. non-native language). Analysis of interval production accuracy showed that during the high-load landing phase significant overproductions were made, compared to the low-load cruising phase, and landing two helicopters led to greater overproductions compared to landing only one. The duration of the two-helicopter sessions was significantly overestimated compared to the single-helicopter ones, and the passage of time was felt significantly faster. Subjective assessments of workload were positively correlated with the temporal estimations and passage of time judgments, and skin responses were positively correlated with the produced intervals. Overall, our results are consistent with past research, suggesting that mental workload modulates time perception in complex, real-world environments, thus making timing behavior a reliable index of the workload changes.

Impacts of mobile phone texting on vertical ground reaction forces during stair negotiation

Stairs are one of the most dangerous places for accidental falls in both daily life and occupational settings. Knowledge about ground reaction force (GRF) characteristics during stair negotiation can facilitate a better understanding of the causal mechanisms for stair falls. The objective of this study was to investigate the effects of mobile phone texting on GRF during stair negotiation and reveal their implications associated with the safety during stair negotiation. Twelve young male adults were involved in an experimental study. They were instructed to carry out texting tasks with different levels of cognitive load while performing stair negotiation. Vertical GRF (vGRF) parameters accounting for force amplitude and loading/unloading rates were compared between texting conditions. Texting tasks resulted in slower force loading and unloading rates during stair ascent, while weight acceptance force during stair descent decreased with the application of texting tasks. These findings suggested that mobile phone texting can compromise postural stability and increase the risk of falls during stair negotiation. The findings from the present study highlight the necessity of avoiding mobile phone usage during stair negation to prevent accidental stair falls. They also have practical implications for developing effective fall prevention interventions.

Pupillometry in telerobotic surgery: A comparative evaluation of algorithms for cognitive effort estimation

Background: Eye gaze tracking and pupillometry are emerging topics in telerobotic surgery as it is believed that they will enable novel gaze-based interaction paradigms and provide insights into the user’s cognitive load (CL). Further, the successful integration of CL estimation into telerobotic systems is thought to catalyze the development of new human-computer interfaces for personalized assistance and training processes. However, this field is in its infancy, and identifying reliable gaze and pupil-tracking solutions in robotic surgery is still an area of ongoing research and high uncertainty. Methods: Considering the potential benefits of pupillometry-based CL assessments in telerobotic surgery, we seek to better understand the possibilities and limitations of contemporary pupillometry-based cognitive effort estimation algorithms in telerobotic surgery. To this end, we conducted a user study using the da Vinci Research Kit (dVRK) and performed two experiments where participants were asked to perform a series of N-Back tests, either while (i) idling or (ii) performing a peg transfer task. We then compare four contemporary CL estimation methods based on direct analysis of pupil diameter in the spatial and frequency domains. Results: We find that some methods can detect the presence of cognitive effort in simple scenarios (e.g., when the user is not performing any manual task), they fail to differentiate the different levels of CL. Similarly, when the manual peg transfer task is added, the reliability of all models is compromised, highlighting the necessity of more robust methods that consider different factors that complement the pupil diameter information. Conclusion: Our results offer a quantitative perspective of the limitations of the current solutions and highlight the necessity of developing tailored designs for the telerobotic surgery environment.

"Choose it, and remember it": The impact of choice on destination memory.

Destination memory can be defined as the capacity to remember to whom we transmit information. It is measured through the accuracy of retrieving the association between the information we transmit and the person to whom we transmit it. A destination memory procedure aims to emulate human interaction by sharing facts with celebrities (i.e., familiar faces) since we often communicate with people we know. However, the role of the choice about who we intend to transmit the information to has not been evaluated before. This paper investigated whether deciding with whom to share a piece of information benefits destination memory. We designed two experiments with different levels of cognitive load, increasing it from Experiment 1 to Experiment 2. The experiments included two conditions: the choice condition, in which participants chose from two options to whom they desired to share a fact, and the no-choice condition, in which participants simply shared facts with celebrities without the possibility of a choice. Experiment 1 suggested that a choice component did not affect destination memory. However, when in Experiment 2 we raised the cognitive load by increasing the number of stimuli, we found that selecting the recipient during the more challenging task provided an advantage in destination memory. This result is congruent with the explanation that the shift of the participants' attentional resources to the recipient, caused by the choice component, leads to a destination memory improvement. In sum, it seems that a choice component can improve destination memory only under demanding attentional conditions.

Cognitive workload affects ocular accommodation and pupillary response.

Cognitive involvement in reading causes variations in the tonus of autonomic nerve system. The aim of this study was to examine the effect of short-term cognitive load on accommodation and pupils' absolute values and temporal variability in test persons performing three different types of tasks. We aimed to show how cognitive tasks of different type and difficulty level affect accommodation and pupil behavior during a short time interval. Participants (n=58; mean age 16.4 years, SD=0.56) performed reading from a 10-inch LCD screen placed at 40cm distance. Three different types of tasks (numerical, textual, and the Stroop task), each at three different levels of cognitive load were introduced. Participants had 90s to complete each task. Accommodative and pupillary responses were measured with videoretinoscope Power Refractor 3 at 50Hz. Pupil size was largest in the Stroop task (M=5.20mm, SD=0.75mm), followed by the numerical tasks (M=5.02mm, SD=0.72mm) and textual tasks (M=4.78mm, SD=0.71mm). Accommodative fluctuations - measured as accommodation SD - were largest in the textual tasks (M=0.67 D, SD=0.34 D), followed by the numerical tasks (M=0.61 D, SD=0.40 D) and the Stroop task (M=0.52 D, SD=0.21 D). In our experiment, short-term cognitive load was associated with altered pupillary and accommodative response to near tasks. In conflicting tasks (Stroop) or in performing continuing calculations, the pupils were larger; in tasks requiring logical reasoning, the accommodative fluctuations were greater. These effects can potentially be associated with current near-point stress and myopia growth models.

The effect of biomechanical features on classification of dual-task gait.

Early detection of Alzheimer's Disease and Related Disorders (ADRD) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task was administered at two different inter-stimulus intervals representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated Measures ANOVA showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single vs. Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single vs. Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.

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

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

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

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

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

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

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

The use of multimedia learning is increasing in modern education. On the other hand, it is crucial to design multimedia contents that impose an optimal amount of cognitive load, which leads to efficient learning. Objective assessment of instantaneous cognitive load plays a critical role in educational design quality evaluation. Electroencephalography (EEG) has been considered a potential candidate for cognitive load assessment among neurophysiological methods. In this study, we experiment to collect EEG signals during a multimedia learning task and then build a model for instantaneous cognitive load measurement. In the experiment, we designed four educational multimedia in two categories to impose different levels of cognitive load by intentionally applying/violating Mayer’s multimedia design principles. Thirty university students with homogenous English language proficiency participated in our experiment. We divided them randomly into two groups, and each watched a version of the multimedia followed by a recall test task and filling out a NASA-TLX questionnaire. EEG signals are collected during these tasks. To construct the load assessment model, at first, power spectral density (PSD) based features are extracted from EEG signals. Using the minimum redundancy - maximum relevance (MRMR) feature selection approach, the best features are selected. In this way, the selected features consist of only about 12% of the total number of features. In the next step, we propose a scoring model using a support vector machine (SVM) for instantaneous cognitive load assessment in 3s segments of multimedia. Our experiments indicate that the selected feature set can classify the instantaneous cognitive load with an accuracy of 84.5 ± 2.1%. The findings of this study indicate that EEG signals can be used as an appropriate tool for measuring the cognitive load introduced by educational videos. This can be help instructional designers to develop more effective content.

Effects of inhibitory control capacity and cognitive load on involuntary past and future thoughts: A laboratory study

The present study focused on involuntary thoughts about personal past events (i.e., involuntary autobiographical memories; IAMs), and involuntary thoughts about future events and plans (i.e., involuntary future thoughts; IFTs). The frequency of these involuntary thoughts is influenced by cognitive demands of ongoing activities, but the exact underlying mechanism(s) has yet to be revealed. The present study tested two possible explanations: (1) the special inhibitory mechanism switches on when one is engaged in attentionally demanding activities; (2) different levels of cognitive load interfere with cue-noticing that act as triggers for IAMs and IFTs. We report a study with pre-selected groups of participants that differed in terms of their individual level of inhibitory control capacity (high vs. low), and completed both standard and attentionally demanding versions of a laboratory vigilance task with irrelevant cue-words to trigger IAMs and IFTs, and random thought-probes to measure their frequency. To examine the level of incidental cue-noticing, participants also completed an unexpected cue-recognition task. Despite large differences between groups in inhibitory control capacity, the number of IFTs and IAMs, reported in the attentionally demanding condition, was comparable. In addition, high cognitive load reduced the number of IAMs, but not IFTs. Finally, the recognition of incidental cues encountered in the vigilance task was reduced under high cognitive load condition, indicating that poor cue-noticing may be the main underlying mechanism of cognitive load effect rather than the lack of inhibitory resources needed to suppress involuntary retrieval. This and other possible mechanisms and avenues for future research are discussed.

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers’ cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload.

Quiet Eye and Computerized Precision Tasks in First-Person Shooter Perspective Esport Games

The gaze behavior in sports and other applied settings has been studied for more than 20 years. A common finding is related to the “quiet eye” (QE), predicting that the duration of the last fixation before a critical event is associated with higher performance. Unlike previous studies conducted in applied settings with mobile eye trackers, we investigate the QE in a context similar to esport, in which participants click the mouse to hit targets presented on a computer screen under different levels of cognitive load. Simultaneously, eye and mouse movements were tracked using a high-end remote eye tracker at 300 Hz. Consistent with previous studies, we found that longer QE fixations were associated with higher performance. Increasing the cognitive load delayed the onset of the QE fixation, but had no significant influence on the QE duration. We discuss the implications of our results in the context of how the QE is defined, the quality of the eye-tracker data, and the type of analysis applied to QE data.

The Effect of Physical Activity with Different Levels of Cognitive Load on Executive Control Network of Attention in Youth

The Effect of Physical Activity with Different Levels of Cognitive Load on Executive Control Network of Attention in Youth

Cognitive load and the effectiveness of distraction for acute pain in children.

Distraction tasks that place continuous, high demand on executive resources have been shown to reduce pain intensity and pain unpleasantness ratings in some healthy adult samples. We examined the effects of a high-demand 'working memory' 1-back task compared to a low-demand 'motor control' task on pain intensity and unpleasantness ratings in healthy children. Additionally, dispositional mindfulness was examined to explore the mechanisms of distraction on the affective processing of pain. Fifty-seven children (9-13years old) experienced three randomly presented heat levels (not painful, slightly painful, moderately painful) during two distraction conditions involving different levels of cognitive load (a high load 'working memory' task and a low load 'motor' control task) in counter-balanced order. Children completed measures of dispositional mindfulness, and attentional control and emotional control. As predicted, children's pain intensity and pain unpleasantness ratings were lower in the high load condition compared to the low load condition. These differences were amplified in the moderately painful heat trials. In contrast with predictions, dispositional mindfulness did not significantly predict the effectiveness of distraction. Dispositional mindfulness was significantly related to measures of children's attentional and emotional control abilities; however, an exploratory serial mediation model did not produce significant indirect or overall effects to suggest a strong influence of mindfulness on the effectiveness of distraction. Results demonstrate that distraction that places higher demand on executive resources is more effective for acute pain management for children. Further research is needed to explore cognitive and affective moderators of the effectiveness of distraction for children. This study is one of the first to demonstrate that working-memory engagement can attenuate pain intensity and pain unpleasantness in children aged 9-13. The findings suggest that distraction tasks used in clinical settings for moderately painful medical procedures may benefit more children if they are adequately demanding of cognitive resources.

Classification of Cognitive Load Based on Neurophysiological Features From Functional Near-Infrared Spectroscopy and Electrocardiography Signals on n-Back Task

Cognitive load can be estimated using individuals' task performance, their subjective measures, and neurophysiological measures. Neurophysiological measures, which among others include brain activation signals obtained with various brain imaging techniques, such as the functional near-infrared spectroscopy (fNIRS), and signals from the peripheral physiology, such as the electrocardiography (ECG) signal, allow an objective and continuous estimation of cognitive load. In this article, the fNIRS and ECG signals were simultaneously collected from 32 participants and used to classify three levels of cognitive load on n-back task. A set of 30 fNIRS and ECG features proposed in this article enables the classification of different levels of cognitive load on n-back task using the support vector machine (SVM), k-nearest neighbors (KNN), and linear discriminant analysis (LDA) classification models. When combining the fNIRS and ECG features, three difficulties of the n-back task were classified with the mean accuracies ranging from 61% to 67%, while two difficulties were classified with the mean accuracy ranging from 70% to 84%. The most important features in the classification are discussed. The results presented in this article extend the existing empirical evidence that combining brain imaging and peripheral physiology features increases the accuracy of multi-level cognitive load classification, thus further underscoring the importance of multimodal approach to cognitive load classification.

The Role of Situation Criticality in Affecting the Effect of Cognitive Load on Drivers’ Brake responses: A Driving Simulator Based Study

A dramatic increase in talking on the phone whilst driving has been seen over the past decades, which posed a significant safety threat on the whole society consequently. Studies on the topic regarding the effect of phone conversations on drivers’ driving performances have never come to a cease, especially on the studies of drivers’ brake response times. However, few studies focus on the relationship between situation criticality and the effect of cognitive load on drivers’ brake responses. To better understand it, a driving simulator experiment with two braking scenarios corresponding to two levels of situation criticality was conducted in this study. Participants were asked to follow a lead vehicle as they normally did and answer arithmetic problems (simple and complex) in three phone modes (baseline, hands-free, and handheld) in the meantime. Drivers’ brake response times to the lead vehicle under five conditions were collected and fitted in accumulator models, in which visual looming and brake lights onset were included as the sensory cues. Results demonstrated that the previously proposed mechanistically explicit simulation model was able to predict drivers’ brake response times on different levels of cognitive load and the increased effect of cognitive load on drivers’ brake response times in less critical situations was demonstrated in this paper as well.

Predicting driver takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers’ takeover performance before the issue of a takeover request (TOR) by analyzing drivers’ physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers’ physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers’ takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers’ takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.