Estimating mental workload in executive function-based tasks

This article presents a theoretical framework for enhancing manufacturing productivity and safety by integrating cognitive ergonomics and artificial intelligence (AI). The framework addresses the critical gap in current research for a holistic approach to managing cognitive workload by proposing an integrated approach to managing cognitive workload and individual differences in modern manufacturing contexts. Despite the growing recognition of cognitive ergonomics, existing frameworks often fail to provide a unified model that incorporates AI and adaptive systems to optimize worker performance without negatively impacting mental workload or well-being. This paper proposes a comprehensive five-step theoretical framework integrating cognitive ergonomics principles, AI, and adaptive systems to optimize worker performance and productivity in manufacturing environments. The framework begins with assessing manufacturing operations and evaluates mental resource allocation to prevent overload. It optimizes cognitive and physical workload through real-time monitoring, integrates AI for dynamic task allocation, and establishes continuous feedback loops to adapt task demands, reduce errors, and enhance safety. This paper aims to contribute to developing predictive models for cognitive workload and tailored technologies for cognitive load reduction, filling a significant gap in existing research. The proposed framework provides a standardized approach to improving manufacturing operations by combining cognitive ergonomics with adaptive automation systems. Ultimately, it aims to promote worker well-being and performance, ensuring that AI and adaptive systems complement, rather than detract from, cognitive capacity while reducing cognitive workload, mental pressure, and performance declines in high-workload environments.

This study deals with evaluation of mental workload for machine operators using vital reaction especially to use the heart rate variability. The frequency analysis for R-R interval of an electrocardiogram is used in order to get the RSA peak which is the typical index for mental workload. In this study we paid attention to this RSA. Therefore the experiment for the driver using driving simulator has been done by change of respiratory frequency and volume as parameters. A linear malt-regression equation by using of the parameters is constructed in order to analyze the effect of them to the RSA. Finally, it is found that the RSA as an index of mental workload has to consider the effect of both frequency and volume of the respiration.

The growing dependence on mobile phones for communication has raised concerns regarding the neurological impact of radio-frequency electromagnetic fields, especially in audio and video calls. This research investigates the impact of WhatsApp audio and video calls on cognitive load and mental fatigue using non-invasive electroencephalography (EEG) signals. EEG signals were recorded from 28 healthy participants during baseline, 3-minute, and 4-minute call sessions, with participants equally distributed between audio and video call groups. The signals were preprocessed using bandpass filtering, continuous wavelet transform, and independent component analysis to isolate theta and alpha frequency bands. Cognitive load was assessed using the theta-alpha ratio (TAR), and mental fatigue was measured using Mahalanobis distance-based analysis of theta and alpha rhythms. The findings reveal that video calls impose significantly more cognitive load (e.g., TAR mean: 0.78 (SD: 0.36) for 3-minute video call and mean: 0.76 (SD: 0.37) for 3-minute audio call, p<0.05) and mental fatigue (e.g., 42.11 microvolts for 4-minute video call and 38.84 microvolts for 4-minute audio call) than audio calls, and both effects become stronger for prolonged durations. Machine learning (ML) classification also demonstrated high separability, with receiver operating characteristic analysis exhibiting area under the curve values above 0.90 for distinguishing call conditions. These results demonstrate that video communication places higher cognitive and fatigue demands as compared to audio communication. By integrating EEG indices, self-report measures, and ML classification, this study exhibits convergent evidence for the cognitive impact of mobile phone calls and illustrates the necessity to consider communication modality when assessing workload and fatigue in real-world contexts.

Dysphagia frequently co-occurs with cognitive impairment, suggesting that swallowing may involve cognitive processes. However, the relationship between cognitive load and swallowing performance remains unclear. This study aimed to examine the effects of different levels of cognitive load on swallowing performance under dual-task conditions in healthy adults. Forty-three graduate students (mean age: 24.49 ± 1.63 years, male/female: 22/21) were recruited and performed five tasks: swallowing single task (SST), low-load cognitive single task (STlow), high-load cognitive single task (SThigh), low-load cognitive-swallowing dual task (DTlow), and high-load cognitive-swallowing dual task (DThigh). Here, a dual-task refers to the concurrent execution of a swallowing task and a cognitive task, designed to evaluate the competition for attentional resources and performance trade-offs. The low-load cognitive task consisted of a digit forward-span task, while the high-load cognitive task involved a digit backward-span task, which demands greater cognitive processing complexity and working memory load. Swallowing measures (e.g., swallowing volume) and cognitive task accuracy were recorded, and dual-task cost (DTC) was calculated to evaluate performance changes under dual-task conditions. Swallowing volume differed significantly among SST, DTlow, and DThigh (p < 0.05), with lower volume in DThigh compared to SST (p < 0.05). Cognitive accuracy was significantly lower in DThigh than in DTlow (p < 0.05), indicating greater interference under high cognitive load. The findings confirm that swallowing engages executive function and attentional resources, and suggest that participants prioritize swallowing performance during dual-task conditions by adjusting prefrontal activation patterns rather than increasing overall cortical activation to manage interference. This study provides preliminary evidence for the cognitive involvement in swallowing and highlights the need to consider cognitive load in dysphagia assessment and rehabilitation.

Motivation drives individuals to overcome costs to achieve desired outcomes, such as rewards or avoidance of punishment, with significant variability across individuals. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and anterior insula are key brain regions implicated in effort-based decision-making. Here, we utilized proton magnetic resonance spectroscopy (1H-MRS) at 7 Tesla on 69 healthy participants in these brain regions to uncover the neurometabolic factors that influence these differences. We designed and applied an effort-based decision-making task requiring mental and physical effort to probe motivated behavior, complemented by computational modeling to extract key behavioral parameters. Gradient boosting machine learning was applied to explore the predictive role of specific metabolites in motivated behavior. Our results reveal that a model established on dmPFC/dACC metabolites explains decisions to exert high mental effort and sensitivity to mental effort. In particular, glutamate, aspartate, and lactate in dmPFC/dACC, three metabolites linked through the tricarboxylic acid cycle and glycolysis, were identified as key discriminative metabolites in the dmPFC/dACC, predictive of mental effort choices, underpinning energy supply and cognitive processes. Anterior insula metabolites did not significantly relate to effort-related decisions. Notably, glutamine and lactate levels between the periphery (plasma) and the dmPFC/dACC were correlated, suggesting a metabolic link between peripheral and central biomarkers of effort. Our findings provide novel insights into the neurometabolic underpinnings of motivated behavior and propose novel biomarkers for mental effort-based decision-making. Importantly, our study highlights the relevance of multivariable approaches in elucidating complex cognitive functions.

Online courses are becoming ubiquitous and increasingly tend to use authentic learning tasks as the driving force for teaching and learning. Nevertheless, designing online courses that incorporate real– world tasks is more challenging as these problems require more cognitive processes (van Merriënboer Sluijsmans, 2009). This phenomenon can be explained by Cognitive Load Theory (CLT) introduced by Sweller (1994). CLT distinguishes three types of cognitive load: intrinsic, extraneous and germane load. The level of intrinsic load is assumed to be determined by the level of element interactivity. An element can be a definition, concept, formula and procedure that needs to be or has been learned. Extraneous load is mainly imposed by instructional procedures that are suboptimal, whereas germane load refers to the learners’ working memory resources available to deal with the complexity of the task or learning material (Sweller, 2010). Accordingly, the experienced cognitive load is mainly dependent of students’ prior knowledge. Nevertheless, cognitive load can also be determined by students’ motivation (Feldon, Franco, Chao, Peugh, Maahs-Fladung, 2018; Verhoeven, Schnotz, Paas, 2009). As a consequence, when designing an online course for complex tasks, it is important to understand how the different types of cognitive load are affected by students’ cognitive and motivational characteristics. Therefore, in the current study, a high and low complex task was developed relating to the learning and teaching of geometry. The complexity of the task was manipulated by increasing the element interactivity for the high complex task (Sweller, 2010). In the low complex task one element was questioned each time, and consequently students had to apply a single rule, formula or procedure. By contrast, the high complex task was based on a real-life context (e.g., teaching geometry), and had higher element interactivity. Subsequently, the high complex task required learners to engage in a series of cognitive activities such as analysing, decision making, implementing and evaluating, while holding several procedures and rules in mind. Accordingly, we expected the high complex task to induce more cognitive load. The same amount of support containing the same content, was provided during both tasks. Consequently, in this context, students could take initiative in diagnosing their learning needs by identifying appropriate support. Since students could consult different amounts of support, this self-directed learning strategy could also influence the perceived cognitive load (van Merriënboer Sluijsmans, 2009). Accordingly, the amount of consulted support was also taken into account during the analyses. The aim of the study was twofold. First, as a manipulation check of task complexity, we investigated differences in the experienced cognitive load while solving a high and low complex task. Secondly, we examined whether students’ cognitive and motivational characteristics influence the different types of perceived cognitive load, when taking into account the amount of consulted support for both the high and low complex task. A multivariate approach was chosen to assess the degree of interplay that may exist among students’ cognitive, motivational characteristics, consultation of support and the different types of perceived cognitive load. By conducting this study, we wanted to gain insight into whether the cognitive, motivational characteristics and consultation of support influence the perceived cognitive load differently for a high and low complex task.

Context: In recent years, several studies explored different facets of the developers' cognitive load while executing tasks related to software engineering. Researchers have proposed and assessed different ways to measure developers' cognitive load at work and some studies have evaluated the interplay between developers' cognitive load and other attributes such as productivity and software quality. Problem: However, the body of knowledge about developers' cognitive load measurement is still dispersed. That hinders the effective use of developers' cognitive load measurements by industry practitioners and makes it difficult for researchers to build new scientific knowledge upon existing results. Objective: This work aims to pinpoint gaps providing a classification and a thematic analysis of studies on the measurement of cognitive load in the context of software engineering. Method: We carried out a Systematic Mapping Study (SMS) based on well-established guidelines to investigate nine research questions. In total, 33 articles (out of 2,612) were selected from 11 search engines after a careful filtering process. Results: The main findings are that (1) 55% of the studies adopted electroencephalogram (EEG) technology for monitoring the cognitive load; (2) 51% of the studies applied machine-learning classification algorithms for predicting cognitive load; and (3) 48% of the studies measured cognitive load in the context of programming tasks. Moreover, a taxonomy was derived from the answers of research questions. Conclusion: This SMS highlighted that the precision of machine learning techniques is low for realistic scenarios, despite the combination of a set of features related to developers' cognitive load used on these techniques. Thus, this gap makes the effective integration of the measure of developers' cognitive load in industry still a relevant challenge.

Purpose The impact of different screen-based typography styles on individuals’ cognitive processing of information has not been given much consideration in the literature, though such differences would imply different learning outcomes. This study aims to enrich the current understanding of the impact of reading in single- and multiple-column types on students’ cognitive processing. Design/methodology/approach An electroencephalogram (EEG) was used to record and analyze the brain signals of 27 students while reading from single- and multiple- column layouts. Findings The results showed a significant difference in students’ cognitive load when reading text from different types of columns. All students exerted less processing efforts when text was presented in two-column format, thus experiencing less cognitive load. Originality/value Using EEG, this study examined the neural consequences of reading in single- and multiple-column types on cognitive load during reading. The findings can be used to enrich the current instructional design practices on how different typographical formats facilitate learners’ cognitive performance.

This experiment investigated the impacts of difficult processing in the context of a health narrative by integrating two distinct literatures: cognitive load and processing fluency. Difficult processing was manipulated via an auditory cue induction of reactive cognitive control (a manipulation of cognitive load) and language difficulty (a manipulation of processing fluency). We examined whether cognitive load can impact persuasion-relevant outcomes, and similarly whether processing fluency can impact memory. Results revealed that the processing fluency manipulation impacted memory, suggesting subjective difficulty was indicative of resource availability, as well as narrative persuasion. Additionally, the processing fluency and cognitive load manipulations interacted such that when the language was difficult, the presence of auditory cues enhanced memory. Our results show that cognitive load and processing fluency interact to shape message processing and effects.

Cognitive load researchers have used varying subjective techniques based on rating scales to quantify experienced cognitive load. Although it is generally assumed that subjects can introspect on their cognitive processes and have no difficulty in assigning numerical values to the imposed cognitive load, little is known about how visual characteristics of the rating scales influence the validity of the cognitive load measure. In this study we look at validity of four subjective rating scales (within groups) differing in visual appearance by participants rating perceived difficulty and invested mental effort in response to working on simple and complex weekday problems. We used two numerical scales (the nine-point Likert scale most often used in Cognitive load theory research and a Visual Analogue Scale ranging between 0–100%) and two pictorial scales (a scale consisting of emoticons ranging from a relaxed blue-colored face to a stressed red-colored face and an “embodied” scale picturing nine depicted weights from 1–9 kg). Results suggest that numerical scales better reflect cognitive processes underlying complex problem solving while pictorial scales Underlying simple problem solving. This study adds to the discussion on the challenges to quantify cognitive load through various measurement methods and whether subtleties in measurements could influence research findings.

This paper introduces the concept of information entropy in studying mental workloads to predict the mental workload of an urban railway dispatcher and thereby ensure safe rail system operation. This study combines factors that can influence mental workload, including visual behaviors required for dispatchers to obtain information, information display duration, and the amount of information in order to establish a comprehensive mental workload prediction model. Experimental monitoring tasks were carried out on a simulation dispatch interface platform to verify the model’s validity. Three assessment methods (task performance assessment, subjective assessment, and physiological assessment) were adopted to measure the mental workload levels of dispatchers under different task conditions. The results demonstrate that the model’s theoretical prediction value significantly correlates with the various experimental results, thereby verifying the model validity and indicating that it can be used to predict the mental workload for different dispatch tasks, to provide a reference for work performance evaluation, and in designing optimized dispatch display interfaces.

Among the variety of synonymously similar scientific concepts used in psychological research (cognitive overload, sensory overload, knowledge overload, information fatigue syndrome, cognitive information load, information overload, cognitive load), we chose the concept of “information overload” for categorical analysis, because it defines the most holistically and complete the corresponding psychological phenomenon, meaningfully reflecting and naming significant realities. The essential signs of this phenomenon are: the external one – “what caused the state of tension” (informational message) – and the internal one - “what mental state appears as a result” (overload). Information overload is defined as a state of an individual’s mental stress caused by received excessive information messages; this excessiveness is experienced subjectively as a stressful situation associated with worsened efficiency of the individual’s cognitive mental processes and causes a mental protective blocking of high-quality deep intellectual processing of incoming information and its transformation into full-fledged personal knowledge. In the context of multi-sensory perception by a media consumer of modern informational sources, two processes are important: coding by an addressee and decoding by an addressee of an information message; accordingly, in the process of media-perceptual communication, information overload becomes a barrier to knowledge formation by an individual perceiving certain information flows. The content and nature of information overload as a psychological phenomenon is revealed in the measurement of the four aspects we have identified: cognitive, behavioral, emotional-motivational and competence. At the cognitive aspect, information overload is considered in the context of an individual's cognitive activities, affected by the functional limitations of cognitive processes (perception, attention, memory, thinking), and is determined through an individual's cognitive style. At the psychological dimension of the behavioral aspect, information overload is interpreted as a way for mass consciousness manipulating with the political or consumer-related context for individuals’ behavior modeling, in particular, during decision making. At the emotional-motivational aspect, information overload is defined as the emotional state of mental stress associated with unexpected and unfavorable informational influences, accompanied by a deterioration of an individual’s professional efficiency. At the psychological dimension of the competence-based aspect, information overload is interpreted as a psychological threat to an individual’s mental health and is defined as underdeveloped media literacy, as a result of used ineffective strategies by a media consumer in modern mediacommunication. As for counteraction, it is necessary to form media literacy of information consumers, increase their personal self-efficacy.

Evaluation of physiological responses to mental workload in n-back and arithmetic tasks

Very short answer questions (VSAQs) have gained attention for their superior psychometric properties compared to multiple-choice questions (MCQs). While VSAQs require knowledge recall, MCQs primarily involve knowledge recognition. This difference in cognitive processes may lead to varying cognitive workloads, defined as the amount of mental processing in working memory. Previous studies have not demonstrated consistent differences, likely due to reliance on self-reported measures. Eye tracking provides objective, process-level indicators of cognitive workload. This study investigated whether answering VSAQs requires a higher cognitive workload than answering MCQs. In a within-subject randomized crossover experiment, sixth-year medical students answered both VSAQs and MCQs. Cognitive workload was measured using screen-based eye tracking, focusing on the number of fixations and revisitations as objective indicators of mental effort. Data were analyzed using mixed-effects models. Thirty-four medical students participated, yielding 1,326 observations, which is the multiplication of the number of students by the number of questions (39 questions). Mixed-effects models showed a significant effect of question type on both workload indicators: VSAQs elicited more fixations and revisitations than MCQs (β_std = 0.30–0.39, p < .001). This effect remained after controlling for accuracy. Incorrect answers were associated with higher workload (β_std = −0.15–−0.16, p < .01). Heatmaps confirmed these findings, showing denser fixations on key diagnostic features for VSAQs and on answer options for MCQs. Answering VSAQs imposed a higher cognitive workload than MCQs. The presence of answer options in MCQs may reduce workload by providing unintentional cues, while VSAQs require active retrieval. Eye tracking proved valuable for distinguishing cognitive workload across assessment formats.

Language in interpreting: Multidimensional evidence on emotional states, cognitive load, and their correlation.