One of the primary goals behind building interactive technologies is managing the cognitive load or mental workload experienced by end-users. The primary motivation is optimising their performance, enhancing engagement, and minimising errors. Any human activity, even the most rudimentary physical or cognitive tasks, involves some degree of mental processing. Therefore, it would have some amount of cognitive load involved. Technological advances of the last two decades have shaped human-computer interaction, requiring more cognitive processing and less physical engagement. A critical evaluation of the current understanding of cognitive load and mental workload and identifying key areas of progress is important because it can aid the design of interactive technologies. Cognitive load/mental workload measurement is one of the vital keys to developing new technologies and user interfaces that maximise human performance.
Defining, measuring, and modelling the construct of cognitive load is far from a trivial task. Self-reporting and task measures, mainly used within psychology, pedagogy, and human factors, have proven to support an overall analysis of the cognitive load experienced on a task. This is because they are mainly gathered before or post-task execution. However, they are not diagnostic over time and minimally support the development of accurate models of cognitive load that account for temporal dynamics. Therefore, a greater research effort should be devoted to applying physiological measures such as those used within neuroscience to advance the science behind cognitive load. These include measures gathered using electroencephalography (EEG), magnetic resonance imaging (MRI), and functional near-infrared spectroscopy (fNIRS), just to mention a few, potentially offering diagnostical power thanks to their capacity to gather brain responses continuously over time. Additionally, given the non-stationarity nature of such signals, modern statistical and machine-learning methods can prove useful for modelling cognitive load. Other physiological measures include those gathered with eye-tracking and other sensor-based wearable technologies.
Modelling cognitive load successfully and its application in ecological settings with non-invasive technologies can greatly impact human behaviour analysis. In detail, understanding cognitive load in real-world environments can lead to the design of better and safer interactive technologies with a significant impact on improving the condition and performance of operators in working environments. It can also contribute to better living and social and professional interactions. For example, spotting conditions of extremely low or high cognitive load experienced by operators can trigger adaptive automation, thus preventing boredom or fatigue and, in the long term, contributing to well-being.
This Research Topic aims to publish rigorously peer-reviewed research on cognitive load, its theories, measures, models, and applications. Original Research articles are of special interest, but Review, Perspective, and Opinion articles that discuss and debate timely topics on cognitive load research are also welcome. It seeks to integrate and cross-link research on the construct of cognitive load, specifically with methods employed within neuroscience. It will promote and foster the understanding of its link with human performance, attention, and behaviour.
Areas covered by this section include but are not limited to
• Theories and models of cognitive load and mental workload (e.g., Multiple Resource Theory)
• Application of physiological measurement techniques (e.g., EEG, fMRI, MRI), especially mobile brain activity measurement methods (e.g. eye tracking and wearable sensors) in the context of Human-Machine Interaction (HMI)
• Models of workload that employ psychological techniques (e.g., NASA-TLX, Workload Profile, SWAT)
• Analytical models for cognitive load modelling employing statistical, machine and deep learning methods
• Applications of online and offline models of cognitive load in ecological settings (e.g., educational psychology, human-media interaction)
• Cognitive load-based technologies and solutions for understanding the human brain at work in everyday life (e.g., cognitive neuro ergonomics, neuroinformatics and neuroscience)
• Application of passive brain-computer interfaces (pBCI) in the context of cognitive activation and cognitive load modelling
• Applications of novel or existing models of cognitive load grounded on cognitive load theory, pedagogy, teaching and learning, and its application for instructional efficiency
• Cognitive load and ethical considerations in the development of interactive, wearable and monitoring technologies
One of the primary goals behind building interactive technologies is managing the cognitive load or mental workload experienced by end-users. The primary motivation is optimising their performance, enhancing engagement, and minimising errors. Any human activity, even the most rudimentary physical or cognitive tasks, involves some degree of mental processing. Therefore, it would have some amount of cognitive load involved. Technological advances of the last two decades have shaped human-computer interaction, requiring more cognitive processing and less physical engagement. A critical evaluation of the current understanding of cognitive load and mental workload and identifying key areas of progress is important because it can aid the design of interactive technologies. Cognitive load/mental workload measurement is one of the vital keys to developing new technologies and user interfaces that maximise human performance.
Defining, measuring, and modelling the construct of cognitive load is far from a trivial task. Self-reporting and task measures, mainly used within psychology, pedagogy, and human factors, have proven to support an overall analysis of the cognitive load experienced on a task. This is because they are mainly gathered before or post-task execution. However, they are not diagnostic over time and minimally support the development of accurate models of cognitive load that account for temporal dynamics. Therefore, a greater research effort should be devoted to applying physiological measures such as those used within neuroscience to advance the science behind cognitive load. These include measures gathered using electroencephalography (EEG), magnetic resonance imaging (MRI), and functional near-infrared spectroscopy (fNIRS), just to mention a few, potentially offering diagnostical power thanks to their capacity to gather brain responses continuously over time. Additionally, given the non-stationarity nature of such signals, modern statistical and machine-learning methods can prove useful for modelling cognitive load. Other physiological measures include those gathered with eye-tracking and other sensor-based wearable technologies.
Modelling cognitive load successfully and its application in ecological settings with non-invasive technologies can greatly impact human behaviour analysis. In detail, understanding cognitive load in real-world environments can lead to the design of better and safer interactive technologies with a significant impact on improving the condition and performance of operators in working environments. It can also contribute to better living and social and professional interactions. For example, spotting conditions of extremely low or high cognitive load experienced by operators can trigger adaptive automation, thus preventing boredom or fatigue and, in the long term, contributing to well-being.
This Research Topic aims to publish rigorously peer-reviewed research on cognitive load, its theories, measures, models, and applications. Original Research articles are of special interest, but Review, Perspective, and Opinion articles that discuss and debate timely topics on cognitive load research are also welcome. It seeks to integrate and cross-link research on the construct of cognitive load, specifically with methods employed within neuroscience. It will promote and foster the understanding of its link with human performance, attention, and behaviour.
Areas covered by this section include but are not limited to
• Theories and models of cognitive load and mental workload (e.g., Multiple Resource Theory)
• Application of physiological measurement techniques (e.g., EEG, fMRI, MRI), especially mobile brain activity measurement methods (e.g. eye tracking and wearable sensors) in the context of Human-Machine Interaction (HMI)
• Models of workload that employ psychological techniques (e.g., NASA-TLX, Workload Profile, SWAT)
• Analytical models for cognitive load modelling employing statistical, machine and deep learning methods
• Applications of online and offline models of cognitive load in ecological settings (e.g., educational psychology, human-media interaction)
• Cognitive load-based technologies and solutions for understanding the human brain at work in everyday life (e.g., cognitive neuro ergonomics, neuroinformatics and neuroscience)
• Application of passive brain-computer interfaces (pBCI) in the context of cognitive activation and cognitive load modelling
• Applications of novel or existing models of cognitive load grounded on cognitive load theory, pedagogy, teaching and learning, and its application for instructional efficiency
• Cognitive load and ethical considerations in the development of interactive, wearable and monitoring technologies