Understanding charts, diagrams, and graphs is required in many domains (e.g., education, business, and healthcare), thus fostering the interpretation and communication of quantitative information. Analytical reasoning and semiotically-informed observation are examples of activities that rely substantially on an individual's understanding of such visual structures. This skill is compromised when an individual's mental load exceeds the limitations of their working memory, impairing their understanding and making them more error-prone.
To explore the mental load of individuals, a number of research have suggested using behavioral and physiological measures. Unfortunately, the majority of available techniques estimate the difficulty of such visual structures at a coarse task level. Hence, they cannot be utilized to identify its most intellectually taxing aspects in detail. To solve this constraint, the research topic shall offer the presentation of innovative scientific research on cognitive neuroscience (e.g., eye tracking, electrodermal activity) that seeks to quantify the understanding of visual structures and inherent aspects.
The research's primary objective is to enable a fine-grained investigation of mental load to, for instance, unravel the aspects that render interpreting such visual structures difficult. This method offers a foundation for innovative understanding and adaptive learning techniques. In addition, it provides the opportunity to study novel hypotheses addressing the characteristics of particular aspects of visual structures.
This Research Topic offers an avenue for presenting novel original research on cognitive aspects during the comprehension of visual structures. We invite submissions that explore various aspects, topics, and issues, including but not limited to the following:
• Theoretical investigations about the systematic collection of processes and phenomena that aren’t directly observable.
• Empirical studies that employ smart sensors such as eye tracking, electrodermal activity (EDA), heart rate, and electroencephalogram (EEG) to measure feedback (e.g., mental load).
• Prototypical implementations of theories and the development of novel techniques in order to measure the mental load.
• Investigation of pattern that are applied by the human cognitive system.
• Guidelines and directives to relieve the working memory (i.e., lower mental load).
• Exploration of intra- and inter-individual traits and their effects.
• Machine-learning-driven approaches on the human cognitive system.
• Deployment of artificial cognitive systems imitating cognitive and behavioral activities.
• Investigations that explore the combination of existing theories from various domains – inter alia - software engineering and process modeling with neuroscience and cognitive psychology.
Understanding charts, diagrams, and graphs is required in many domains (e.g., education, business, and healthcare), thus fostering the interpretation and communication of quantitative information. Analytical reasoning and semiotically-informed observation are examples of activities that rely substantially on an individual's understanding of such visual structures. This skill is compromised when an individual's mental load exceeds the limitations of their working memory, impairing their understanding and making them more error-prone.
To explore the mental load of individuals, a number of research have suggested using behavioral and physiological measures. Unfortunately, the majority of available techniques estimate the difficulty of such visual structures at a coarse task level. Hence, they cannot be utilized to identify its most intellectually taxing aspects in detail. To solve this constraint, the research topic shall offer the presentation of innovative scientific research on cognitive neuroscience (e.g., eye tracking, electrodermal activity) that seeks to quantify the understanding of visual structures and inherent aspects.
The research's primary objective is to enable a fine-grained investigation of mental load to, for instance, unravel the aspects that render interpreting such visual structures difficult. This method offers a foundation for innovative understanding and adaptive learning techniques. In addition, it provides the opportunity to study novel hypotheses addressing the characteristics of particular aspects of visual structures.
This Research Topic offers an avenue for presenting novel original research on cognitive aspects during the comprehension of visual structures. We invite submissions that explore various aspects, topics, and issues, including but not limited to the following:
• Theoretical investigations about the systematic collection of processes and phenomena that aren’t directly observable.
• Empirical studies that employ smart sensors such as eye tracking, electrodermal activity (EDA), heart rate, and electroencephalogram (EEG) to measure feedback (e.g., mental load).
• Prototypical implementations of theories and the development of novel techniques in order to measure the mental load.
• Investigation of pattern that are applied by the human cognitive system.
• Guidelines and directives to relieve the working memory (i.e., lower mental load).
• Exploration of intra- and inter-individual traits and their effects.
• Machine-learning-driven approaches on the human cognitive system.
• Deployment of artificial cognitive systems imitating cognitive and behavioral activities.
• Investigations that explore the combination of existing theories from various domains – inter alia - software engineering and process modeling with neuroscience and cognitive psychology.
