Cognitive Analyses for Interface Design Using Dual N-Back Tasks for Mental Workload (MWL) Evaluation

In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back...

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Bibliographic Details
Published in:International journal of environmental research and public health 2023-01, Vol.20 (2), p.1184
Main Authors: Arana-De Las Casas, Nancy Ivette, De la Riva-Rodríguez, Jorge, Maldonado-Macías, Aide Aracely, Sáenz-Zamarrón, David
Format: Article
Language:English
Subjects:
Cognition
Error analysis
Human error
Human performance
Humans
Man-Machine Systems
Methods
Physiology
Productivity
Redesign
Surveys and Questionnaires
Task analysis
Task Performance and Analysis
User interface
Variance analysis
Workers
Workload
Workload - psychology
Workloads
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Summary:In the manufacturing environments of today, human-machine systems are constituted with complex and advanced technology, which demands workers' considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study's contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user-system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants' mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph20021184