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

Nancy Ivette Arana-De Las Casas1,2, Jorge De la Riva-Rodríguez1, Aide Aracely Maldonado-Macías1

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Summary

This study designed a graphical user interface (GUI) to measure mental workload using Dual N-Back tasks. Cognitive analysis and NASA-TLX results identified GUI improvements for better human-system interaction in manufacturing.

Keywords:
NASA-TLXhierarchical task analysis (HTA)mental workload (MWL)task analysis for error identification (TAFEI)

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Area of Science:

  • Human-Computer Interaction
  • Cognitive Engineering
  • Manufacturing Systems

Background:

  • Modern manufacturing relies on complex human-machine systems, increasing worker mental workload.
  • Effective graphical user interface (GUI) design is crucial for managing cognitive load and preventing errors.

Purpose of the Study:

  • To design and evaluate a GUI engineered to induce varying levels of mental workload.
  • To analyze human performance and identify potential errors within the GUI during cognitive tasks.
  • To enhance user-system interaction through cognitive analysis and interface redesign.

Main Methods:

  • Utilized Hierarchical Task Analysis and Task Analysis Method for Error Identification for cognitive assessment.
  • Employed Dual N-Back tasks within the GUI to systematically induce mental workload.
  • Collected subjective mental workload data using the NASA-Task Load Index (NASA-TLX) questionnaire.

Main Results:

  • Statistical analysis (ANOVA) confirmed the GUI successfully induced distinct mental workload levels (Low, Medium, High).
  • Subjective NASA-TLX scores correlated with induced workload levels, validating the experimental design.
  • Cognitive analysis revealed specific areas within the GUI requiring redesign to improve user interaction and reduce error potential.

Conclusions:

  • The developed GUI effectively manipulates mental workload for performance analysis.
  • Cognitive analysis of the GUI provides actionable insights for interface optimization.
  • This research contributes to designing more efficient and error-resistant human-machine systems in manufacturing.