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As industries progress toward integrating more complex technologies within Industry 4.0 frameworks, ensuring work instructions that balance cognitive load and performance is increasingly critical, especially under the human-centric principles of the 5th industrial revolution. Drawing on Cognitive Load Theory (CLT), this study compares two instructional methods-visual-based and code-based-to determine whether cognitive overload can be reduced without compromising task outcomes in a controlled, assembly-like scenario derived from industrial tasks. We recruited 30 participants from the academic field (students and researchers), who completed assembly tasks under both visual-based and code-based instructions. Cognitive load was measured objectively by (Galvanic Skin Response, Heart Rate Variability, and hand motion acceleration) and subjectively through (NASA Task Load Index, short Dundee Stress State Questionnaire). Operational efficiency was assessed via task completion time (TCT), number of task repetitions (NTR), and assembly precision based on the standard deviation. The findings demonstrated that visual-based instructions significantly reduced cognitive load with a Formula: see text. It also showed an improvement in two of the performance metrics during the use of visual-based instructions for the TCT and NTR with Formula: see text. However, although code-based instructions increased cognitive load, they showed better assembly precision with a Formula: see text. These results suggest that while simple and direct instructions facilitate task execution and reduce cognitive loads, deep thinking approaches may still hold value for tasks requiring high precision.
Eesee et al. (Tue,) studied this question.