Cognitive demands and mental workload: A filed study of the mining control room operators

Abstract

Cognitive demand and mental workload assessment are essential for the optimal interaction of human-machine systems. The aim of this study was to investigate the cognitive demands and mental workload as well as the relationship between them among the mining control room operators. This cross-sectional study was performed on 63 control room operators of a large mining plant located in Iran. Cognitive demands and mental workload were assessed using cognitive task analysis (CTA) and NASA Task Load Index (NASA-TLX), respectively and the analysis was performed using SPSS version 21. Independent samples T-test, Mann-Whitney U test and multivariate linear regression were used for data analysis. Twelve cognitive demands were extracted after observing the tasks and conducting semi-structured interviews with the control room staff. The mean scores of total cognitive demands and MWL were 6.60 and 72.89, respectively, and these two indicators showed a positive and significant correlation (r = 0.286; P = 0.023). The participants' demographic characteristics such as age, education, and work experience did not affect mental workload, but the two cognitive demands (memory and defect detection) affected MWL. High cognitive demands and mental workload indicate poor interaction between humans and machines. Due to the effect of memory load and defect detection on mental workload, it is recommended to assign cognitive tasks based on memory and defect detection to the machine to reduce the mental workload and improve human-machine interaction.

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