Model-based development of a mental workload-sensitivity index for subject clustering. (1st November 2020)

• Record Type: Journal Article
• Authors: Fürstenau, Norbert; Radüntz, Thea; Mühlhausen, Thorsten
• Is Part Of: Theoretical issues in ergonomics science. Volume 21:Number 6(2020)
• Page Start: 684
• Page End: 708
• Publication Date: 2020-11-01
• Subjects: Mental workload -- resource limitation -- sensitivity index -- instantaneous self-assessment -- logistic model -- validation -- participant clustering; Workload sensitivity index -- theory and validation; Human engineering -- Periodicals; Human engineering -- Research -- Periodicals; 620.82
• DOI: 10.1080/1463922X.2020.1711990
• Languages: English
• ISSNs: 1463-922X

Abstract

Abstract: Considering individual differences are common praxis in several research areas and subjects' clustering is important to improve knowledge. The concept of mental workload (WL) comprises a number of individual characteristics but is rarely used for subject clustering. In our article, we introduce an approach for the calculation of a WL-sensitivity index that can be used for such purpose. Based on the hypothesis of cognitive resource limitation, we present a two-parametric (nonlinear) logistic model that predicts WL-sensitivity parameters for sample means across participants of a subjective WL measure. It takes into account the specific scale limits of WL metrics, and integrates domain expert knowledge as prior information. Experimental evidence is provided by means of a human-in-the-loop simulation experiment with air traffic controllers. The WL effects were measured using subjective 'Instantaneous Self-Assessment' (ISA) under eight different task load scenarios realised by variation of traffic flow n and a dichotomous non-nominal (priority) event e . Analysis of the ISA( n ) data shows that the theoretically predicted ISA vs. n characteristic exhibits good agreement with the experimental parameter estimates when based on the ISA scenario averages despite large inter-individual variance. For those scenarios including the event ( e = 1), a significant increase of WL sensitivity is observed for n > critical load nx that gives rise to an additional nonlinearity. Moreover, for the given traffic load range, our two-parameter model may be linearised so that in a simple way participant subgroups of different WL sensitivity may be defined based on a linear dimensionless ISA( n ) sensitivity index.