Model-based development of a mental workload-sensitivity index for subject clustering

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.