Model-based development of a mental workload-sensitivity index for subject clustering
| Autor: | Thorsten Mühlhausen, Thea Radüntz, Norbert Fürstenau |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Index (economics)
Computer science media_common.quotation_subject Human Factors and Ergonomics instantaneous self assessment Logistic regression Machine learning computer.software_genre workload subject clustering 0502 economics and business Model-based design resource limitation 0501 psychology and cognitive sciences Sensitivity (control systems) Cluster analysis 050107 human factors logistic model media_common validation 050210 logistics & transportation Praxis business.industry 05 social sciences Subject (documents) Workload sensitivity index Artificial intelligence business computer |
| DOI: | 10.6084/m9.figshare.11720193.v1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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