Modeling Mental Workload Via Rule-Based Expert System: A Comparison with NASA-TLX and Workload Profile
In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and
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Abstract. In the last few decades several fields have made use of the construct of human mental workload (MWL) for system and task design as well as for assessing human performance. Despite this interest, MWL remains a nebulous concept with multiple definitions and measurement techniques. State-of-the-art models of MWL are usually ad-hoc, considering different pools of pieces of evidence aggregated with different inference strategies. In this paper the aim is to deploy a rule-based expert system as a more structured approach to model and infer MWL. This expert system is built upon a knowledge-base of an expert and translates into computable rules. Different heuristics for aggregating these rules are proposed and they are elicited using inputs gathered in an user study involving humans performing web-based tasks. The inferential capacity of the expert system, using the proposed heuristics, is compared against the one of two ad-hoc models, commonly used in psychology: the NASA-Task Load Index and the Workload Profile assessment technique. In detail, the inferential capacity is assessed by a quantification of two properties commonly used in psychological measurement: sensitivity and validity. Results show how some of the designed heuristics can over perform the baseline instruments suggesting that MWL modelling using expert system is a promising avenue worthy of further investigation. Keywords: Rule-based expert system
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· Mental workload · Heuristics
Introduction
Mental workload (MWL) is a multi-faceted phenomenon with no clear and widely accepted definition. Intuitively, it can be described as the amount of cognitive work expended to a certain task during a given period of time. However, this is a simplistic definition and other factors such as stress, time pressure and mental effort can all influence MWL [11]. The principal reason for measuring MWL is to quantify the mental cost of performing a task in order to predict operator and system performance [1]. It is an important construct, mainly used in the fields of c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 215–229, 2016. DOI: 10.1007/978-3-319-44944-9 19
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psychology and ergonomics, mainly with application in aviation and automobile industries [5,20] and in interface and web design [15,16,23]. According to Young and Stanton, underload and overload can weaken performance [28]. However, optimal workload has a positive impact on user satisfaction, system success, productivity and safety [12]. Often the information necessary for modelling the construct of MWL is uncertain, vague and contradictory [13]. State-of-the-art measurement techniques do not take into consideration the inconsistency of data used in the modelling phase, which might lead to contradictions and loss of information. For example, if the time spent on a certain task is low it can be derived that the overall MWL is a
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