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The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type of reasoning is considered for modelling MWL, given the intrinsic uncertainty involved in assessing it. In particular, it is shown how the NASA − TLX and the WP can be translated into defeasible structures whose inferences can achieve similar validity of the original instruments, even when less information is available. It is also discussed how these structures can have a higher extensibility and how their inferences are more self-explanatory than the ones produced by the NASA − TLX and WP.
Rizzo, L., Longo, L. (2017) Representing and inferring mental workload via defeasible reasoning: a comparison with the NASA Task Load Index and the Workload Profile. In: 1st Workshop on Advances In Argumentation In Artificial Intelligence, Bari, Italy, 2017. pp. 126-140.