Formalising Human Mental Workload as a Defeasible Computational Concept
Citation:
Luca Longo, Formalising Human Mental Workload as a Defeasible Computational Concept, Trinity College Dublin, 2014Download Item:
Longo_Luca_PhD_Trinity_College_Dublin.pdf (Accepted for publication (author's copy) - Peer Reviewed) 9.348Mb
Abstract:
Human mental workload has gained importance, in the last few decades, as a fundamental design concept
in human-computer interaction. It can be intuitively defined as the amount of mental work necessary for
a person to complete a task over a given period of time. For people interacting with interfaces, computers
and technological devices in general, the construct plays an important role. At a low level, while processing
information, often people feel annoyed and frustrated; at higher level, mental workload is critical and
dangerous as it leads to confusion, it decreases the performance of information processing and it increases
the chances of errors and mistakes. It is extensively documented that either mental overload or underload
negatively affect performance. Hence, designers and practitioners who are ultimately interested in system or
human performance need answers about operator workload at all stages of system design and operation. At
an early system design phase, designers require some explicit model to predict the mental workload imposed
by their technologies on end-users so that alternative system designs can be evaluated. However, human
mental workload is a multifaceted and complex construct mainly applied in cognitive sciences. A plethora of
ad-hoc definitions can be found in the literature. Generally, it is not an elementary property, rather it emerges
from the interaction between the requirements of a task, the circumstances under which it is performed and
the skills, behaviours and perceptions of the operator. Although measuring mental workload has advantages
in interaction and interface design, its formalisation as an operational and computational construct has not
sufficiently been addressed. Many researchers agree that too many ad-hoc models are present in the literature
and that they are applied subjectively by mental workload designers thereby limiting their application in
different contexts and making comparison across different models difficult.
This thesis introduces a novel computational framework for representing and assessing human mental
workload based on defeasible reasoning. The starting point is the investigation of the nature of human
mental workload that appears to be a defeasible phenomenon. A defeasible concept is a concept built upon
a set of arguments that can be defeated by adding additional arguments. The word ���defeasible�۪ is inherited
from defeasible reasoning, a form of reasoning built upon reasons that can be defeated. It is also known as
non-monotonic reasoning because of the technical property (non-monotonicity) of the logical formalisms
that are aimed at modelling defeasible reasoning activity. Here, a conclusion or claim, derived from the
application of previous knowledge, can be retracted in the light of new evidence. Formally, state-of-the-art
defeasible reasoning models are implemented employing argumentation theory, a multi-disciplinary paradigm
that incorporates elements of philosophy, psychology and sociology. It systematically studies how arguments
can be built, sustained or discarded in a reasoning process, and it investigates the validity of their conclusions Since mental workload can be seen as a defeasible phenomenon, formal defeasible argumentation theory
may have a positive impact in its representation and assessment. Mental workload can be captured, analysed,
and measured in ways that increase its understanding allowing its use for practical activities. The research
question investigated here is whether defeasible argumentation theory can enhance the representation of
the construct of mental workload and improve the quality of its assessment in the field of human-computer
interaction.
In order to answer this question, recurrent knowledge and evidence employed in state-of-the-art mental
workload measurement techniques have been reviewed in the first place as well as their defeasible and
non-monotonic properties. Secondly, an investigation of the state-of-the-art computational techniques for
implementing defeasible reasoning has been carried out. This allowed the design of a modular framework
for mental workload representation and assessment. The proposed solution has been evaluated by comparing
the properties of sensitivity, diagnosticity and validity of the assessments produced by two instances of the
framework against the ones produced by two well known subjective mental workload assessments techniques
(the Nasa Task Load Index and the Workload Profile) in the context of human-web interaction. In detail,
through an empirical user study, it has been firstly demonstrated how these two state-of-the-art techniques can
be translated into two particular instances of the framework while still maintaining the same validity. In other
words, the indexes of mental workload inferred by the two original instruments, and the ones generated by
their corresponding translations (instances of the framework) showed a positive and nearly perfect statistical
correlation. Additionally, a new defeasible instance built with the framework showed a better sensitivity and
a higher diagnosticity capacity than the two selected state-of-the art techniques. The former showed a higher
convergent validity with the latter techniques, but a better concurrent validity with performance measures.
The new defeasible instance generated indexes of mental workload that better correlated with the objective
time for task completion compared to the two selected instruments. These findings support the research
question thereby demonstrating how defeasible argumentation theory can be successfully adopted to support
the representation of mental workload and to enhance the quality of its assessments.
The main contribution of this thesis is the presentation of a methodology, developed as a formal modular
framework, to represent mental workload as a defeasible computational concept and to assess it as a numerical
usable index. This research contributes to the body of knowledge by providing a modular framework built
upon defeasible reasoning and formalised through argumentation theory in which workload can be optimally
measured, analysed, explained and applied in different contexts.
Sponsor
Grant Number
Irish Research Council for Science and Engineering Technology (IRCSET)
Author's Homepage:
http://people.tcd.ie/llongoDescription:
PUBLISHED
Author: LONGO, LUCA
Publisher:
Trinity College DublinType of material:
ThesisCollections:
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Full text availableKeywords:
Mental Workload, Defeasible Reasoning,, Argumentation TheorySubject (TCD):
Creative Technologies , Intelligent Content & Communications , Artificial Intelligence , Artificial Intelligence/Cybernetics , Computer Science/Engineering , DESIGN , DESIGN METHODS AND AIDS , Human Factors , Human Factors in Engineering , Internet technologies , Knowledge Management , Knowledge and data engineering , NONMONOTONIC REASONING , REASONING , WORKLOAD , argumentation theory , defeasible reasoning , membership functions , mental workloadLicences: