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Full-Text Articles in Engineering
Comparing Anova And Powershap Feature Selection Methods Via Shapley Additive Explanations Of Models Of Mental Workload Built With The Theta And Alpha Eeg Band Ratios, Bujar Raufi, Luca Longo
Comparing Anova And Powershap Feature Selection Methods Via Shapley Additive Explanations Of Models Of Mental Workload Built With The Theta And Alpha Eeg Band Ratios, Bujar Raufi, Luca Longo
Articles
Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At …
Corrigendum: Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock
Corrigendum: Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P. A. Hancock
Articles
In the published article, the name of Gabriella Hancock was incorrectly written as “Gabriela M. Hancock.” The correct name is “Gabriella Hancock.” In the published article, there was also an error in the author list as published. Gabriella Hancock was listed as the last author, but should have been listed as third author. P. A. Hancock was listed as third author but should be listed as the last author. The corrected author list appears below. Luca Longo1, Christopher D.Wickens, Gabriella Hancock and P. A. Hancock. The authors apologize for this error and state that this does not change the scientific …
Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P.A. Hancock
Human Mental Workload: A Survey And A Novel Inclusive Definition, Luca Longo, Christopher D. Wickens, Gabriella Hancock, P.A. Hancock
Articles
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and …
Modeling Cognitive Load As A Self-Supervised Brain Rate With Electroencephalography And Deep Learning, Luca Longo
Modeling Cognitive Load As A Self-Supervised Brain Rate With Electroencephalography And Deep Learning, Luca Longo
Articles
The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing …
Examining The Modelling Capabilities Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning, Luca Longo, Lucas Rizzo, Pierpaolo Dondio
Examining The Modelling Capabilities Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning, Luca Longo, Lucas Rizzo, Pierpaolo Dondio
Articles
Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload …
A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran
A Comparison Of Instructional Efficiency Models In Third Level Education, Murali Rajendran
Dissertations
This study investigates the validity and sensitivity of a novel model of instructional efficiency: the parabolic model. The novel model is compared against state-of-the-art models present in instructional design today; Likelihood model, Deviational model and Multidimensional model. This models is based on the assumption that optimal mental workload and high performance leads to high efficiency, while other models assume that low mental workload and high performance leads to high efficiency. The investigation makes use of two instructional design conditions: a direct instructions approach to learning and its extension with a collaborative activity. A control group received the former instructional design …
Mental Workload Assessment: Knowledge-Bases Based Upon The Features Of The Original Nasa Task Load Index, Lucas Rizzo, Luca Longo
Mental Workload Assessment: Knowledge-Bases Based Upon The Features Of The Original Nasa Task Load Index, Lucas Rizzo, Luca Longo
Other resources
No abstract provided.
Using Spatialisation To Support Exploratory Search Behaviour, Clement Roux
Using Spatialisation To Support Exploratory Search Behaviour, Clement Roux
Dissertations
Information-seekers traditionally interact with digital content through keyword-based search interfaces displaying results in list views. Well-defined lookup search tasks are performed brilliantly with these interfaces, enabling users to find relevant information and develop a relative understanding of the underlying information space. However, it is feasible to suggest that ill-defined and abstract search tasks could be better supported with a different interface that could allow the user to explore a library’s content and develop an appropriate mental model of the information space. One such approach is based on the use of visualisation, an approach to data analysis that aims to reduce …
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Modeling Mental Workload Via Rule-Based Expert System: A Comparison With Nasa-Tlx & Workload Profile, Lucas Rizzo, Sarah Jane Delany, Pierpaolo Dondio, Luca Longo
Conference papers
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 transates into computable …
Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh
Eliciting Knowledge Bases With Defeasible Reasoning: A Comparative Analysis With Machine Learning, Peter Keogh
Dissertations
This thesis compares the ability of an implementation of Defeasible Reasoning (via Argumentation Theory) to model a construct (mental workload) with Machine Learning. In order to perform this comparison a defeasible reasoning system was designed and implemented in software. This software was used to elicit a knowledge base from an expert in an experiment which was then compared with machine learning. The central findings of this thesis were that the knowledge based approach was better at predicting an objective performance measure, time, than machine learning. However, machine learning was better equiped to identify another object measure task membership. The knowledge …