Personality Trait Theory and Multitasking Performance: Implications for Ergonomic Design

Although system designers usually minimise the role of individual differences in operation, personality variables could explain differences in multitasking performance. A concomitant theoretical issue is whether primary or surface personality traits do a better job of predicting performance than the Five-Factor Model (FFM) or global traits. A sample of 174 undergraduates completed the Sixteen Personality Factor Questionnaire (16PF), which was followed by a performance task. A computer-based task that measured simultaneous performance on an arithmetic task and a mental rotation task was used to measure multitasking performance; scores measured the percent accuracy. Primary traits for low emotional sensitivity and high abstractedness, self-control, and general reasoning were all correlated with performance (R 2 = .11), but global or traits corresponding to the FFM were not, except in one sporadic task trial. There was also a strong gender effect on performance. Implications for the study of personality traits in ergonomics are discussed

Psycho-Physiologically-Based Real Time Adaptive General Type 2 Fuzzy Modelling and Self-Organising Control of Operator's Performance Undertaking a Cognitive Task

—This paper presents a new modelling and control fuzzy-based framework validated with real-time experiments on human participants experiencing stress via mental arithmetic cognitive tasks identified through psycho-physiological markers. The ultimate aim of the modelling/control framework is to prevent performance breakdown in human-computer interactive systems with a special focus on human performance. Two designed modelling/control experiments which consist of carrying-out arithmetic operations of varying difficulty levels were performed by 10 participants (operators) in the study. With this new technique, modelling is achieved through a new adaptive, self-organizing and interpretable modelling framework based on General Type-2 Fuzzy sets. This framework is able to learn in real-time through the implementation of a re-structured performance-learning algorithm that identifies important features in the data without the need for prior training. The information learnt by the model is later exploited via an Energy Model Based Controller that infers adequate control actions by changing the difficulty level of the arithmetic operations in the human-computer-interaction system; these actions being based on the most current psycho-physiological state of the subject under study. The real-time implementation of the proposed modelling and control configurations for the human-machine-interaction under study shows superior performance as compared to other forms of modelling and control, with minimal intervention in terms of model re-training or parameter re-tuning to deal with uncertainties, disturbances and inter/intra-subject parameter variability

Cusp Catastrophe Models for Cognitive Workload and Fatigue in Teams

The use of two cusp catastrophe models has been effective for untangling the effects of cognitive workload, fatigue, and other complications on the performance of individuals. This study is the first to use the two models to separate workload and fatigue effects on team performance. In an experiment involving an emergency response simulation, 360 undergraduates were organized into 44 teams. Workload was varied by team size, number of opponents, and time pressure. The cusp models for workload and fatigue were more accurate for describing trends in team performance criteria compared to linear alternatives. Individual differences in elasticity-rigidity were less important than subjective workload and experimental conditions as control variables. Fluid intelligence within the team was an important compensatory ability in the fatigue model. Results further supported the nonlinear paradigm for the assessment of cognitive workload and fatigue and demonstrated its effectiveness for understanding team phenomena

Nonlinear Dynamical Systems for Theory And Research In Ergonomics

Nonlinear dynamical systems (NDS) theory offers new constructs, methods and explanations for phenomena that have in turn produced new paradigms of thinking within several disciplines of the behavioural sciences. This article explores the recent developments of NDS as a paradigm in ergonomics. The exposition includes its basic axioms, the primary constructs from elementary dynamics and so-called complexity theory, an overview of its methods, and growing areas of application within ergonomics. The applications considered here include: psychophysics, iconic displays, control theory, cognitive workload and fatigue, occupational accidents, resilience of systems, team coordination and synchronisation in systems. Although these applications make use of different subsets of NDS constructs, several of them share the general principles of the complex adaptive system

What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.Comment: 57 pages, 2 figures, 1 table, to be published in Artificial Intelligence, Markus Langer, Daniel Oster and Timo Speith share first-authorship of this pape

The Effect of Stages and Levels of Automation and Reliability on Workload and Performance for Remotely Piloted Aircraft Operations

This thesis investigates incorporating different stages and levels of automation with varying degrees of reliability into a remotely piloted aircraft (RPA) surveillance task in order to determine how automation implementation and reliability affect operator workload and system performance. The study uses IMPRINT discrete event simulation to evaluate three levels of reliability in twelve different baseline automation implementations within a remotely piloted vehicle task. Three stages and four levels are modeled, for a total of twelve combinations, along with a baseline task with no automation. The stages modeled are the information acquisition stage, the decision and action selection stage, and the action implementation stage, coupled with the automation recommendation level, the operator consent level, the operator veto level, and the fully automatic level. The reliability is assessed at 100%, with reduced reliabilities of 80%, 70%, and 60%. This study finds that stages of automation have greater impact on performance and the workload values than levels of automation. Automation with reduced reliability is found to have significantly reduced performance for all stages except the response stage models. However, reductions in reliability are found to have little impact on operator workload

The Effects of Information and Communication Technology Use on Human Energy and Fatigue: A Review

Information and communication technologies (ICTs) are generally assumed to save time and energy, yet user fatigue due to ICT use is on the rise. The question about the effects of ICT use on human energy and fatigue is marred by inconsistencies in terminology, definitions, and measures. The aim of this paper is therefore twofold. First, we provide a consolidation and classification of subjective energy and fatigue concepts from four leading research areas. Second, we review the empirical literature on the relationship between ICT use and seven different subjective energy and fatigue concepts from the four areas. We show that ICT use can both energize and fatigue users, sometimes even at the same time, a phenomenon that we term Digital Fatigue Paradox. Overall, there is more evidence for the fatiguing effect, which also appear to be stronger, even though ICT users might actually believe the opposite to be true. By consolidating the mechanisms through which ICT use energizes and fatigues users in a conceptual model, we provide initial explanation for the paradox and derive implications for organizational policy, ICT design, and regulation that strive to improve the user experience with ICTs and prevent ill-being, i.e., foster well-being.Series: Working Papers / Institute for IS & Societ

Toward Computational Simulations of Behavior During Automated Driving Takeovers: A Review of the Empirical and Modeling Literatures

Objective: This article provides a review of empirical studies of automated vehicle takeovers and driver modeling to identify influential factors and their impacts on takeover performance and suggest driver models that can capture them. Background: Significant safety issues remain in automated-to-manual transitions of vehicle control. Developing models and computer simulations of automated vehicle control transitions may help designers mitigate these issues, but only if accurate models are used. Selecting accurate models requires estimating the impact of factors that influence takeovers. Method: Articles describing automated vehicle takeovers or driver modeling research were identified through a systematic approach. Inclusion criteria were used to identify relevant studies and models of braking, steering, and the complete takeover process for further review. Results: The reviewed studies on automated vehicle takeovers identified several factors that significantly influence takeover time and post-takeover control. Drivers were found to respond similarly between manual emergencies and automated takeovers, albeit with a delay. The findings suggest that existing braking and steering models for manual driving may be applicable to modeling automated vehicle takeovers. Conclusion: Time budget, repeated exposure to takeovers, silent failures, and handheld secondary tasks significantly influence takeover time. These factors in addition to takeover request modality, driving environment, non-handheld secondary tasks, level of automation, trust, fatigue, and alcohol significantly impact post-takeover control. Models that capture these effects through evidence accumulation were identified as promising directions for future work. Application: Stakeholders interested in driver behavior during automated vehicle takeovers may use this article to identify starting points for their work