A proposed methodology for the prediction of mental workload, based on engineering system parameters

In the design of engineering systems, mental workload is one of the most important factors in the allocation of cognitive tasks. Current methods of task allocation have criteria that are defined in only general terms and are thus not very useful in aiding detailed decision-making in system design. Whilst there are many quantitative criteria available to determine the physical space in human-machine interaction, system designers really require an explicit model and specific criteria for the following identification of the mental workload imposed by the system; prediction of both human and system performance; evaluation of the alternatives of system design; and the design of system components. It is argued that the available methods of workload or performance are either too domain-dependent to apply to the design of other systems, or subject-dependent and thus do not reflect the objective workload imposed by the system. The presented research adopts a new approach to cognitive task analysis in dynamic decision-making systems. Based on the characteristics derived from task analysis, a general conceptual model of the prediction of mental workload in system design is proposed. In the new model, workload is represented by a set of system parameters—task arrival rate, task complexity, task uncertainty, and performance requirements—which are considered to be the main sources of workload. In this context, workload becomes an objective demand of engineering systems, independent of any subjective factors. Whether an individual or population is overloaded depends upon their workload threshold with respect to the specified task and environment. It is hoped that this new model, after both laboratory and industrial validation, could be used by system designers to predict the workload imposed on people by systems.     

Review and reappraisal of modelling and predicting mental workload in single- and multi-task environments

The concept of mental workload has long been recognized as an important factor in individual performance within complex systems. It is documented that either overload or underload may degrade performance, and further affect the efficiency of the whole system. Therefore, systems designers need some explicit models to predict the mental workload imposed on individuals by the system at an early design phase so that alternative system designs can be evaluated. In examining mental-workload literature, it is found that few predictive mental-workload models have considered factors specific to individuals. This research aims to develop a practical framework for predicting mental workload in both single- and multi-task environments considering such individual factors. In order to describe mental workload more precisely and more completely, a framework for mentalworkload definitions, which contains instantaneous workload, average workload, accumulated workload, peak workload and overall workload, is proposed. In order to model individual factors, two new variables, i.e. effective workload and ineffective workload, are introduced to model the taskgenerated workload and individual-generated workload. The extension of the model to multi-task environments is also discussed. The proposed conceptual models are domain-independent and could be used to guide the development of operational models for different specific tasks.     

Operator performance and subjective response in control of flexible manufacturing systems

In this study, allocation of functions and system size (number of machines) were manipulated to test their effects on operator performance and subjective response in a flexible manufacturing system (FMS). An experiment was conducted using a cross-over design with three independent variables: number of machines, task allocation, and sequence of task presentation. Operator performance was measured by response time, and subjective response assessed using two questionnaires. Physiological measures were also taken (EKG and respiratory rate). The results revealed that the number of machines in the system did not affect performance or subjective or physiological response while the effect of allocation level was significant for both performance and subjective response.     

Operator performance and subjective response in control of flexible manufacturing systems

Abstract

In this study, allocation of functions and system size (number of machines) were manipulated to test their effects on operator performance and subjective response in a flexible manufacturing system (FMS). An experiment was conducted using a cross-over design with three independent variables: number of machines, task allocation, and sequence of task presentation. Operator performance was measured by response time, and subjective response assessed using two questionnaires. Physiological measures were also taken (EKG and respiratory rate). The results revealed that the number of machines in the system did not affect performance or subjective or physiological response while the effect of allocation level was significant for both performance and subjective response.     

A Narrative-Based View of Coexistence Education

Collective narratives of groups in conflict—their perceived histories, beliefs, self-image, and those of their adversaries—play a central role in interpreting and fueling the conflict—and, thus, can play an equally central role in facilitating coexistence. One of their main correlates is their implied delegitimization of the "other's" collective narrative, its pains, its sufferings, its history, and its aspirations. It is this deligitimization that ought to be the main target for change if coexistence is to be promoted, including the acknowledgement of one's own contribution to the conflict. Four dilemmas are discussed: coexistence programs for the dominant versus the subordinate groups; possible counterproductive outcomes; resistance against antagonistic, dominant narratives; and the problem of short-term intervention programs.