Workload Measures

Valerie Jane Gawron

• Éditeur: CRC Press
• Année: 2019
• ISBN:
  9780429671234
• Édition: 3
• Sujet: Psychology
• Sous-sujet: Behaviorism

This book was developed to help researchers and practitioners select measures to be used in the evaluation of human/machine systems. The book includes definitions of human workload and a review of measures. Each measure is described, along with its strengths and limitations, data requirements, threshold values, and sources of further information. To make this reference easier to use, extensive author and subject indices are provided.

Features

• 
  
  

• Offers readily accessible information on workload measures
• Presents general description of the measure
• Covers data collection, reduction, and analysis requirements
• Details the strengths and limitations or restrictions of each measure, including proprietary rights or restrictions
• Provides validity and reliability data as available

1

Introduction

Human factors specialists, including ergonomists, industrial engineers, engineering psychologists, human factors engineers, and many others, continually seek better (more efficient and effective) ways to characterize and measure the human element as part of the system so we can build trains, planes, and automobiles, process control stations, and others with superior human/system interfaces. Yet the human factors specialist is often frustrated by the lack of readily accessible information on human performance, workload, and Situational Awareness (SA) measures. To fill that void, this book was written to guide the reader through the critical process of selecting the appropriate measures of human performance, workload, and SA for objective evaluations.

Chapter 2 describes measures of workload. Each measure is described, along with its strengths and limitations, data requirements, threshold values, and sources of further information. To make this desk reference easier to use, extensive author and subjective indices are provided.

2

Human Workload

Workload has been defined as a set of task demands, as effort, and as activity or accomplishment (Gartner and Murphy, 1979). The task demands (task load) are the goals to be achieved, the time permitted to perform the task, and the performance level to which the task is to be completed. The factors affecting the effort expended are the information and equipment provided, the task environment, the participant’s skills and experience, the strategies adopted, and the emotional response to the situation. These definitions provide a testable link between task load and workload. For example (paraphrased from an example given by Azad Madni, vice president, Perceptronics, Woodland Hills, CA, on the results of an Army study), the workload of a helicopter pilot in maintaining a constant hover may be 70 on a scale of 0 to 100. Given the task of maintaining a constant hover and targeting a tank, workload may also be 70. The discrepancy results from the pilot self-imposing a strict performance requirement on hover-only (no horizontal or vertical movement) but relaxing the performance requirement on hover (a few feet movement) when targeting the tank to keep workload within a manageable level. These definitions enable task load and workload to be explainable in real work situations. These definitions also enable a logically correct analysis of task load and workload.

Realistically, workload can never exceed 100% (a person cannot do the impossible). Any theories or reported results that allow workload to exceed 100% are not realistic. However, as defined, task load may exceed 100%. An example is measuring “time required/time available.” By the proposed definition, this task load measurement may exceed 100% if the performance requirements are set too high (thereby increasing the time required) or the time available is set too low. In summary, workload cannot exceed 100% even if task load does exceed 100%.

Workload has been measured as stand-alone performance (see Section 2.1) or secondary task performance (see Section 2.2) or as subjective measures (see Section 2.3) or in digital simulation (see Section 2.4). Physiological measures of workload have also been identified but are not discussed here. An excellent reference on physiological measures of workload is presented in Caldwell et al. (1994). Dissociation between workload and performance is discussed in Section 2.5.

Guidelines for selecting the appropriate workload measure are given in Wierwille et al. (1979) and O’Donnell and Eggemeier (1986). For mental workload see Moray (1982). Wierwille and Eggemeier (1993) listed four aspects of measures that were critical: diagnosticity, global sensitivity, transferability, and implementation requirements. A general guide is presented in Figure 2.1. Note that the bottom branch can be repeated for simulation.

FIGURE 2.1
Guide for selecting a workload measure.

A similar categorization of workload measures is presented in Stanton et al. (2005). Their categories are: primary and secondary task performance measures, physiological measures, and subjective-rating measures. Further, Funke et al. (2012) proposed a theory of team workload as well as subjective, performance, physiological, and strategy shift measures. Sharples and Megaw (2015) provided the following classification of workload measures:

1. Analytic techniques: comparative analysis, mathematical models, expert opinion, task analytic methods, and simulation models

2. Empirical techniques: primary task performance, secondary task performance

3. Psychophysiological techniques: cardiac activity, brain activity, electrodermal activity, eye function, body fluid analysis, muscle and movement analysis

4. Subjective/operator opinion techniques: single-dimensional scales, multidimensional scales, relative judgments, instantaneous judgments, interviews, and observations.

Finally, Matthews and Reinerman-Jones (2017) published a book on workload assessment.

Sources

Caldwell, J.A., Wilson, G.F., and Cetinguc, M. Psychophysiological Assessment Methods (AGARD-AR-324). Neuilly-Sur-Seine, France: Advisory Group For Aerospace Research and Development, May 1994.

Funke, G.J., Knott, B.A., Salas, E., Pavlas, D., and Strang, A.J. Conceptualization and measurement of team workload: A critical need. Human Factors 54(1): 36–51, 2012.

Gartner, W.B., and Murphy, M.R. Concepts of workload. In B.O. Hartman and R.E. McKenzie (Eds.) Survey of Methods to Assess Workload (AGARD-AG-246). Neuilly-Sur-Seine, France: Advisory Group for Aerospace Research and Development, 1979.

Matthews, G., and Reinerman-Jones, L.E. Workload Assessment: How to Diagnose Workload Issues and Enhance Performance. Santa Monica, California: Human Factors and Ergonomics Society, 2017.

Moray, N. Subjective mental workload. Human Factors 24(1): 25–40, 1982.

O’Donnell, R.D., and Eggemeier, F.T. Workload assessment methodology. In K.R. Boff, L. Kaufman, and J.P. Thomas (Eds.) Handbook of Perception and Human Performance. New York, NY: John Wiley and Sons, 1986.

Sharples, S., and Megaw, T. Definition and measurement of human workload. In J.R. Wilson and S. Sharples (Eds.) Evaluation of Human Work (pp. 515–548). Boca Raton: CRC Press, 2015.

Stanton, N.A., Salmon, P.M., Walker, G.H., Barber, C., and Jenkins, D.P. Human Factors Methods: A Practical Guide for Engineering and Design. Aldershot, United Kingdom: Gower ebook, December 2005.

Wierwille, W.W., and Eggemeier, F.T. Recommendations for mental workload measurement in a test and evaluation environment. Human Factors 35(2): 263–281, 1993.

Wierwille, W.W., Williges, R.C., and Schiflett, S.G. Aircrew workload assessment techniques. In B.O. Hartman and R.E. McKenzie (Eds.) Survey of Methods to Assess Workload (AGARD-AG-246). Neuilly-Sur-Seine, France: Advisory Group for Aerospace Research and Development, 1979.

2.1 Stand-Alone Performance Measures of Workload

Performance has been used to measure workload. These measures assume that, as workload increases, the additional processing requirements will degrade performance. O’Donnell and Eggemeier (1986) identified four problems associated with using performance as a measure of workload: (1) underload may enhance performance, (2) overload may result in a floor effect, (3) confounding effects of information-processing strategy, training, or experience, and (4) measures are task specific and cannot be generalized to other tasks. Meshkati et al. (1990) stat...