Prediction of Mental Workload in Single and Multiple Tasks Environments

Abstract

This research aimed to develop and validate a practical framework for predicting mental workload in both single-and multitask environments with particular consideration of individual factors. To describe mental workload more precisely and completely, a framework for mental workload measures, containing instantaneous workload, average workload, accumulated workload, peak workload, and overall workload was proposed. To model individual factors, 2 new variables (effective workload and ineffective workload) were introduced to conceptually model task-and individual-generated workloads. Under the conceptual model, the operational models for predicting human mental workload for human-computer interaction tasks were developed. The model used a multidimensional approach and allowed the quantification of different aspects of load. Two experimental studies were conducted to validate the proposed model. The results revealed that (a) the framework, which consisted of average workload, accumulated workload, and instantaneous workload could describe workload more precisely than a single overall workload; (b) the proposed mental workload model, which explained 42% of the variance associated with NASA-Task Load index subjective mental workload ratings and explained 78% of the variance associated with performance time, was supported and could be used to predict mental workload; (c) the relationships between the effective/ineffective workload and the 4 independent variables were partially validated. The results showed that both task-related factors and individual-related factors could significantly affect mental workload; (d) mental workload was significantly affected by time pressure. The workload in a self-paced multitask environment was 29% lower than the workload in a system-paced multitask environment in the experiment. The workload in a self-paced, multitask environment was 19% lower than the workload in a system-paced, single-task environment.

Real-time Prediction of User Performance based on Pupillary Assessment via Eye-Tracking

“…This task-focused difficulty measure is appropriate to assess effective mental effort (Xie & Salvendy, 2000). We successfully evaluated the subjective perceived task difficulty prior the experiment with a small non-documented student sample.…”

“…We argue that, as the recurring pupillary diameter response, the pupil diameter variability (PDV) represents a user's instantaneous mental effort (cf. Xie & Salvendy, 2000) in a timeframe of a few seconds. Since a user's mental effort correlates positively with the user's performance, we argue that PDV, which represents a user's instantaneous mental effort, may be a suitable predictor of user performance.…”

“…From various theoretical perspectives (e.g., cognitive load, task technology fit, job demandsresources), users need to adaptively regulate their mental effort to increase their performance. However, current means to measure users' mental effort based on their own subjective rating do not do so adequately (e.g., Xie & Salvendy, 2000); further, one cannot possibly perform such measurements so in real time.…”

Measuring and Explaining Cognitive Load During Design Activities: A Fine-Grained Approach

“…The alignment will be then used to slice the design activity into periods of time with start and end timestamps to calculate sub-task specific CL. Different measures such as the average CL, accumulated CL, or the instantaneous CL as suggested by [17] can be calculated for the respective periods of time. For an overview of CL measurements in general and neuro-physiological measurements in particular please see [1].…”

The Importance of Human Mental Workload in Web Design

“…The operational and practical nature of the construct of human mental workload, in the last few decades, has been acquiring interest in Neuro-science, Physiology and even Computer science (Kramer and Sirevaag, 1987;Kantowitz, 1988;Donnell and Eggemeier, 1998;Young and Stanton, 2001). There is a wide application field (Donnell and Eggemeier, 1998;Tracy and Albers, 2006;Xie and Salvendy, 2000;Gwizdka, 2009a;Gwizdka, 2009b) and this new research domain may have an important impact in the future, above all in Human-Computer Interaction. The concept has become increasingly important since modern interactive systems and interfaces may impose severe requirements on mental workload or information-processing capabilities.…”