Physiological states constantly undergo fluctuational changes. Some changes occur via natural organismic processes (breathing, heat compensation), while others are the result of confrontations with changing or unexpected (work) stimuli. In ergonomic research, such cue- or event-related arousal changes are of great interest, as these changes are seen as indicators of increasing or decreasing workload. Using physiological measurement techniques, a non-interruptive observation of these (mental or physical) load patterns becomes possible.
2.1 Defining and operationalizing mental workload
While there was and is little dissensus in ergonomics that the study of MWL is fundamental to the understanding of functions and limitations of the human information processing system (Wickens
2017), measurement and analysis strategies as well as operationalization of MWL remain highly discussed topics (Van Acker et al.
2018; Dehais et al.
2020). MWL has a long history in human factors research. Physiological activation was connected to the prediction of task performance. Later the concept of this relationship was extended, associating it with the idea of a finite information processing capacity which is confronted with variable cognitive demands. Thus, the first central proposition postulates a fit or a misfit between external demands and internal capacitively limited resources. The second proposition concerns the problem of an occurring misfit and if there is a possibility to dynamically cope with it in order to compensate and avoid states of longer lasting (hypo- or hyper‑) stress and discomfort (Selye
1974). Both propositions are the basis of a dynamic adaptive approach, which assumes that in general, but especially in the case of misfit, the organism is seeking for a balanced state of homeostasis and cognitive comfort (Hancock and Warm
1989; Dehais et al.
2020). Finally, there is the assumption that misfits of longer duration should be ergonomically countermeasured.
While these basic assumptions are commonly shared between most researchers, the topics of concrete operationalization and measurement are widely discussed and applied methodologies vary greatly. Different approaches exist to measure MWL using either subjective ratings, more objective performance or observational data, or measuring a wide range of neuro- and psychophysiological indicators. All those methods have in common that they assume to be able to record the individual workers changes of MWL at the work place. A wide range of studies was conducted proving in either laboratory or field settings that those indicators are able to differentiate between levels of MWL (Myrtek et al.
1994; Marquart et al.
2015; Delliaux et al.
2019; Jafari et al.
2020), assembly products of different complexity (Bläsing and Bornewasser
2020,
2021), or even to detect potential mental overload (Hoover et al.
2012). Regarding the measurement of MWL the most discussable points seem to be which indicator to choose, how to analyze it, and if all those indicators are measuring the same aspect of MWL or if it is a rather multidimensional construct (Matthews et al.
2015a).
To a large extent, measuring MWL is a rather practical problem. The operationalization of MWL, on the other hand, is a more theoretical concern. While the concept of the existence of a limited (cognitive) resource that is necessary to cope with external demands is intuitive and easy to understand, theories tend to remain on a rather abstract and descriptive level. Using neurophysiological measurements, it becomes possible to begin to better understand the basic processes of attention and behavior (Parasuraman
2011), but a unified theory of MWL, that merges theoretical aspects from neuroscience, human factors and ergonomics, as well as basic physiology is still missing (Dehais et al.
2020).
Sufficient digital models for mental workload (MWL) and adaptational processes (especially concerning states of over- and underload) do not yet exist. Digital process and strain models probably offer a better chance for a meaningful integration of the measured parameters into some kind of a cognitive overload risk analysis. In a more ergonomical direction, an indicator of this kind could even become the basis for automated workload-matched adaptations for e.g. information assistance systems. However, this approach implies that we are able to measure workload in real time, that we can assess the real amount of event related workload in absolute values, and that we can define redlines, which dynamically separate e.g. regions of reserve capacity and regions of overload from a region of comfort (Young et al.
2015). Until now, none of these implications is completely fulfilled.
2.2 Approaches to mental workload measurement
MWL arises in the area of tension between task-related requirements and personal resources, experiences, and competencies, and thus represents a dynamic and highly interindividual different phenomenon. Being able to validly assess MWL at the work place has to become the basis of ergonomical countermeasures. Thus, a deeper consideration of the different measurement possibilities (subjective, performance related, observational, and physiological; Chen et al.
2016; Longo
2018) becomes necessary. Most use cases and laboratory settings are using a combination of different indicators to compensate for their individual weaknesses and combine their strengths. The combination of physiological measurements and observational data enables to control the analysis for non-work-related distractions or focus on concrete event-related changes. However, psychometric analysis casts doubt on the assumption that the combination of different MWL indicators might always be sufficient. Matthews et al. (
2015b) showed that single indicator approaches were sensitive but occasionally contradictory. To avoid such dissociative results, the used approaches have to be precisely coordinated.
Subjective ratings and similar methods might not be suited for just-in-time detection of MWL changes. Automatically (mechanically) recorded performance parameters, as well as observational data (e.g. in the form of timestamps from machines and assistance systems) have to be focused and combined with objectively measurable physiological changes to fully cover work process related MWL changes.
Improved and miniaturized sensor technologies, resulting in the possibility to most widely measure continuous, mobile, and non-interruptive directly at the work place, have led to an increase in reception for such measurements in recent years (Charles and Nixon
2019). Products originally coming from the consumer market have received increasing interest in human factors and ergonomics research, and have accelerated technological improvement even for more professional equipment. As a consequence, mobile devices are not only increasing in resolution (resp. sampling frequency), but also decreasing in acquisitional costs. At the same time, individuals become more used to wearing them and open-minded to quantify themselves (Swan
2012). These effects pave the way for a broader use in field research. Where procedures such as electrocardiography (ECG) (Mulder
1992; Sammito et al.
2015) and the measurement of muscle activity (EMG) (Kluth et al.
2013) have been already widely used, entirely new possibilities are now opening up, e.g., via mobile eye-tracking and mobile electroencephalography (EEG) solutions (Wascher et al.
2020), allowing for broader objective insights into the processes of workers MWL.
The basic assumption of those rather psychophysiological measurement methods is that the activation of resources to cope with changing work stimuli leads to a measurable change in the activity of the autonomous nervous system (ANS) (Oken et al.
2006; McEwen and Gianaros
2011; Jarczok et al.
2013). While this can be either seen as a more homeostatic process to keep the individuum in a balanced state (Ramsay and Woods
2014), or just a reaction of the bottlenecked information processing in the working memory (Baddeley
2003; Chen et al.
2016), the change of the measured indicators is always interpreted as a momentarily, simultaneously occurring change in MWL.
Neuroergonomics focus more on the usage of modern imaging techniques like functional infrared spectroscopy (fNIRS) or functional magnet resonance imaging (fMRI). Those techniques enable a deeper understanding of concrete neurophysiological brain reactions to concrete work stimuli using an improved spatial and temporal resolution. The idea of resources as the main point for MWL is displaced in favor of an idea of concrete neurophysiological markers and degraded mental states. MWL becomes a measurable brain state. These techniques represent an attempt to open the former black box of MWL and to predict which neuronal and metabolic states lead to decreased performance.
This paradigm shift contrasts classical psychophysiological approaches (Backs and Boucsein
2000), where researchers are rather focused on peripheral correlates of unknown central, neuronal, or metabolic processes. Of course, brain activity and heart rate both are not identical to MWL, but where a neuroergonomist believes he is already inside or closest to the brain and can directly observe brain functions, a psychophysiologist still believes he is outside but has a better chance to take a rough look on information processes and MWL. Inherently, both perspectives assume to validly measure the true amount of MWL.
In this article the focus will be on some of the most widely used (and easiest to apply) psychophysiological measurements. Using heart rate (HR) and different heart rate variability (HRV) indicators as ECG derivates, it becomes possible to relate changing MWL and changing cardiovascular activity to each other. The used HRV indicators are mostly time-based which, due to their mathematical basis, makes them easier to calculate and assess in a dynamic, and, perceptively, just-in-time manner. HR and HRV have already been proven to be able to differentiate between levels of MWL (Myrtek et al.
1994; Delliaux et al.
2019), even using first machine learning approaches to quantify those changes (Hoover et al.
2012). Furthermore, gaze and pupil related parameters like pupillary response (PR), fixation duration (FD), or saccadic peak velocity (SPV) derived from eye tracking measurement are able to show physiological changes with a direct relationship to information processing resp. changing informational load (Di Stasi et al.
2010; Marquart et al.
2015; Di Nocera et al.
2016; Herten et al.
2017; Mathôt
2018).
Although there are different positions, research agrees that it is possible to detect meaningful changes of MWL during work processes using either neuro- or psychophysiological measurement techniques. To interpret changes e.g. of heart rate as changes in MWL, the high complexity and the dynamics of MWL distribution in the working process need to be assessed (exemplarily in Bläsing and Bornewasser
2020). During working processes under real life conditions, the amount of information to be processed is constantly changing and so is the resulting MWL. Thus, continuously changing MWL becomes an elementary part of each working process, fluctuating in dependent on the current informational load. For a better understanding of such processes, it is necessary to develop an analysis framework, which not only takes these special conditions into account, but even actively highlights them.