Mental Workload, Attention and Limits on Human Cognition


I recently co-authored this paper on mental workload with colleagues at ISAE-AERO from Toulouse.  Frederic Dehais invited me to contribute to a paper that he had under development, which was based around the diagram you can see above this post.

I was very happy to be involved and have an opportunity to mull over the topic of mental workload and its measurement, which has always long been an equal source of interest and frustration.  Back in the 1990s sometime, I remember a conference presentation where the speaker opened with a spiel that went something like this – ‘when I told my bosses I was doing a study on mental workload, they said mental workload?  Didn’t we solve that problem last year?’  Well, nobody had solved that problem that year or any other year since, and mental workload remains a significant topic in human factors psychology.

The development of psychological concepts, like mental workload, traditionally proceeds along two distinct lines or strands, these being theory and measurement or testing.  This twofold approach was certainly true of the early days of mental workload in the late 1970s and early 1980s, when resource models of human information processing were rapidly evolving and informing the development of multidimensional workload measures drawn from subjective self-report, performance and psycho/neuro-physiology.   But as time passed, mental workload research developed a definite bias in the direction of measurement at the expense of theory.  This shift is not that surprising given the applied nature of mental workload research, but when I read this state-of-the-art review of mental workload published in Ergonomics five years ago, I couldn’t help noticing how little had changed on the theoretical side.  The notion of finite capacity limitations on cognitive performance still pervades this whole field of activity, but deeper questions about these resource limits (e.g., what are they?  What mechanisms are involved?) are rarely addressed.  This is a problem, especially for applied work in human factors, because it becomes difficult to draw inferences from our measures and make solid predictions about performance impairment that go beyond the obvious.

Of course, there are resource-based models that have been very successful, the multiple resource model provides an excellent framework for understanding patterns of interference during multitasking.  But old-fashioned resource limitations, which are regularly trotted out as a generic explanation for finite limits on cognitive performance, rarely express anything deeper than a well-worn analogy with the role of RAM in a computer.  According to this view, if the human information processing system is pushed to capacity, the system grows slower and slower until it’s no longer able to function.  Although this analogy is intuitive, I never particularly liked it, partly because I find it overly mechanistic but also because I was came to mental workload via research on mental effort, and as a direct result, was always more drawn to models that emphasised dynamic and adaptive processes of self-regulation, such as Hockey’s cybernetic approach or this model described by Hancock and Warm.  In the meantime, and back to the diagram at the top of this post, Fred was experiencing a different set of qualms because the notion of resources did not match well with what he knew about neurological mechanisms.  More importantly, his work on perseveration highlighted a striking category of performance impairment where the operator is unable to adjust even very simple behaviours, which begs a question about how inherent limits on cognitive performance could be responsible.  

The image at the top of this post shows Fred’s original idea for delineating a space that captures limitations on performance based on two dimensions: task engagement and activation.  Working together with myself, Raphaelle and Alex, we began to outline a model of mental workload based on cortical networks and autonomic activation.  

Our starting position was that the attentional control system in the human brain responds flexibly to changes in task demand, however this flexibility promotes specific modes of attentional processing that may not always be appropriate for the task scenario.  For example, in the top-right corner of the diagram, we have a situation where the person engages exclusively with a specific task or sub-task, in other words, attention is focused on that task to the exclusion of all other information, regardless of whether the the other information is important or even life-threatening.  Alternatively, the task at hand may be very dull, and in the absence of external stimulation, attention turns inwards, activating the default mode network for periods of freely-associated thoughts, introspection and memory recall.  These periods of mind wandering can be so absorbing that important information is missed from out there in the so-called ‘real’ world.

When a cognitive task is very challenging, the resource model predicts performance decrements due to inherent limits on the human information processing system.  In the same way that RAM is eaten up by processing demands, our cognitive resources are drained by high levels of demand until it is no longer possible to perform the task.  Alternatively, unlike computers, people can perceive poor performance and anticipate impending failure, which inevitably affects their level of engagement with the task.  Informed by motivational intensity theory, I’d argue that people simply disengage in the face of overwhelming task demands particularly when the probability of success is remote.  This act of disengagement is simply another word for ‘giving up’ but it makes perfect sense to withdraw effort from the task that is perceived to be impossible or simply not that important.  We have some published work on disengagement due to high levels of working memory demand using EEG and fNIRS that presents evidence to support this position.

In short, the paper is pushing for a conception of performance limits and mental workload based on neurological mechanisms, where performance degrades due to the dominance of specific attentional states (e.g., perservation, mind wandering, disengagement) that increase the likelihood of error.  This theoretical explanation shifts emphasis from a pervasive and somewhat superficial explanation of insufficient resources to a perspective on mental workload that emphasises attentional mechanisms at the neurophysiological level.  It also preserves the very human capacity of the individual to be self-regulating with respect to attentional control and task motivation.

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