By: Victoria Brazil (@SocraticEM)
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Cognitive load theory (CLT) is relevant for our simulation design, delivery, and debriefing. By carefully applying this theory, we can better match our scenario design to the zone of proximal development for individual and team learning. We can use cognitive load as a useful construct (and terminology) for helping teams reflect on performance in a debrief. CLT is relevant for our own cognitive load and performance as simulation faculty. But understanding and managing cognitive load ideally involves some measurement of it, and this is challenging. However, we may have reached a technology tipping point; it might be that directly measuring brain activity using functional near-infrared spectroscopy (fNIRS) is possible and useful.
But lets wind back a bit…
Cognitive load theory and simulation
Cognitive load refers to the amount of mental effort required to process information and perform tasks. It is influenced by the complexity of the task, the individual’s prior knowledge and experience, and the available cognitive resources. See Adam Szuleski’s brilliant work for more comprehensive discussion of CLT, and this article from Gabe Reedy on CLT and simulation, but briefly…
Cognitive load can be categorized into three types:
- Intrinsic Load: This is the inherent difficulty of the task itself. Some tasks naturally require more mental effort due to their complexity, novelty, or the amount of information involved.
- Extraneous Load: This refers to the unnecessary cognitive load imposed by instructional design or environmental factors. Poorly designed simulations or distractions can contribute to extraneous load, diverting cognitive resources from the task at hand.
- Germane Load: This is the desirable cognitive load that facilitates learning and skill acquisition. It involves the construction and automation of mental schemas, enabling the learner to process information more efficiently over time.
Ideally, our simulations optimize germane load and minimize extraneous load. But – to guide our design and delivery – how do we know if our participants are ‘cognitively overloaded’?
Measuring Cognitive Load
Excellent work has been done in the area of cognitive load measurement in simulation. There are subjective measures, such as self-report questionnaires and ratings of mental effort, and objective measures, such as physiological and behavioral indicators. One widely used subjective measure of cognitive load is the NASA Task Load Index (NASA-TLX). Developed by NASA in the 1980s, this is a multidimensional rating scale that assesses different aspects of cognitive load experienced by individuals during a task. It provides a holistic measure by considering multiple dimensions, including mental, physical, and temporal demands, as well as effort, frustration, and performance. It forms the basis of the newly developed simulation task load index (SIM-TLX).
Maybe physiology is a better measure?
Subjective measures of CLT have been limited by the requirement for retrospective assessment. Maybe objective, physiologic, indicators of cognitive load, including pupillometry, eye tracking, electrocardiography (ECG), electroencephalography, and galvanic skin response (GSR) can afford less intrusive real‐time data capture. Ruberto et al. measured simulation participants cognitive load in real time through a combination of ECG and galvanic skin response. They adapted simulation difficulty to the participant’s cognitive load, by changes in the simulated patient’s symptoms.
And maybe … Functional near-infrared spectroscopy (fNIRS)
Bahr and colleagues offer us an insight into the potential of fNIRS for measuring cognitive load in simulations.1 Functional near-infrared spectroscopy (fNIRS) measures changes in blood oxygenation and deoxygenation in the brain by detecting near-infrared light absorption. An fNIRS device is worn as headgear with embedded light sources and detectors. Unlike fMRIs, devices are lightweight, portable, and can be easily worn during simulation training (without looking like a cyborg). They do not require participants to lie in a confined space, allowing for more naturalistic assessments. Apparently fNRIS has a wide range of applications; drowsiness detection in driving, elite sports training, altitude research, rehabilitation and much more.
In the article, the authors tested fNRIS devices worn by team leaders in paediatric cardiac arrest scenarios, and they found quite good correlation between the data captured by the device and cognitively demanding critical events in the resuscitation. They high light the potential for “future simulation research through objective determination of which aspects of care are associated with higher cognitive load and may be high-yield targets for education or systems improvement.”
So, is this the future for simulation training?
Learners wearing fNIRS headbands (or other biometric sensors) as we measure their cognitive load in real time? Is this a research tool to aid simulation design? Or will we use it to design healthcare team structure and function to balance cognitive load in teams? It might be all of these, but I think we’re past the ‘gimmick’ phase and can look forward to more work in this area.
Reference
Bahr, N., Ivankovic, J., Meckler, G. et al. Measuring cognitively demanding activities in pediatric out-of-hospital cardiac arrest. Adv Simul 8, 15 (2023).
Image courtesy of Soterix medical
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