In his 2019 review of twenty years of Cognitive Load Theory, John Sweller explores several avenues of further investigation. One such avenue relates to emotions, stress and uncertainty and how these factors influence cognitive load. Within the education community aspects of cognition in learning are often starkly separated from seemingly non-cognitive factors. Curiously, the former is generally considered to be more traditionalist while the latter progressive (see here for a discussion on these differences). Some may, therefore, find it curious that Sweller (a figure favoured by traditionalists) is discussing factors one would think are favoured by progressives. This simple highlights the often fatal flaws in the progressive/traditionalist dichotomy.
Cognitive load can be defined as ‘the relationship between cognitive demands placed on the user by a task and the user’s cognitive resources’ (Palinko et al, 2010), but see my article here for a more in-depth discussion. However, it’s not just the demands of the task that increase cognitive load but also, what Choi et al. (2014) describes as, environmental causal factors. Stress and uncertainty, it’s proposed, add to load and consume much needed resources. Emotions, of course, were the topic of my first book The Emotional Learner, although not within the context of cognitive load directly. Becoming Buoyant addressed cognitive load in relation to academic buoyancy, a model developed by Andrew Martin, a colleague of Sweller at the University of New South Wales. Martin has since shifted his emphasis towards academic buoyancy’s application to the reduction of load, culminating in Load Reduction Instruction, a model of instructional design that sits nicely beside Sweller’s own Cognitive Load Theory.
But how does all this knit together? Choi proposes three types of effects of the physical learning environment on cognitive load and learning: Cognitive effects, physiological effects and affective affects. Those familiar with The Emotional Learner and Becoming Buoyant will recognise these terms. Cognitive effects correspond to uncertainty, while physiological effects would include stress. These, in turn, correspond to the control and composure elements of the 5Cs of academic buoyancy. Affective effects would include emotions, particularly the influence of positive and negative effectual states. I much prefer Pekrun’s classification of activating and deactivating emotions, for reasons I describe in The Emotional Learner.
Cognition and Emotion
From a neuroscience standpoint, brain mechanisms that give rise to conscious emotions aren’t all that different to those that give rise to cognition. They are also processed in a similar way, differing only in terms of the inputs into the brain’s cortical-based general neural networks. It then follows that if cognitions influence cognitive load during a learning task, so do emotions. We certainly know that more anxious people perform worse on formal tests than less anxious people, including tests of general intelligence. Research has also discovered that cortisol (the so-called stress hormone) influences both memory consolidation and memory retrieval, only in different directions. If emotions influence memory generally then one would assume that this includes cognitive load.
Stress, emotions & uncertainty in working memory
According to Moran (2016), stress, emotions and uncertainty may restrict the capacity of working memory by competing with task-relevant processes, so learning is best supported by attempting to prevent such states, or rather develop strategies to minimise their influence. This is certainly the case in education generally, but has a bigger impact on professional and vocational tasks such as those carried out by doctors and nurses or the military. Such professions require rapid decision making, often in the absence of complete information. Furthermore, medical staff will regularly face emotional situations during the course of their work, so they require the ability to successfully regulate emotional states that might hamper their ability. We can’t exclude these emotions, so we have to discover a practical means to deal with them that won’t increase load because all these situations will increase the load on those all important cognitive resources. In the language of Cognitive Load Theory, if emotion, stress and anxiety are part and parcel of the task to be learned, they’ll contribute to intrinsic cognitive load, the mental effort associated with the specific task.
One study, for example, explored the impact of heightened emotions in medical professionals tasked with recognising heart murmurs. Participants with emotional states categorised as invigorating reported increased cognitive load, while those categorised as tranquil reported decreased cognitive load. The study was, however, correlational and used a subjective measure of cognitive load (Fraser et al., 2012). Another study, this time by Um et al. (also from 2012) found that inducing positive emotions during a learning task decreased the perceived difficulty of the task (a possible decrease in extraneous load) but inducing positive emotions before the learning task increased the mental effort participants invested during the task (a possible increase in germane load).
Emotions and Cognitive Load: Possible Explanations
How emotions influence cognitive load is a topic of some debate. In a 2019 literature review, Plass and Kalyuga identified four possible explanations.
1. Emotions represent extraneous cognitive load and compete for resources. Within the Cognitive Load Theory paradigm, extraneous load describes elements that are unrelated to the learning task, yet still compete for limited resources. Anxiety isn’t going to help us complete the task, but it’s still going take up resources that could be better used to do so.
2. Emotions may affect intrinsic cognitive load. This is especially the case when emotion regulation forms part of the learning outcome.
3. Emotions influence motivation. Motivation effects mental effort, so there is going to be an impact on germane load, or the integration of new information into currently stored schemas.
4. Emotions affect memory by broadening and narrowing cognitive resources. This can be explained through the Broaden and Build framework proposed by Fredrickson and explained in detail in The Emotional Learner. The gist of the model is that positive emotions broaden our ability via the formation of thought-action repertoires, while negative emotions (e.g. anxiety) narrow it. When we are under positive affect, information stored in long-term memory is effortlessly recalled without increasing load, yet under negative affect, extraneous load is increased.
These four possible explanations certainly imply that there are other factors, besides task or instructional related ones, that lead to an increase in cognitive load. These findings may help to identify strategies that can reduce environmental causal factors. One particular method investigated by Sonal Arora from Imperial College London looked at how surgeons could use imagination and mental practice prior to performing a procedure to decrease anxiety. The emphasis here was on building and improving coping strategies that could then be applied in the future, what I’ve described elsewhere as positive psychological capital.
The Collective Working Memory Effect
However, Ginns (2005) argues that such mental practice may not work with novice learners because they won’t necessarily be able to use their imagination in the same way. Instead, collaboration might be a better option, mainly due to the collective working memory effect.
Collective Working Memory states that group learning could be more effective than individual learning in the case of more complex tasks because the load can be shared. As Kirschner, Pass and Kirschner (2011) put it:
“…for high complexity tasks, group members would learn in a more efficient way than individual learners, while for low complexity tasks, individual learning would be more efficient.”
Cognitive Load and Academic Buoyancy
Academic Buoyancy is a type of resilience relating specifically to academic attainment. Martin defines it as ‘the ability of students to successfully deal with academic setbacks and challenges that are ‘typical of the ordinary course of school life (e.g. poor grades, competing deadlines, exam pressure, difficult schoolwork)’
In Becoming Buoyant I suggest that some components of the original 5C model (and the adapted 6C model) could indeed help to reduce cognitive load by automating some tasks and behaviours (e.g. adaptive habits) and reducing the burden on working memory that comes with anxiety, a position also proposed by Benjamin Hawthorne and colleagues at the University of Melbourne. Martin has already demonstrated that students’ anxiety predicts counter-productive strategies (e.g. procrastination, self-handicapping) to deal with fear of failure (Martin, 2001). Hawthorne suggests that this is because anxiety overloads working memory with extraneous cognitive load associated with fear and rumination. Put simply, we’re so occupied with our worries and fears that we can’t attend to the learning task at hand; these fears are using too many resources and there is precious little left for learning.
In a 2018 study, for example, Martin and Evans found that Load Reduction Instruction related favourably with Academic Buoyancy. Load Reduction Instruction is a method aimed at reducing cognitive load through a series of instructional steps: difficulty reduction, support and scaffolding, practice, feedback, and guided independence. It would appear that implementing Load Reduction Instruction had a positive impact on levels of academic buoyancy. As Hawthorne points out ‘anxiety may moderate or even mediate the relationship between academic buoyancy and extraneous cognitive load’.
Some Final Thoughts
Strategies to reduce cognitive load are as much about dealing with environmental factors as purely academic ones. Methods such as direct instruction (favoured by so-called traditionalists) do make learning new things less burdensome. Providing adequate scaffolding and feedback help to reduce uncertainty and, consequently, anxiety and fear of failure. Once these early processes have increased student confidence, they can engage in a more hands on approach (guided independence, within the Load Reduction Instruction framework). These efforts to reduce load result in two main outcomes: more efficient learning, and the reduction of uncertainty and stress, leading to increased academic buoyancy via the accumulation of adaptive coping strategies.
At least that’s the hypothesis and the evidence so far appears to support it. Nevertheless, more research needs to investigate possible causal relationships. Furthermore, studied need to look at different learning environments and types of learning, both academic and professional. One thing seems clear: we can’t study emotions and ignore cognition, while neither can we concentrate our efforts on cognition and neglect emotion.