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Embodied cognition: Enhancing thinking through movement and gesture

Written by: Christopher Tay
9 min read
Christopher Tay, Headteacher, Longden CE Primary School and Nursery, Shropshire, UK; Visiting Lecturer in ITE, University of Chester, UK

Embodied cognition (EC) includes a range of ideas, commonly bound by the understanding that the thinking cycle – perception/interpretation – occurs across the brain, body and environment. The role of gesture and movement in enhancing instruction is well documented (Jump, 2022), but EC represents a growing research programme that stretches beyond commonplace notions of embodiment, seeking to challenge the orthodox idea of the brain as a reflexive organ waiting for an incoming stream of information from the body and beyond. It offers a radical alternative to the traditional dualist notion of the mind–body split and standard cognition accounts of thinking as an input–computation–output process that occurs in a brain sealed off from the world by the sensory buffer (Shapiro, 2011). Thinking in EC terms is dynamic and concurs with current ideas of the brain as a hungry organ of prediction and expectation, constantly at work on its situation (Clark, 2011, 2016).

The role of movement 

In its most generalised form, EC sheds light on the accepted norm that movement is essential to learning in the Early Years. Movement in infancy is essential to the development of a child’s perceptual powers, as exploration of the environment increases the amount and variety of information to be processed (Kontra et al., 2012). Of relevance to later stages of learning, movement in infancy lays down neural networks of spatial representations that are utilised in the organising of school knowledge (Bottini and Doeller, 2020). 

In mathematics, movement harnesses spatial thinking in the use of manipulatives, which benefit the learner in their representational form but also enact thinking that appears to mimic essential cognitive processes, such as categorisation and pattern-detection. Vygotsky’s (1986) idea of material carriers as objects that convey cognitive benefit is fulfilled in EC’s combination of spatial thinking and the recruitment of objects in the environment for offloading cognitive effort.

Active teaching approaches that utilise performance as a form of physical corollary to text-based studies report convincing results in developing students’ understanding of challenging texts. The work of the Royal Shakespeare Company has demonstrated increased engagement and understanding for students that standard text-based approaches have failed to achieve (Irish, 2011). 

The importance of gesture

Gesture – expressive movement of hands, body or face – is another common action whose meaning is usefully informed by EC. Attaching precise meaning to the endless variety of gestural signals is too complex for any practical use in the classroom, but research linking gesture to learning suggests that we should take seriously its role in enhancing thinking. The use of gesture by both teacher and student has been shown to benefit knowledge transfer (Kontra et al., 2012). Far from simply illustrating thinking, gesture is an act of cognition and appears to operate at a similar level of abstraction to prosody in conveying information that is above the level of the words themselves (Novak et al., 2014). 

Thinking is situated across mind, body and environment 

EC is most commonly considered as a form of enacted cognition projected outwardly: thinking that is physically materialised in actions and objects. The relevance of this is highlighted by recent interest in the role of movement and gesture in generative learning (Fiorella and Mayer, 2015). EC’s value also lies in the way in which it presents the environment as a dynamic contributor to perceptual processes, enhancing thinking by maximising the information available for processing. While the majority of our activity represents action in the world, some actions are intended to alter that world in order to extract information not immediately apparent; consider the imperceptible sideways head movement used to judge the space between two objects in the distance (Clark, 2011). 

Such epistemic actions are unconsciously executed, underpinning one of the more radical aspects of EC: that a large amount of our thinking is carried out without the need for manipulation of mental representations, as in the standard version of cognition. This key idea stems from the work of ecological psychologist James Gibson (1979), whose concept of affordance describes the opportunities in the environment for direct perception and action that rarely involve the conscious cortex.

The concept of affordance in EC raises the question of whether certain forms of thinking require any sort of mental representation at all. Wilson and Golonka’s (2013) explication of the ‘flyball’ problem demonstrates this difference between EC and standard cognition. In standard cognition, the outfielder watches the ascent of the ball and, based on stored schemas, calculates roughly where the ball will descend and moves to catch it. If correct, tracking outfielders’ movements would reveal conscious intention in patterns of straight lines. In fact, real-time tracking reveals an arc of travel that curves from the original field position to the point of contact with the ball. Further investigation reveals movement unconsciously directed by the brain, whose sole task is to keep the sensory input – the ball – in contact with the eye by adjusting position such that the ball appears to travel in a straight line to the fielder rather than the other way around. The outfielder solves the flyball problem by using the information available for perception generated by the ball. 

As a working principle of EC, the quality of cognition relies on the extent to which all the information available has been used for processing, including information created by movement in the environment (Wilson and Golonka, 2013). This has resonance for those familiar with the ‘stuck’ child, where useful information, including prior knowledge, remains locked away because of a lack of the kind of dynamic engagement with the learning environment evidenced in the flyball problem. 

This can happen when approaches to learning design view the brain as a reflexive computational machine waiting for input from the outside world. The central dilemma here, for learning across the stages, is that children begin in a design that teaches them through active exploration, to mine the world for information but, having developed these powers of engagement, students rapidly find themselves in an environment largely mediated by adult-directed input. 

Standard conceptions of the brain

Two aspects of the orthodox conception of the brain can be problematic: cognition as a linear process of input-computation-output; and the tendency for enquiry to treat the brain as a set of separate functional components orchestrated by the processes of cognition. EC incorporates a different view of the brain as a dynamic system that is constantly at work on the world, whose elements cannot be meaningfully separated (Shapiro, 2011; Clark, 2016). 

Cognitive load theory (CLT) treats working memory in isolation from attentional and inhibitory control that together make up the mechanism of cognitive control known as executive function (Diamond, 2013). Attentional and inhibitory control represent the mechanism by which the brain manages engagement, whereas working memory addresses the interpretation of incoming sensory signals. One of the central principles of CLT is that working memory recruits prior knowledge from long-term memory to reduce cognitive load and support successful problem-solving. A Year 2 SAT maths paper that I once marked had this response: 7 + 8 = 9. Clearly something else was needed to inhibit the strong message from earlier learning. Of interest here is the research into conceptual learning in maths and science that finds more able children experience the same powerful prepotent ideas from long-term memory but are better at discounting them in favour of more likely solutions (Mareschal, 2016).

Dynamic system brain

Orthodox research treats the brain as a reflexive organ of computation, and enquiry centres on understanding responses to external stimuli, so-called task-evoked activity. This activity is viewed as separate from the constant stream of intrinsic spontaneous activity already at work in the brain. In a dynamic system view of the brain, separating the brain into different modes and components is not the priority, with some surprising results. 

For example, the majority of the brain’s metabolic resources are used to maintain intrinsic spontaneous activity; task-evoked activity utilises only a small fraction of resources by comparison (Clark, 2016). In the dynamic system brain, the real work is the intrinsic spontaneous activity, which is merely modulated by intermittent incoming sensory signals. There is apparently no definitive boundary between the brain in its ‘resting’ state and during task-evoked activity (Llinas, 2014). 

Using spatiotemporal measurement, the brain is seen to toggle freely between intrinsic spontaneous activity, where cognition is distributed across more areas for longer, and task-evoked activity, where cognition appears more concentrated in specific regions for shorter periods. These concentrated clusters are known as neural synergies and represent a rapid bringing together of neural resources in order to deal with information arriving via sensory input from the world (Bolt et al., 2017). In its thinned-out distributed state, when no external signal of interest is evident, the brain maintains a ‘repertoire’ of potential synergies: a range of shadowy, partially formed clusters ready for situations as they arise (Uddin, 2020). In this state, the brain might be said to be anticipating the world, open to variation in what might possibly be ‘out there’. It is a state of alertness – engagement at a scale that casts a wide net of expectation in search of information that would indicate which of the possible synergies to evoke in response to changes in the environment. The predictive brain (Clark, 2016) proposes a similar story of a brain hungry for information, speculating on the most likely combination of neural resources needed for the situation as it unfolds. 

When that need for focused thinking arises, the success of task-evoked cognition depends upon the quality of intrinsic activity prior to the stimulus (Bolt et al., 2017). Dynamic system brain theory proposes that what precedes the planned learning event is crucial to the success of the learning event itself. Students are already dynamically engaged with their environment before instruction or task focus begins. What sustains this state of alertness is what, in cognitive terms, might be called healthy uncertainty – the kind of unknowing that sustains the brain in an open-to-variation state, curious and actively seeking information. 

EC and the enabling environment 

While the embodied enaction of thinking is an important element of EC, it is in its concern for the inward processes of perception that EC may have its greatest implications for education. Pedagogy informed by EC would focus on strategies to support learners in developing the ability to extract all the available information from the environment for processing, including the recruitment of objects and the use of movement for the purposes of provoking, extending or enhancing thinking. It would also focus on supporting learners in dynamically engaging with the environment as a general state of mind through cultivating uncertainty – a healthy state of curiosity and openness to variation or difference – as the precursor to high-quality task-evoked activity. In this way, EC brings a scientifically principled richness to the notion of the enabling environment and, in doing so, extends the relevance of this concept beyond its usual context in the Early Years to all learning stages.

As a research programme, EC may help us to learn more about how enabling environments can serve to enhance the quality of thinking through improving students’ engagement. Practical insights from this can be found in research that asked whether silent students learned less than those who participated in classroom talk. From an EC perspective, the findings may help us to rethink what we mean by participation. The silent students learned as well or better than the vocal ones, the key factor being the programme of active listening that all students had taken over an extended period of time, creating a highly enabling environment (O’Connor et al., 2017).

Insight from EC reminds us that there is more to learning than knowledge acquisition. Shifting attention to the equally important processes of engagement might help us to heed Sfard’s (1998) advice of keeping two purposes in balance: ‘learning as acquisition’ and ‘learning as participation’.

References
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