The 'ecological-dynamics' of skill acquisition: Part 1

The separation of humans from their environments is rooted in the foundations of modern science from around the time of the enlightenment (see Svenson, 1998; Glimcher, 2005). A central criticism of which is the presumption that physical phenomena are fundamentally deterministic in nature, which according to Lickliter (2009) is unnecessarily reductionist and not supported by current understanding of biological and psychological development. During the first part of the 20th century the emerging discipline of quantum physics (see Glimcher, 2005) would show that at an atomic and sub atomic level particles demonstrated fundamentally indeterminate behaviour and could only be described probabilistically. Given that ‘learning’ takes place in dynamic and unpredictable contexts, evidence also suggests that living systems are inherently indeterminate (see Hall, 2006) and as such the interaction between an individual and its environment must also be indeterminate in nature (see Chow et al., 2011).

In an indeterminate physical world the environment, and the situations we encounter in it, acts to produce an external flow of energy that the biological organism dissipates by producing its own internal entropy (see Kondepudi, 2012). The organism and its surrounding environment constitute a single system (Turvey, 2009) in which the value of each can be predicted from the value of the other, under all considered circumstances (Beek et al., 2003). With this in mind the appropriate scale of analysis for understanding, and potentially predicting, human behaviour is the interaction between the organism and the environment. As such a biological system, able to exchange energy and matter with the environment, is said to have ‘agency’ because when it interacts with an environment it is subsequently changed by the interaction (Ovens et al., 2013).

The process of producing internal entropy as a dissipative response to external (environmental) entropy is known as ‘catalysis’ (see Cuff, 2007). When stochastic perturbations act to disrupt their system dynamics, open systems strive to self-organise and develop new structure where no previous knowledge of the structures impending form is known (see Stephen et al., 2009). Biological systems display ‘meta-stability’, that is to say that they have access to multiple solutions to performance problems (Phillips et al., 2010). They exhibit nonlinearity in their ability to respond to environmental constraints. Insights such as these have their origins in biology, physics and psychology (see Seifert et al., 2013) and have given theoretical impetus to the development of ‘nonlinear pedagogy’ and the ‘constraints-led approach’ to motor learning (see Brymer et al., 2010).

According to Simon (2007), the field of Ecological dynamics has been responsible for a full scale paradigm shift in thinking about the acquisition of superior performance in sport. The ecological approach was developed, in many respects, as an alternative to highly structured, mechanistic and overly cognitive ‘enrichment theories’ (Araujo & Davids, 2011) such as the theory of ‘deliberate practice’. In enrichment theories environmental stimuli are ambiguous, individuals overcome ambiguity by developing increasingly sophisticated processes and internal structures (see Davids et al., 2012). Instead, in the field of ecological dynamics, physical phenomena are characterised as dynamic, nonlinear biological systems (Seifert et al., 2013), capable of spontaneously self-organising under constraints (Renshaw et al., 2009); making them non-algorithmic, non-computational (see Hanford, 1997; Kondepudi, 2012; Turvey & Carello, 2012) and non-representational within one-dimensional and linear thinking.

 

Talent Development, Physics and Biology

Our bodies and minds are creatures of adaptation and given that biological systems are in continuous state of adaption, our current state of being can be represented as the phase space that we currently inhabit. On a larger scale it is this process that forms colonies of organisms that have been subject to similar interactions. In this way we converge on a phase space as a result of the continuous interaction between organism and environment in a process involving thermodynamic flow of energy. That is to say that both the environment and the organism produce entropy, entropy is the thermodynamic flow that allows us to predict the systems current state of organisation and all potential future states. For example if the organism continuously follows the path of least resistance to a local source of energy it will reach a state of maximum entropy production. In this example the entropy producing organism has overwhelmed the entropy in the environment and will eventually transit towards a state of decline because this is the only direction currently available to it.

Managing upward directional transits in relation to talent development is clearly a fundamental concern for those working within the process. Whilst all of the above may seem like a complicated way of explaining the obvious it is in the rule based detail of these principles that I believe provides the greatest opportunity for successful talent development. Hopefully as the emerging fields of ecological psychology and dynamical systems theory reach the mainstream we will see a greater number of performers/teams reaching their full potential.

Introduction to non-Linear pedagogy Pt 2 - Coaching

The development of non-linear pedagogy is predicated on the notion that the learner is a non-linear dynamical system that produces movement solutions in response to information in its environment.  An agent and its environment are inseparable in which case open systems are susceptible to perturbations that can either fatally disorder it or drive it to self-organise. The view that the biological adaptive process drives the development of talent in this way has given rise to the notion that ‘expertise is an adaptation’ to the many interacting (i.e. psychological, physiological, sociological) constraints acting on the individual at any given time.

The basis therefore of a non-linear pedagogy is that the coach can manipulate the sources that act to constrain performance in a way that guides the performer to a more functional state. For example evidence shows that the manipulation of constraints can lead to the production of successful motor patterns and decision making behaviour (Chow et al 2006). In ‘constraints led coaching’ the coach creates training environments that are designed to induce adaptation in the ‘known’ mechanisms that control performance in the target context – they attempt to build the machine for the race it's in!

Some thoughts on designing learning in a constraint based framework;

-          The key constraints that can be manipulated are: the task (i.e. Rules), the individual (deliberately fatigued), the environment (i.e. the scaling parameters of the playing space)

-          The configurations of constraints by the coach are not designed to prescribe the way the learner behaves but instead guide it (Davids et al 2012)

-          Constraints cannot influence the learning process independently (see Davids 2008) i.e. the technical, mental, tactical aspect of performance must be integrated in to the training environment.

 

-  Practice therefore  should be ‘representative’, if the setting for practice provides more time for participation and it doesn’t invoke physical, psychological and performance adaptation the it should not be considered as a talent development environment (Cobley et al 2012)

-          Practice should designed to drive the performer to the ‘edge of stability’ where they are forced to function at high levels of intensity (Renshaw et al 2012)

 

Introduction to non-linear pedagogy

Extended engagement in practice leads to functional adaptation in the mechanisms that control performance. The emergence of new structure characterises the adaptive process and occurs when specific constraints act to perturb the systems state of homeostasis. In this respect ‘new structure’ emerges even though the system has no previous knowledge of the new structures impending form. Crucially when an athlete experiences a plateau in performance the existing structure of the system will remain unchanged until entropy overwhelms the systems current state of stability. Entropy then, as described by Stephen et al (2009), is an index of disorder or instability acting upon the system driving it to spontaneously reorganise.

The self organising propensity of neurobiological systems can be harnessed for the development of sporting talent. The space in-between stability and instability is known as the ‘phase space’ where the athlete is transitioning from one level of performance to another. This is described by Rernshaw et al (2011) as the meta-stable region of performance where the athlete is poised on the edge of stability in a highly adaptive state. In this respect the developing performer can be thought of as a non-linear dynamical system because what acts to perturb reorganisation of system dynamics in one individual may not be the same in another.   

Conceptualising human development in this way is significant because traditional theories of learning have been unable to show how individual differences can be acounted for and designed into the learning process (Davids et al 2012). For this reason monotonic linear models of talent development aligned to deliberate practice methodology have been unsuccessful (Renshaw et al 2011). 

If this knowledge is to be integrated successfully into learning design, in a way that directly impacts on the talent development process, practitioners will need to have a sound theoretical understanding of the transfer-appropriate processes that govern such thinking.

The Emergent Nature of Skill

Golf is a game played in environments where specifying variables (affordances for action) include: weather systems, topology, surface type and performance pressure; furthermore these specifying variables are non stable and subject to fluctuations.  Regardless of this the golf performer, typically, attempts to overcome these constraints during block practice sessions in predictable environments, leading to inevitable problems in relation to retention and transfer of skill to the performance context.

Not surprisingly research has shown (e.g. Ball et al 2003; Fairweather et al 2002) that no common optimal coordination pattern can exist because each time a skill is performed in ‘real life’ it is subject to a set of unique variables (e.g. wind, temperature, slope, physiological status, psychological factors) that will be present to a lesser (or greater) extent each time a skill is performed. In thermodynamics, which predicts a tendency toward entropy, such systems are called ‘open systems’ which are in contrast to ‘closed systems’ that tend to be information impoverished. This effect is seen in skill acquisition during block practice in ‘closed’ contexts where performance tends to be effective during the practice session however retention and transfer tend to be poor, whilst the random prescription of practice tasks, known as contextual interference, has been shown to have the opposite effect (see Lee & Simon 2009).

According Ecological psychologists (e.g.Gibson 1979), specifying variables in the environment are acted upon in the form of a functional (or non-functional) movement response/solution; this is referred to as ‘information-movement coupling’. Under such conditions skill is considered to be an emergent attribute constructed when an agent firstly becomes aware of the key information sources in the environment; and secondly fine tunes the movement response. These interactions produce order through a process called ‘self organisation’ as the human system acts to form coherent spatial and temporal structures (Huys et al 2009).

One way the coach can encourage the self organising propensity of the human system is through a process called constraints based coaching (see Chow et al 2006). In constraints based coaching the practice session takes place with all information sources present and flowing (e.g. the mental, technical, tactical, physical aspects of performance). In this situation the coach can manipulate environmental constraints, such as changing the scaling parameters of the playing field, to induce greater levels of skilled behaviour.

Designing 'representive tasks' in this way according to Davids et al (2007), exemplify how perception, decision making and action (a) are examples of adaptive behaviour, (b) embodies the performer – environment system, (c) function in a task specific manner, (d) are dependent on interacting constraints that are specific to the performance context.   

Instruction Versus Environment

In an earlier post (The Problem with Stack and Tilt) I stated that the most adaptive co-ordination patterns are those that are soft assembled and in tune with environmental fluctuations - this new post is will look at this notion in more detail.

 When movement patterns are soft assembled the player is able to focus ‘externally’ on the anticipated outcome of the action, instead of ‘internally’ on the specific movement itself.  In other words the player begins with the outcome in mind (based on how they perceive the shot/situation) and this perception is then orientated to an appropriate movement pattern – this is called action-perception coupling.

In contrast to this, movement patterns that begin life as instructor led demonstrations of the correct posture, weight shift, arm position, hip action etc.,  have been shown to degrade performance under pressure and in some cases are more detrimental to performance than receiving no instruction at all.

(Typically this type of instruction occurs in highly managed, static environments that are characterised by safety and control. In order to reduce information overload ‘part task' practice activities are learned as a precursor to performing the whole skill)

It is likely that during the initial rehearsal of these new movements the player experiences a degree of improved performance. This is likely caused by practicing in a stable environment that allows fine tuning of movement parameters from one attempt to another. This often results in an artificially high level of performer confidence which often results in poor or negative transfer to a stressful performance situation.

It would of course be wrong to suggest that an emerging performer/novice needs no instruction at all, however skill acquisition practitioners advice that as soon as the learner acquires a rough approximation of the movement pattern they should shift their training to a more random schedule.

Whilst time spent in an instructional context would seem to diminish positive transfer to target context this should not have negative implications for the golf coach/instructor/teacher. Instead it would be hoped that a generation of cutting edge coaching research begings to inform the future direction of the industry.

References

Davids, K; Button, C; Bennett (2009) Dynamics of Skill Acquisition, A constraints led approach, Champaign, Human Kinetics

Schmidt, R.A & Wrisberg, C.A (2008) Motor Learning and Performance, A situation based learning approach Champaign, Human Kinetics

Wulf,G., Lauterbach,B., & Toole,T. (1999). Learning advantages of an external focus of attention in golf.Research Quarterly for Exercise & Spor

The Problem with Stack and Tilt!?

Never before has a golf coaching innovation divided opinion to the extent that the swing classification system known as ‘Stack and Tilt’ has. For this reason it needs no introduction apart to say that discourse on this subject is almost always dominated by arguments about its bio-mechanical principals.

Let me be clear about one thing, bio-mechanically and for the purpose of striking a golf ball, I believe the principals of S&T to be sound; however I also believe that this may well be its critical weakness!

The organisation of the system came about based on the research/advice of noted coach Mac O’Grady and by the principals laid out in the book “the golfing machine”. For this reason I will refer to S&T as being created in a laboratory setting characterised by predictability and control.  The ultimate evaluation of a laboratory creation is the degree to which the system is able to interact with the environment that it was designed for.

It would seem that at this stage in time S&T for many interacts perfectly with the environment that created it, exemplified by impressive ball striking by its advocates in closed contexts, but not so well with its target environment. As such its functionality or fitness in a naturalistic setting has been drawn into question which has hindered its implementation on a far greater scale.

The situation is not surprising, nor is it exclusively an S&T issue; research into motor learning and skill acquisition provides an interesting explanation for this. In open systems movement orientates itself to information sources in the environment (ecological constraints), such constraints (weather, playing surfaces, pressure, perception etc.) shape a systems behaviour and as such the most adaptive coordination patterns are those that are ‘soft assembled’ and able to tune into the prevailing task conditions. In other words the environment creates the movement pattern and not the other way about.

S&T, and any other swing conception, created and learned in a static controlled environment will always orientate best to static and controlled conditions. Unfortunately few sports are played in more unpredictable and interchangeable environments than golf. That is why the research shows that the most transferable skills are those that are created and mastered when all information sources are present and flowing.