The dynamics of expertise acquisition in sport: The role of affective learning design
Introduction
In sport, performers must adapt to the constraints of dynamic performance environments, with commensurate variable conditions and situations, while performing under different emotional states that constrain their cognitions, perceptions and actions (Jones, 2003, Lewis, 2004). Despite the documented presence of emotions1 in sport performance, thus far (see Vallerand & Blanchard, 2000), limited attention has been paid to the role that emotions might play during the acquisition and development of expertise. Traditionally, emotions have generally been viewed as negative and detrimental constraints on behaviour, considered better to be removed from practice task contexts until a skill is well established (Hutto, 2012). This reductionist approach to learning design is in line with traditional thinking in the acquisition of skill in which practice tasks are decomposed to putatively reduce the cognitive loading on performers as they attempt to enhance expertise (Lewis & Granic, 2000). Here we raise questions on the reductionist approach to learning design and discuss an alternate principled approach suggesting how affective constraints on behaviour may be included during the acquisition of expertise in sport, drawing on the theoretical rationale of ecological dynamics.
In existing research on movement behaviours, ideas from dynamical systems theory have been integrated with concepts from Gibsonian ecological psychology, forming the ecological dynamics approach to understanding performance and learning (Araújo et al., 2006, Davids et al., 2001, Warren, 2006). An ecological dynamics approach to enhancing expertise recognises the need for individuals to form mutual functional relationships with specific performance environments during practice and training (Araújo and Davids, 2011, Davids et al., 2013, Seifert et al., 2013). In a functionalist approach to the study of perception and action, Gibson (1979) emphasised the role of the environment and proposed that an individual's movements bring about changes in informational variables from which affordances (invitations for action) are perceived to support behaviours (Withagen, de Poel, Araújo, & Pepping, 2012). As a result a cyclic process is created where action and perception underpin goal-directed behaviours in specific performance environments (Gibson, 1979).
Studying emergent behaviours and the acquisition of expertise at this individual-environment scale of analysis takes into account how perceptions, actions, intentions, feelings and thoughts continuously emerge under the constraints of information external and internal to the individual (Seifert and Davids, 2012, Warren, 2006). Humans, conceptualised as complex dynamic systems, exhibit self-organising, coordination tendencies during learning and performance to achieve specific task objectives (Kelso, 1995, Lewis, 2000b). The informational variables in a specific performance environment, along with associated goals and intentions, constrain how each individual behaves (Davids et al., 2001, Freeman, 2000, Juarrero, 2000). Coordination tendencies (e.g., behaviours in human movement systems) that are stable are described as attractors (Kelso, 1995, Zanone and Kelso, 1992). Stable attractors are states of system organisation that represent well learnt, stable patterns of behaviour (Kelso, 1995, Thelen and Smith, 1994). It is important to note that coordination tendencies may be functional or dysfunctional in terms of meeting the demands of a specific task, or during learning (Warren, 2006). Depending on the depth or stability of an attractor, changes in informational variables that act as control parameters have the potential to perturb (disrupt) coordination tendencies (e.g., Balagué et al., 2012, Passos et al., 2008). Perturbations can lead to phase transitions in coordination tendencies, often producing changes in behaviour. Unstable system states correspond to a ‘hill’ above potential ‘wells’ where coordination tendencies may be variable and possibly less functional (Kelso, 1995, Vallacher and Nowak, 2009). Unstable system states are more open to influence by changes to informational variables both internal and external to an individual during performance (Davids et al., 2001). Through practice and experience in sport, athletes, considered as dynamic movement systems, can learn to enhance stability of performance behaviours and increase their resistance to perturbations, including negative thoughts, and emotions (e.g., differences in ice climbing performance between experts and novices, see Seifert et al., 2013, Seifert et al., 2013). An important question for sport psychologists and coaches concerns how practice programmes can be designed to provide athletes with learning experiences that help them to exploit functional coordination tendencies (i.e. system states which are stable yet adaptable) under the affective constraints of sport performance.
Ecological dynamics is an integrated theoretical rationale of human behaviour that can underpin a principled approach to learning design in clinical (Newell & Valvano, 1998) and sport performance environments (Araújo et al., 2006). The basis of behavioural change through learning involves the systematic identification and manipulation of system control parameters (informational constraints) to perturb stable states of organisation and facilitate transitions to more functional system states (Kelso, 1995, Kelso, 2012). Attractors can take the form of intentions, and/or goals that a performer is 'attracted to' following changes in values of system control parameters (Davids et al., 2001, Davids et al., 2013, Warren, 2006). Stable system states often represent desired forms of organisation that are functional. Enhanced functionality, i.e. ‘what works’ (see Thelen & Smith, 1994), is achieved when an athlete establishes a successful relationship with a performance environment and task goals are achieved (e.g., through more accurate or faster performance outcomes). Simultaneously, functional coordination tendencies can satisfy the psychological needs (i.e. ‘what feels good’) of each individual performer in particular performance situations (Carver et al., 2000, Hollis et al., 2009, Lewis, 2004). In order for a behavioural attractor to become stable through learning, the intrinsic dynamics (the predispositions and tendencies) of each performer and the task dynamics (e.g., specific performance requirements) must converge (Davids et al., 2001, Zanone and Kelso, 1992). The relative stability of behavioural attractors is important to facilitate achievement of successful performance at specific points in time. But, learning environments also need to be dynamic and variable to allow an individual to adapt to changing individual, task and environmental constraints over the short and long time timescales of development (Lewis, 2002, Newell, 1986). A key task for sport psychologists and practitioners is to understand how to effectively manipulate constraints to facilitate the development of new behavioural attractor patterns essential for expertise acquisition.
Sport psychologists have begun to identify control parameters to design effective learning environments that are carefully matched to each individual's intrinsic dynamics, or predispositional behavioural tendencies. Carefully designed learning environments can guide athletes towards metastable performance regions, in which a functional blend of coordination stability and adaptability can result in rich behavioural solutions emerging (Hristovski et al., 2006, Pinder et al., 2012). Metastability is a state of partial organisation where a system ‘hovers’ in a state of dynamic stability, switching between functional states of organisation in response to changing constraints, and displaying subsequent behavioural flexibility (variability, instability) (Fingelkurts and Fingelkurts, 2004, Phillips et al., 2014). Metastability allows a system to transit rapidly between co-existing functional states of organisation, essential for adaptive performance behaviours in dynamic environments (Chow et al., 2011, Kelso, 2012, Kelso and Tognoli, 2009). During learning events in specific performance environments, being in a state of metastability allows performers to discover and explore performance solutions (Kelso, 1995, Seifert et al., 2013). In sport, empirical data has revealed how locating samples of boxers and cricketers in metastable performance regions during practice helped them to explore and exploit rich and creative performance solutions to achieve their task goals (Hristovski et al., 2006, Pinder et al., 2012).
Adopting novel and potentially functional states of system organisation is a consequence of learning and/or development, as individuals transit from the ‘known’ to the ‘unknown’, i.e., moving from a familiar task or situation to one that is new or different. Of interest to sport psychologists is the fact that increases in movement variability during phases of learning are often accompanied by increased intensity and range of emotions (Lewis, 2004). These emotions can be attributed to: (i) the challenges of learning a new movement pattern; (ii) the perceived risk of failure to achieve specific performance outcomes; and (iii), the underlying uncertainty and/or excitement associated with performing in an unknown situation. Observable changes in behaviours and emotions of athletes are of importance since they can act as predictors for potential phase transitions in system behaviours, such as coordinated movement response characteristics (Chow et al., 2011, Kelso, 1995). The theoretical rationale of ecological dynamics suggests that it is essential to design learning environments that guide athletes towards metastable regions of a perceptual-motor workspace during performance (physically and emotionally) to aid the acquisition of expertise in sport (Oudejans and Pijpers, 2009, Pinder et al., 2012). In achieving this aim, an important challenge for sport psychologists and practitioners is how to design learning environments that successfully simulate key constraints of competitive performance environments in sport. Brunswik (1956) advocated that, for the study of individual-environment relations, cues or perceptual variables should be sampled from an organism's environment to be representative of the environmental stimuli that they are adapted from, and to which behaviour is intended to be generalised (Araújo et al., 2007, Pinder et al., 2011b). The term representative design captures the idea of sampling perceptual variables from an individual's performance environment to be designed into an experimental task (Brunswik, 1956). Recent work has considered how the concept of representative design can be applied to the study of sport performance (Araújo et al., 2006, Araújo et al., 2007). Inspired by Brunswik's (1956) insights, the term representative learning design (RLD) has been proposed to highlight the importance of creating representative environments for learning skills and developing expertise (Davids et al., 2012, Pinder et al., 2011b).
Previous empirical work on RLD (Pinder, Davids, Renshaw, & Araújo, 2011a) has focussed on visual information provided during practice in training environments of elite athlete programmes (Barris et al., 2013, Barris et al., 2013), and changes to the complexity of organisation in tasks for practising passing skills in team games (Travassos, Duarte, Vilar, Davids, & Araújo, 2012). These examples advocate expertise acquisition by nurturing the relationship between key environmental information sources and coordination tendencies of a performer in order for more adaptable and effective movement behaviours to emerge (Davids et al., 2013, Phillips et al., 2010). From this perspective the development of expertise is predicated on the accurate simulation of key performance constraints during practice/learning. This approach differs from traditional methods of decomposing tasks to isolate individual components, in order to manage the information load confronting learners (Phillips et al., 2010, Pinder et al., 2013).
An aspect of RLD that needs attention in future conceptualisation of learning and practice is the role of affective constraints on behaviour (for initial discussions see, Pinder, Renshaw, Headrick, & Davids, 2014). In sport, performers need to be able to adapt to task constraints while performing under differing emotional states induced in competitive performance that can influence their cognitions, perceptions and actions (Jones, 2003, Lewis, 2004). Previous work investigating affect in sport performance has tended to focus on capturing the emotions of athletes in ‘snapshots’ of performance at one point in time, such as before or after competition (for a recent example see Lane, Beedie, Jones, Uphill, & Devenport, 2012). Such an approach, however, has not considered how emotions might continuously interact with intentions, cognitions, perception and actions to constrain the acquisition of functional coordination patterns and the development of expertise. A holistic approach should consider task demands of learning environments and the dynamic psychological state of each individual learner as interacting constraints influencing behavioural (perception-action couplings), cognitive, and emotional tendencies (Davids et al., 2013, Newell, 1986). These ideas suggest how sport psychologists and practitioners may seek ways to sample the intensity of emotionally-charged performance conditions in learning environments and practice simulations. To address this issue, in the following sections of this paper, we will discuss why and how emotions could be incorporated into representative learning designs to enhance acquisition of expertise in sport.
Section snippets
Affective learning design
Yet to be seen in the literature is a principled exploration of the role of emotions in developing expertise in sport (Pinder et al., 2014, Renshaw et al., 2014). The role of affect in developing expertise might be harnessed by adhering to two principles: (i) the design of emotion-laden learning experiences that effectively simulate the constraints and demands of performance environments in sport; (ii) recognising individualised emotional and behavioural tendencies that are indicative of
Affective learning design in practice
The intertwined relationship between movement behaviour and emotions poses many challenges and implications for sport psychologists and other practitioners interested in understanding how the concept of ALD can be applied to the acquisition of expertise in sport. Key considerations for implementing ALD include (i) adopting an individualised approach, (ii) acknowledging different time scales of learning, and (iii) embedding emotions in situation-specific task constraints. Sport psychologists
Conclusions
Founded on ecological dynamics principles, previous work has conceptualised and advocated a representative learning design for effective development of skill and expertise in sport. To take forward the understanding and application of this approach we have highlighted the importance of emotions in learning and introduced an integrated concept of ALD with potential scope for future theoretical modelling. Two key interlinked principles of ALD have been identified: (i) the design of emotion-laden
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