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.
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.
