Mind, Body, World- Foundations of Cognitive Science, 2013a

interaction with the world are not available to consciousness and are therefore difficult
to study phenomenologically.
Embodied cognitive science’s interest in phenomenology is an example of a
reaction against the formal, disembodied view of the mind that classical cognitive
science has inherited from Descartes (Devlin, 1996). Does this imply, then, that
embodied cognitive scientists do not engage in the formal analyses that characterize
the computational level of analysis? No. Following the tradition established by
cybernetics (Ashby, 1956; Wiener, 1948), which made extensive use of mathematics
to describe feedback relations between physical systems and their environments,
embodied cognitive scientists too are engaged in computational investigations.
Again, though, these investigations deviate from those conducted within classical
cognitive science. Classical cognitive science used formal methods to develop
proofs about what information processing problem was being solved by a system
(Marr, 1982), with the notion of “information processing problem” placed in the
context of rule-governed symbol manipulation. Embodied cognitive science operates
in a very different context, because it has a different notion of information processing.
In this new context, cognition is not modelling or planning, but is instead
coordinating action (Clark, 1997).
When cognition is placed in the context of coordinating action, one key element
that must be captured by formal analyses is that actions unfold in time. It has
been argued that computational analyses conducted by classical researchers fail to
incorporate the temporal element (Port & van Gelder, 1995a): “Representations are
static structures of discrete symbols. Cognitive operations are transformations from
one static symbol structure to the next. These transformations are discrete, effectively
instantaneous, and sequential” (p. 1). As such, classical analyses are deemed
by some to be inadequate. When embodied cognitive scientists explore the computational
level, they do so with a different formalism, called dynamical systems
theory (Clark, 1997; Port & van Gelder, 1995b; Shapiro, 2011).
Dynamical systems theory is a mathematical formalism that describes how systems
change over time. In this formalism, at any given time a system is described as
being in a state. A state is a set of variables to which values are assigned. The variables
define all of the components of the system, and the values assigned to these
variables describe the characteristics of these components (e.g., their features) at a
particular time. At any moment of time, the values of its components provide the
position of the system in a state space. That is, any state of a system is a point in a
multidimensional space, and the values of the system’s variables provide the coordinates
of that point.
The temporal dynamics of a system describe how its characteristics change
over time. These changes are captured as a path or trajectory through state space.
Dynamical systems theory provides a mathematical description of such trajectories,
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