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

unit encodings), and in modern networks communication between the two must be
moderated by representational layers of hidden units.
Highly artificial choices of input and output representations and poor choices of
problem domains have, I believe, robbed the neural network revolution of some of
its initial momentum. . . . The worry is, in essence, that a good deal of the research
on artificial neural networks leaned too heavily on a rather classical conception of
the nature of the problems. (Clark, 1997, p. 58)
The purpose of this chapter is to introduce embodied cognitive science, a fairly
modern reaction against classical cognitive science. This approach is an explicit
rejection of methodological solipsism. Embodied cognitive scientists argue that a
cognitive theory must include an agent’s environment as well as the agent’s experience
of that environment (Agre, 1997; Chemero, 2009; Clancey, 1997; Clark, 1997;
Dawson, Dupuis, & Wilson, 2010; Dourish, 2001; Gibbs, 2006; Johnson, 2007; Menary,
2008; Pfeifer & Scheier, 1999; Shapiro, 2011; Varela, Thompson, & Rosch, 1991). They
recognize that this experience depends on how the environment is sensed, which
is situation; that an agent’s situation depends upon its physical nature, which is
embodiment; and that an embodied agent can act upon and change its environment
(Webb & Consi, 2001). The embodied approach replaces the notion that cognition
is representation with the notion that cognition is the control of actions upon the
environment. As such, it can also be viewed as a reaction against a great deal of connectionist
cognitive science.
In embodied cognitive science, the environment contributes in such a significant
way to cognitive processing that some would argue that an agent’s mind has
leaked into the world (Clark, 1997; Hutchins, 1995; Menary, 2008, 2010; Noë, 2009;
Wilson, 2004). For example, research in behaviour-based robotics eliminates
resource-consuming representations of the world by letting the world serve as its
own representation, one that can be accessed by a situated agent (Brooks, 1999). This
robotics tradition has also shown that nonlinear interactions between an embodied
agent and its environment can produce surprisingly complex behaviour, even when
the internal components of an agent are exceedingly simple (Braitenberg, 1984;
Grey Walter, 1950a, 1950b, 1951, 1963; Webb & Consi, 2001).
In short, embodied cognitive scientists argue that classical cognitive science’s
reliance on methodological solipsism—its Cartesian view of the disembodied
mind—is a deep-seated error. “Classical rule-and-symbol-based AI may have made
a fundamental error, mistaking the cognitive profile of the agent plus the environment
for the cognitive profile of the naked brain” (Clark, 1997, p. 61).
In reacting against classical cognitive science, the embodied approach takes
seriously the idea that Simon’s (1969) parable of the ant might also be applicable to
human cognition: “A man, viewed as a behaving system, is quite simple. The apparent
complexity of his behavior over time is largely a reflection of the complexity
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