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

growing variety of models of reorientation are appearing in the literature, including
models consistent with the symbol-manipulating fundamental of classical cognitive
science (Cheng, 1986; Gallistel, 1990), neural network models that are part of connectionist
cognitive science (Dawson et al., 2010), and behaviour-based robots that
are the domain of embodied cognitive science (Dawson, Dupuis, & Wilson, 2010;
Nolfi, 2002). All of these models have two things in common. First, they can produce
rotational error and many of its nuances. Second, this error is produced as a natural
byproduct of a reorientation algorithm; the errors produced by the models are used
in aid of their validation.
3.13 The Impenetrable Architecture
Classical cognitive scientists often develop theories in the form of working computer
simulations. These models are validated by collecting evidence that shows
they are strongly equivalent to the subjects or phenomena being modelled. This
begins by first demonstrating weak equivalence, that both model and subject are
computing the same input-output function. The quest for strong equivalence is furthered
by using intermediate state evidence, relative complexity evidence, and error
evidence to demonstrate, in striking detail, that both model and subject are employing
the same algorithm.
However, strong equivalence can only be established by demonstrating an additional
relationship between model and subject. Not only must model and subject be
employing the same algorithm, but both must also be employing the same primitive
processes. Strong equivalence requires architectural equivalence.
The primitives of a computer simulation are readily identifiable. A computer
simulation should be a collection of primitives that are designed to generate a
behaviour of interest (Dawson, 2004). In order to create a model of cognition, one
must define the basic properties of a symbolic structure, the nature of the processes
that can manipulate these expressions, and the control system that chooses when to
apply a particular rule, operation, or process. A model makes these primitive characteristics
explicit. When the model is run, its behaviour shows what these primitives
can produce.
While identifying a model’s primitives should be straightforward, determining
the architecture employed by a modelled subject is far from easy. To illustrate this,
let us consider research on mental imagery.
Mental imagery is a cognitive phenomenon in which we experience or imagine
mental pictures. Mental imagery is often involved in solving spatial problems
(Kosslyn, 1980). For instance, imagine being asked how many windows there are on
the front wall of the building in which you live. A common approach to answering
this question would be to imagine the image of this wall and to inspect the image,
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