Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 12

Advantages: the “executive” theory of prefrontal cortex function:

if you posit an executive without explaining how it
makes all those good decisions and coordinates all
the other brain areas, you haven't explained too much
(you might as well just put pinstripes and a tie on the
box).
Second, an explicitly specified computational model
can be run to generate novel predictions. A compu-
tational model thus forces you to accept the conse-
quences of your assumptions. If the model must be
modified to account for new data, it becomes very
clear exactly what these changes are, and the scien-
tific community can more easily evaluate the result-
ing deviance from the previous theory. Predictions
from verbal theories can be tenuous due to lack of
specificity and the flexibility of vague verbal con-
structs.
Third, explicitness can contribute to a greater appre-
ciation for the complexities of otherwise seemingly
simple processes. For example, before people tried to
make explicit computational models of object recog-
nition, it didn't seem that difficult or interesting a
problem — there is an anecdotal story about a scien-
tist in the '60s who was going to implement a model
of object recognition over the summer. Needless to
say, he didn't succeed.
Fourth, making a computational model forces you to
confront aspects of the problem that you might have
otherwise ignored or considered to be irrelevant. Al-
though one sometimes ends up using simplifications
or stand-ins for these other aspects (see the list of
problems that follows), it can be useful to at least
confront these problems.
Models allow control. In a computational model you
can control many more variables much more pre-
cisely than you can with a real system, and you can
replicate results precisely. This enables you to ex-
plore the causal role of different components in ways
that would otherwise be impossible.
Models provide a unified framework. As we dis-
cussed earlier, there are many advantages to using a
single computational framework to explain a range
of phenomena. In addition to providing a more strin-
gent test of a theory, it encourages parsimony and

Models help us to understand phenomena. Acom-
putational model can provide novel sources of insight
into behavior, for example by providing a counter-
intuitive explanation of a phenomenon, or by rec-
onciling seemingly contradictory phenomena (e.g.,
by complex interactions among components). Seem-
ingly different phenomena can also be related to each
other in nonobvious ways via a common set of com-
putational mechanisms.
Computational models can also be lesioned and then
tested, providing insight into behavior following spe-
cific types of brain damage, and in turn, into normal
functioning. Often, lesions can have nonobvious ef-
fects that computational models can explain.
By virtue of being able to translate between func-
tional desiderata and the biological mechanisms that
implement them, computational models enable us to
understand not just how the brain is structured, but
why it is structured in the way it is.
Models deal with complexity. A computational mo-
del can deal with complexity in ways that verbal ar-
guments cannot, producing satisfying explanations of
what would otherwise just be vague hand-wavy ar-
guments. Further, computational models can handle
complexity across multiple levels of analysis, allow-
ing data across these levels to be integrated and re-
lated to each other. For example, the computational
models in this topic show how biological properties
give rise to cognitive behaviors in ways that would be
impossible with simple verbal arguments.
Models are explicit. Making a computational model
forces you to be explicit about your assumptions and
about exactly how the relevant processes actually
work. Such explicitness carries with it many poten-
tial advantages.
First, explicitness can help in deconstructing psycho-
logical concepts that may rely on homunculi to do
their work. A homunculus is a “little man,” and many
theories of cognition make unintended use of them
by embodying particular components (often “boxes”)
of the theory with magical powers that end up doing
all the work in the theory. A canonical example is