Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 3

properties of different kinds of matter, and the ways in
which they interact. Similarly, many biological phe-
nomena can be explained in terms of the actions of un-
derlying DNA and proteins.
Although it is natural to think of reductionism in
terms of physical systems (e.g., explaining cognition
in terms of the physical brain), it is also possible to
achieve a form of reductionism in terms of more ab-
stract components of a system. Indeed, one could argue
that all forms of explanation entail a form of reduction-
ism, in that they explain a previously inexplicable thing
in terms of other, more familiar constructs, just as one
can understand the definition of an unfamiliar word in
the dictionary in terms of more familiar words.
There have been many attempts over the years to
explain human cognition using various different lan-
guages and metaphors. For example, can cognition be
explained by assuming it is based on simple logical op-
erations? By assuming it works just like a standard se-
rial computer? Although these approaches have borne
some fruit, the idea that one should look to the brain
itself for the language and principles upon which to ex-
plain human cognition seems more likely to succeed,
given that the brain is ultimately responsible for it all.
Thus, it is not just reductionism that defines the essence
of cognitive neuroscience — it is also the stipulation
that the components be based on the physical substrate
of human cognition, the brain. This is physical reduc-
tionism .
As a domain of scientific inquiry matures, there is a
tendency for constructs that play a role in that domain to
become physically grounded. For example, in the bio-
logical sciences before the advent of modern molecular
biology, ephemeral, vitalistic theories were common,
where the components were posited based on a theory,
not on any physical evidence for them. As the molecular
basis of life was understood, it became possible to de-
velop theories of biological function in terms of real un-
derlying components (proteins, nucleic acids, etc.) that
can be measured and localized. Some prephysical theo-
retical constructs accurately anticipated their physically
grounded counterparts; for example, Mendel's theory of
genetics anticipated many important functional aspects
of DNA replication, while others did not fare so well.
Similarly, many previous and current theories of hu-

man cognition are based on constructs such as “atten-
tion” and “working memory buffers” that are based on
an analysis of behaviors or thoughts, and not on phys-
ical entities that can be independently measured. Cog-
nitive neuroscience differs from other forms of cogni-
tive theorizing in that it seeks to explain cognitive phe-
nomena in terms of underlying neurobiological com-
ponents, which can in principle be independently mea-
sured and localized. Just as in biology and other fields,
some of the nonphysical constructs of cognition will
probably fit well with the underlying biological mech-
anisms, and others may not (e.g., Churchland, 1986).
Even in those that fit well, understanding their biolog-
ical basis will probably lead to a more refined and so-
phisticated understanding (e.g., as knowing the biolog-
ical structure of DNA has for understanding genetics).

1.2.2 Reconstructionism

However, reductionism in all aspects of science — par-
ticularly in the study of human cognition — can suf-
fer from an inappropriate emphasis on the process of
reducing phenomena into component pieces, without
the essential and complementary process of using those
pieces to reconstruct the larger phenomenon. We refer
to this latter process as reconstructionism .Itissimply
not enough to say that the brain is made of neurons;
one must explain how billions of neurons interacting
with each other produce human cognition. Teitelbaum
(1967) argued for a similar complementarity of scien-
tific processes — analysis and synthesis — in the study
of physiological psychology. Analysis entails dissect-
ing and simplifying a system to understand its essential
elements; synthesis entails combining elements and un-
derstanding their interactions.
The computational approach to cognitive neuro-
science becomes critically important in reconstruction-
ism: it is very difficult to use verbal arguments to re-
construct human cognition (or any other complex phe-
nomenon) from the action of a large number of interact-
ing components. Instead, we can implement the behav-
ior of these components in a computer program and test
whether they are indeed capable of reproducing the de-
sired phenomena. Such simulations are crucial to devel-
oping our understanding of how neurons produce cog-