Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 5

Marr's levels and corresponding emphasis on the
computational and algorithmic levels were born out of
the early movements of artificial intelligence , cogni-
tive psychology ,and cognitive science ,whichwere
based on the idea that one could ignore the underly-
ing biological mechanisms of cognition, focusing in-
stead on identifying important computational or cog-
nitive level properties. Indeed, these traditional ap-
proaches were based on the assumption that the brain
works like a standard computer, and thus that Marr's
computational and algorithmic levels were much more
important than the “mere details” of the underlying neu-
robiological implementation.
The optimality or rational analysis approach, which
is widely employed across the “sciences of complex-
ity” from biology to psychology and economics (e.g.,
Anderson, 1990), shares the Marr-like emphasis on the
computational level. Here, one assumes that it is pos-
sible to identify the “optimal” computation or function
performed by a person or animal in a given context, and
that whatever the brain is doing, it must somehow be
accomplishing this same optimal computation (and can
therefore be safely ignored). For example, Anderson
(1990) argues that memory retention curves are opti-
mally tuned to the expected frequency and spacing of
retrieval demands for items stored in memory. Under
this view, it doesn't really matter how the memory re-
tention mechanisms work, because they are ultimately
driven by the optimality criterion of matching expected
demands for items, which in turn is assumed to follow
general laws.
Although the optimality approach may sound attrac-
tive, the definition of optimality all too often ends up
being conditioned on a number of assumptions (includ-
ing those about the nature of the underlying implemen-
tation) that have no real independent basis. In short,
optimality can rarely be defined in purely “objective”
terms, and so often what is optimal in a given situation
depends on the detailed circumstances.
Thus, the dangerous thing about both Marr's levels
and these optimality approaches is that they appear to
suggest that the implementational level is largely irrel-
evant. In most standard computers and languages, this
is true, because they are all effectively equivalent at the
implementational level , so that the implementational is-

sues don't really affect the algorithmic and computa-
tional levels of analysis. Indeed, computer algorithms
can be turned into implementations by the completely
automatic process of compilation. In contrast, in the
brain, the neural implementation is certainly not derived
automatically from some higher-level description, and
thus it is not obviously true that it can be easily de-
scribed at these higher levels.
In effect, the higher-level computational analysis
has already assumed a general implementational form,
without giving proper credit to it for shaping the whole
enterprise in the first place. However, with the advent
of parallel computers, people are beginning to realize
the limitations of computation and algorithms that as-
sume the standard serial computer with address-based
memory — entirely new classes of algorithms and ways
of thinking about problems are being developed to take
advantage of parallel computation. Given that the brain
is clearly a parallel computer, having billions of com-
puting elements (neurons), one must be very careful in
importing seductively simple ideas based on standard
computers.
On the other end of the spectrum, various researchers
have emphasized the implementational level as primary
over the computational and algorithmic. They have
argued that cognitive models should be assembled by
making extremely detailed replicas of neurons, thus
guaranteeing that the resulting model contains all of the
important biological mechanisms (e.g., Bower, 1992).
The risk of this approach is complementary to those that
emphasize a purely computational approach: without
any clear understanding of which biological properties
are functionally important and which are not, one ends
up with massive, complicated models that are difficult
to understand, and that provide little insight into the
critical properties of cognition. Further, these models
inevitably fail to represent all of the biological mecha-
nisms in their fullest possible detail, so one can never
be quite sure that something important is not missing.
Instead of arguing for the superiority of one level
over the other, we adopt a fully interactive, balanced
approach, which emphasizes forming connections be-
tween data across all of the relevant levels, and striking
a reasonable balance between the desire for a simpli-
fied model and the desire to incorporate as much of the