Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 4

1.2.3 Levels of Analysis

a) b)

Although the physical reductionism and reconstruction-
ism motivations behind computational cognitive neuro-
science may appear sound and straightforward, this ap-
proach to understanding human cognition is challenged
by the extreme complexity of and lack of knowledge
about both the brain and the cognition it produces. As a
result, many researchers have appealed to the notion of
hierarchical levels of analysis to deal with this complex-
ity. Clearly, some levels of underlying mechanism are
more appropriate for explaining human cognition than
others. For example, it appears foolhardy to try to ex-
plain human cognition directly in terms of atoms and
simple molecules, or even proteins and DNA. Thus, we
must focus instead on higher level mechanisms. How-
ever, exactly which level is the “right” level is an im-
portant issue that will only be resolved through further
scientific investigation. The level presented in this topic
represents our best guess at this time.
One approach toward thinking about the issue of lev-
els of analysis was suggested by David Marr (1982),
who introduced the seductive notion of computational ,
algorithmic ,and implementational levels by forging
an analogy with the computer. Take the example of a
program that sorts a list of numbers. One can specify
in very abstract terms that the computation performed
by this program is to arrange the numbers such that
the smallest one is first in the list, the next largest one
is next, and so on. This abstract computational level
of analysis is useful for specifying what different pro-
grams do, without worrying about exactly how they go
about doing it. Think of it as the “executive summary.”
The algorithmic level then delves into more of the
details as to how sorting actually occurs — there are
many different strategies that one could adopt, and they
have various tradeoffs in terms of factors such as speed
or amount of memory used. Critically, the algorithm
provides just enough information to implement the pro-
gram, but does not specify any details about what lan-
guage to program it in, what variable names to use, and
so on. These details are left for the implementational
level — how the program is actually written and exe-
cuted on a particular computer using a particular lan-
guage.

Figure 1.1: Illustration of the importance of reconstruction-
ism — it is not enough to say that the system is composed of
components (e.g., two gears as in a ), one must also show how
these components interact to produce overall behaviors. In b ,
the two gears interact to produce changes in rotational speed
and torque — these effects emerge from the interaction, and
are not a property of each component individually.

nition. This is especially true when there are emergent
phenomena that arise from these interactions without
obviously being present in the behavior of individual
elements (neurons) — where the whole is greater than
the sum of its parts. The importance of reconstruction-
ism is often overlooked in all areas of science, not just
cognitive neuroscience, and the process has really only
recently become feasible with the advent of relatively
affordable fast computers.
Figure 1.1 shows a simple illustration of the impor-
tance of reconstructionism in understanding how sys-
tems behave. Here, it is not sufficient to say that the
system is composed of two components (the two gears
shown in panel a). Instead, one must also specify that
the gears interact as shown in panel b, because it is only
through this interaction that the important “behavioral”
properties of changes in rotational speed and torque can
emerge. For example, if the smaller gear drives the
larger gear, this achieves a decrease in rotational speed
and an increase in torque. However, if this same driving
gear were to interact with a gear that was even smaller
than it, it would produce the opposite effect. This is
essentially what it means for the behavior to emerge
from the interaction between the two gears, because it
is clearly not a property of the individual gears in isola-
tion. Similarly, cognition is an emergent phenomenon
of the interactions of billions of neurons. It is not suf-
ficient to say that the cognitive system is composed of
billions of neurons; we must instead specify how these
neurons interact to produce cognition.