Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 6

Cognitive Phenomena

of principles presented throughout the text. We do not
think that the brain nor cognition can be fully described
by these principles, which is why they play an auxiliary
role and are shown off to one side of the figure. How-
ever, they serve to highlight and make clear the connec-
tion between certain aspects of the biology and certain
aspects of cognition. Often, these principles are based
on computational-level descriptions of aspects of cog-
nition. But, we want to avoid any implication that these
principles provide some privileged level of description
(i.e., like Marr's view of the computational level), that
tempts us into thinking that data at the two basic em-
pirical levels (cognition and neurobiology) are less rele-
vant. Instead, these principles are fundamentally shaped
by, and help to strike a good balance between, the two
primary levels of analysis.
The levels of analysis issue is easily confused with
different levels of structure within the nervous system,
but these two types of levels are not equivalent. The
relevant levels of structure range from molecules to in-
dividual neurons to small groups or columns of neu-
rons to larger areas or regions of neurons up to the en-
tire brain itself. Although one might be tempted to say
that our cognitive phenomena level of analysis should
be associated with the highest structural level (the en-
tire brain), and our neurobiological mechanisms level of
analysis associated with lower structural levels, this is
not really accurate. Indeed, some cognitive phenomena
can be traced directly to properties of individual neu-
rons (e.g., that they exhibit a fatiguelike phenomenon if
activated too long), whereas other cognitive phenom-
ena only emerge as a result of interactions among a
number of different brain areas. Furthermore, as we
progress from lower to higher structural levels in suc-
cessive chapters of this topic, we emphasize that spe-
cific computational principles and cognitive phenomena
can be associated with each of these structural levels.
Thus, just as there is no privileged level of analysis,
there is no privileged structural level — all of these lev-
els must be considered in an interactive fashion.

Principles

Neurobiological Mechanisms

Figure 1.2: The two basic levels of analysis used in this text,
with an intermediate level to help forge the links.

known biological mechanisms as possible. There is a
place for both bottom-up (i.e., working from biological
facts “up” to cognition), top-down (i.e., working from
cognition “down” to biological facts), and, most impor-
tant, interactive approaches, where one tries to simulta-
neously take into account constraints at the biological
and cognitive levels.
For example, it can be useful to take a set of facts
about how neurons behave, encode them in a set of
equations in a computer program, and see how the kinds
of behaviors that result depend on the properties of these
neurons. It can also be useful to think about what cog-
nition should be doing in a particular case (e.g., at the
computational level, or on some other principled basis),
and then derive an implementation that accomplishes
this, and see how well that characterizes what we know
about the brain, and how well it does the cognitive job it
is supposed to do. This kind of interplay between neuro-
biological, cognitive and principled (computational and
otherwise) considerations is emphasized throughout the
text.
To summarize our approach, and to avoid the unin-
tended associations with Marr's terminology, we adopt
the following hierarchy of analytical levels (figure 1.2).
At its core, we have essentially a simple bi-level
physical reductionist/reconstructionist hierarchy, with a
lower level consisting of neurobiological mechanisms,
and an upper level consisting of cognitive phenomena .
We will reduce cognitive phenomena to the operation of
neurobiological mechanisms, and show, through simu-
lations, how these mechanisms produce emergent cog-
nitive phenomena. Of course, our simulations will have
to rely on simplified, abstracted renditions of the neuro-
biological mechanisms.
To help forge links between these two levels of anal-
ysis, we have an auxiliary intermediate level consisting

1.2.4 Scaling Issues

Having adopted essentially two levels of analysis, we
are in the position of using biological mechanisms op-