Introduction and Overview - Computational Explorations in Cognitive Neuroscience - page 14

can fit all the data (unless they are actually isomor-
phic in some way).
Models are reductionistic. Onecommonconcernis
that the mechanistic, reductionistic models can never
tell us about the real essence of human cognition. Al-
though this will probably remain a philosophical is-
sue until very large-scale models can be constructed
that actually demonstrate realistic, humanlike cogni-
tion (e.g., by passing the Turing test ), we note that re-
constructionism is a cornerstone of our approach. Re-
constructionism complements reductionism by trying
to reconstruct complex phenomena in terms of the re-
duced components.
Modeling lacks cumulative research. There seems to
be a general perception that modeling is somehow
less cumulative than other types of research. This
perception may be due in part to the relative youth
and expansive growth of modeling — there has been
a lot of territory to cover, and a breadth-first search
strategy has some obvious pragmatic benefits for re-
searchers (e.g., “claiming territory”). As the field be-
gins to mature, cumulative work is starting to appear
(e.g., Plaut, McClelland, Seidenberg, & Patterson,
1996 built on earlier work by Seidenberg & McClel-
land, 1989, which in turn built on other models) and
this topic certainly represents a very cumulative and
integrative approach.

of), which appear to be serial (one thought at a time)
and focused on a subset of things occurring inside and
outside the brain. This fact undoubtedly contributed to
the popularity of the standard serial computer model for
understanding human cognition, which we will use as a
point of comparison for the discussion that follows.
We argue that these conscious aspects of human cog-
nition are the proverbial “tip of the iceberg” floating
above the waterline, while the great mass of cognition
that makes all of this possible floats below, relatively
inaccessible to our conscious introspection. In the ter-
minology of Rumelhart et al. (1986c), neural networks
focus on the microstructure of cognition. Attempts to
understand cognition by only focusing on what's “above
water” may be difficult, because all the underwater stuff
is necessary to keep the tip above water in the first place
— otherwise, the whole thing will just sink! To push
this metaphor to its limits, the following are a few illu-
minating shafts of light down into this important under-
water realm, and some ideas about how they keep the
“tip” afloat. The aspects of cognition we will discuss

are:

Parallelism

Gradedness

Interactivity

Competition

Learning

The final chapter in the topic will revisit some of
these issues again with the benefit of what comes in be-
tween.

Lest you get the impression that computational cog-
nitive neuroscience is unable to say anything useful
about conscious experience, or that we do not address
this phenomenon in this topic, we note that chapter 11
deals specifically with “higher-level cognition,” which
is closely associated with conscious experience. There
we present a set of ideas and models that provide the
bridge between the basic mechanisms and principles de-
veloped in the rest of the topic, and the more sequential,
discrete, and focused nature of conscious experience.
We view these properties as arising partly due to spe-
cializations of particular brain areas (the prefrontal cor-
tex and the hippocampus), and partly as a result of the
emergent phenomena that arise from the basic proper-
ties of neural processing as employed in a coordinated

1.6 Motivating Cognitive Phenomena and Their

Biological Bases

Several aspects of human cognition are particularly sug-
gestive of the kinds of neural mechanisms described in
this text. We briefly describe some of the most impor-
tant of these aspects here to further motivate and high-
light the connections between cognition and neurobiol-
ogy. However, as you will discover, these aspects of
cognition are perhaps not the most obvious to the av-
erage person. Our introspections into the nature of our
own cognition tend to emphasize the “conscious” as-
pects (because this is by definition what we are aware