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also enables one to relate two seemingly disparate
phenomena by understanding them in light of a
common set of basic principles.
Also, it is often difficult for people to detect incon-
sistency in a purely verbal theory — we have a hard
time keeping track of everything. However, a compu-
tational model reveals inconsistencies quite readily,
because everything has to hang together and actually
work
.
of broader principles, not as an end unto itself, and
the way in which the model “uses” these principles
to account for the data must be made clear. Unfor-
tunately, this essential step of making the principles
clear and demonstrating their generality is often not
taken. This can be a difficult step for complex mod-
els (which is, after all, one of the advantages of mod-
eling in the first place!), but one made increasingly
manageable with advances in techniques for analyz-
ing models.
Models can do anything.
This criticism is inevitably
leveled at successful models. Neural network mod-
els do have a very large number of parameters in the
form of the adaptable weights between units. Also,
there are many degrees of freedom in the architec-
ture of the model, and in other parameters that deter-
mine the behavior of the units. Thus, it might seem
that there are so many parameters available that fit-
ting any given set of behavioral phenomena is unin-
teresting. Relatedly, because of the large number of
parameters, sometimes multiple different models can
provide a reasonable account of a given phenomenon.
How can one address this
indeterminacy
problem to
determine which is the “correct” model?
The general issues of adopting a principled, explana-
tory approach are relevant here — to the extent that
the model's behavior can be understood in terms of
more general principles, the success of the model
can be attributed to these principles, and not just to
random parameter fitting. Also, unlike many other
kinds of models, many of the parameters in the net-
work (i.e., the weights) are determined by principled
learning mechanisms, and are thus not “free” for the
modeler to set. In this topic, most of the models use
the same basic parameters for the network equations,
and the cases where different parameters were used
are strongly motivated.
The general answer to the
indeterminacy
problem
is that as you apply a model to a wider range of
data (e.g., different tasks, newly discovered biolog-
ical constraints), and in greater detail on each task
(e.g., detailed properties of the learning process), the
models will be much more strenuously tested. It thus
becomes much less likely that two different models
Problems:
Models are too simple.
Models, by necessity, involve
a number of simplifications in their implementation.
These simplifications may not capture all of the rele-
vant details of the biology, the environment, the task,
and so on, calling into question the validity of the
model.
Inevitably, this issue ends up being an empirical one
that depends on how wrong the simplifying assump-
tions are and how much they influence the results.
It is often possible for a model to make a perfectly
valid point while using a simplified implementation
because the missing details are simply not relevant
— the real system will exhibit the same behavior for
any reasonable range of detailed parameters. Fur-
thermore, simplification can actually be an important
benefit of a model — a simple explanation is easier to
understand and can reveal important truths that might
otherwise be obscured by details.
Models are too complex.
On the flip side, other critics
complain that models are too complex to understand
why they behave the way they do, and so they con-
tribute nothing to our understanding of human behav-
ior. This criticism is particularly relevant if a modeler
treats a computational model as a theory, and it points
to the mere fact that the model reproduces a set of
data as an explanation of this data.
However, this criticism is less relevant if the mod-
eler instead identifies and articulates the critical prin-
ciples that underly the model's behavior, and demon-
strates the relative irrelevance of other factors. Thus,
a model should be viewed as a concrete instantiation
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