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Advantages:

 

Models help us to understand phenomena.

A computational model can provide novel sources of insight into behavior, for example by providing a counterintuitive explanation of a phenomenon, or by reconciling seemingly contradictory phenomena (e.g., by complex interactions among components). Seemingly different phenomena can also be related to each other in nonobvious ways via a common set of computational mechanisms.

      Computational models can also be lesioned and then tested, providing insight into behavior following specific types of brain damage, and in turn, into normal functioning. Often, lesions can have nonobvious effects that computational models can explain.

By virtue of being able to translate between functional desiderata and the biological mechanisms that implement them, computational models enable us to understand not just how the brain is structured, but why it is structured in the way it is.

Models deal with complexity.

  A computational model can deal with complexity in ways that verbal arguments cannot, producing satisfying explanations of what would otherwise just be vague hand-wavy arguments. Further, computational models can handle complexity across multiple levels of analysis, allowing data across these levels to be integrated and related to each other. For example, the computational models in this book show how biological properties give rise to cognitive behaviors in ways that would be impossible with simple verbal arguments.

Models are explicit.

Making a computational model forces you to be explicit about your assumptions and about exactly how the relevant processes actually work. Such explicitness carries with it many potential advantages.

  First, explicitness can help in deconstructing psychological concepts that may rely on homunculi to do their work. A homunculus is a ``little man,'' and many theories of cognition make unintended use of them by embodying particular components (often ``boxes'') of the theory with magical powers that end up doing all the work in the theory. A canonical example is the ``executive'' theory of prefrontal cortex function: if you posit an executive without explaining how it makes all those good decisions and coordinates all the other brain areas, you haven't explained too much (you might as well just put pinstripes and a tie on the box).

  Second, an explicitly specified computational model can be run to generate novel predictions. A computational model thus forces you to accept the consequences of your assumptions. If the model must be modified to account for new data, it becomes very clear exactly what these changes are, and the scientific community can more easily evaluate the resulting deviance from the previous theory. Predictions from verbal theories can be tenuous due to lack of specificity and the flexibility of vague verbal constructs.

Third, explicitness can contribute to a greater appreciation for the complexities of otherwise seemingly simple processes. For example, before people tried to make explicit computational models of object recognition, it didn't seem that difficult or interesting a problem -- there is an anecdotal story about a scientist in the `60s who was going to implement a model of object recognition over the summer. Needless to say, he didn't succeed.

Fourth, making a computational model forces you to confront aspects of the problem that you might have otherwise ignored or considered to be irrelevant. Although one sometimes ends up using simplifications or stand-ins for these other aspects (see the list of problems that follows), it can be useful to at least confront these problems.

Models allow control.

In a computational model you can control many more variables much more precisely than you can with a real system, and you can replicate results precisely. This enables you to explore the causal role of different components in ways that would otherwise be impossible.

Models provide a unified framework.

    As we discussed earlier, there are many advantages to using a single computational framework to explain a range of phenomena. In addition to providing a more stringent test of a theory, it encourages parsimony and 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 inconsistency in a purely verbal theory -- we have a hard time keeping track of everything. However, a computational model reveals inconsistencies quite readily, because everything has to hang together and actually work.

 


next up previous contents
Next: Problems: Up: General Issues in Computational Previous: General Issues in Computational

Randall C. O'Reilly
Fri Apr 28 14:15:16 MDT 2000