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. 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.
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. 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.