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. 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. First, explicitness can help in deconstructing psychological concepts that may rely on homunculi to do their work. 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.