This paper has three parts. Part 1 contains musings on the title of this conference, 'Computational Vision Based on Neurobiology.' Progress has been slow in computational vision because very difficult problems are being tackled before the simpler problems have been solved. Part 2 is about one of these simpler problems in computational vision that is largely neglected by computational vision researchers: the development of a fidelity metric. This is an enterprise perfectly suited for computational vision with the side benefit of having spectacular practical implications. Part 3 discusses the research my colleagues and I have been pursuing for the past several years on the Test-Pedestal approach to spatial vision. This approach can be helpful as a guide for the development of a fidelity metric. A number of experiments using this approach are discussed. These examples demonstrate both the power and the pitfalls of the Test-Pedestal approach.