Measured materials are used in computer graphics to enhance the realism of synthetic images. They are often approximated with analytical models to improve storage efficiency and allow for importance sampling. However, the error metrics used in the optimization procedure do not have a perceptual basis and the obtained results do not always correspond to the best visual match. In this paper we present a first steps towards creating a perceptually-based metric for BRDF modeling. First, a set of measured materials were approximated with different error metrics and analytical BRDF models. Next, a psychophysical study was performed to compare the visual fidelity obtained using different error metrics and models. The results of this study show that the cube root metric leads to a better perceptual approximation than other RMS based metrics, independently of the analytical BRDF model used. More benefit of using the cube root metric compared to the RMS based metrics is obtained for sharp specular lobes, and as the specular lobe broadens the benefit of using the cube root metric decreases. The use of the cube root error metric will improve the visual fidelity of renderings made using BRDF approximations and expand the usage of measured materials in computer graphics.