 It is here that some philosophers and neuroscientists turned to computational methods (Churchland and Sejnowski 1992). One hope was that the way computational models have functioned in more developed sciences, like in physics, might provide a useful model. One computational resource that has usefully been applied in more developed sciences to similar “cross-level” concerns are dynamical systems. Global phenomena, such as large-scale meteorological patterns, have been usefully addressed as dynamical, nonlinear, and often chaotic interactions between lower-level physical phenomena. Addressing the interlocking levels of theory and explanation in the mind/brain using computational resources that have worked to bridge levels in more mature sciences might yield comparable results. This methodology is necessarily interdisciplinary, drawing on resources and researchers from a variety of levels, including higher ones like experimental psychology, artificial intelligence, and philosophy of science.