I will describe a range of models, from cellular to cortical scales, that illuminate how we accumulate evidence and make simple decisions. Large networks composed of individual spiking neurons can capture biophysical details of synaptic transmission and neuromodulation, but their complexity renders them opaque to analysis. Employing methods of mean field and dynamical systems theory, I will argue that these high-dimensional stochastic differential equations can be approximately to simple drift-diffusion (DD) processes like those used to fit behavioral data in cognitive psychology. The DD models are analytically tractable, coincide with optimal methods from statistical decision theory, and prompt new experiments as well as questions on why we fail to optimize. If time permits, I will describe work in progress on a multi-area model of attention and descision making. The talk will draw on joint work with Fuat Balci, Rafal Bogacz, Jonathan Cohen, Philip Eckhoff, Sam Feng, Mike Schwemmer, Eric Shea-Brown, Patrick Simen, Marieke van Vugt, KongFatt Wong-Lin and Miriam Zacksenhouse. Research supported by NIMH and AFOSR.