The mammalian brain is particularly well suited for managing streams of (often noisy) evidence, both internally and externally generated, to converge to a decision. This evidence accumulation process can adapt to changing environments and reward opportunities, mediated by cortico-basal-ganglia-thalamic (CBGT) circuits that both contribute to action selection and use feedback signals to modify future decisions. Dysfunction in how these pathways use feedback to guide future decisions is a primary mechanism for many addictive behaviors (e.g., opioid addiction, obesity). Our prior work has identified subsystems, which we call control ensembles, within the CBGT pathways that regulate dimensions of the evidence accumulation process, leading to various neural states with differing receptivity to the evidence streams that drive decisions, encapsulated in a particular decision policy. We propose a series of empirical and theoretical investigations that bridge across levels of analysis to understand the flow of information through CBGT circuits during the decision-making process. On the theory side we will use our models to understand the low-dimensional representational space of CBGT circuits throughout the decision-making process, using energy landscape models coupled with dimensionality reduction. Using computational models we will model decision trajectories through CBGT networks by applying entropy based analyses to the network behavior and building predictions of observed dynamics in both discrete and continuous actions (Specific Aim 1). Empirically, we will test predictions emerging from our network model and provide new observations to support model refinement using experiments in rodents (optogenetics, electrophysiology) as they perform both tasks with dynamic reward contingencies featuring either discrete choices or continuous motor control (Specific Aim 2). Our theoretical and empirical work will evolve in a mutual-development cycle, with theoretical experiments being used to derive novel behavioral and neural predictions and empirical experimental results being used to revise and update the generative model properties that lead to subsequent predictions.