Relative value coding is governed by divisive normalization, a computational algorithm widely described in sensory cortices, suggesting a common cortical mechanism for contextual processing. Normalized value coding provides insight into both the physiological mechanism of decision-making and the efficiency of choice behavior. Using computational modeling and human choice experiments, we find that the the combination of normalization and stochastic variability in neural value coding can explain many context-dependent violations of rational choice theory. Ongoing projects include the development of normalization-based behavioral strategies to remediate context-dependent choice inefficiencies and examining value coding in human subjects using neuroimaging techniques.