Optimal policy for multi-alternative decisions

Abstract

Every-day decisions frequently require choosing among multiple alternatives. Yet, the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions. This strategy requires evidence accumulation to nonlinear, time-dependent bounds, that trigger choices. A geometric symmetry in those boundaries allows the optimal strategy to be implemented by a simple neural circuit involving a normalization with fixed decision bounds and an urgency signal. The model captures several key features of the response of decision-making neurons as well as the increase in reaction time as a function of the number of alternatives, known as Hicks law. In addition, we show that, in the presence of divisive normalization and internal variability, our model can account for several so called irrational behaviors such as the similarity effect as well as the violation of both the independent irrelevant alternative principle and the regularity principle.