A Tale of Two Environments: Divisive Normalization and the (In)Flexibility of Choice

Abstract

The Divisive Normalization (DN) function has been described as a "canonical neural computation" in the brain that achieves efficient representations of sensory and choice stimuli. Recent theoretical work indicates that it efficiently encodes a specific class of Pareto-distributed stimuli. Does the brain shift to different encoding functions in other types of environments, or is there evidence for DN encoding in other types of environments? In this paper, using a within-subject choice experiment, we show evidence of the latter. Our subjects made decisions in two distinct choice environments with choice sets either drawn from a Pareto distribution or from a uniform distribution. Our results indicate that subjects choices are better described by a divisive coding strategy in both environments. Moreover, subjects appeared to calibrate a DN function to match, as closely as possible, the actual statistical properties of each environment. These results suggest that the nervous system may be constrained to use divisive representations under all conditions. Significance StatementHow does the frequency with which we encounter different kinds of decision problems affect how the brain represents those problems? Recent empirical findings suggest that we adapt our internal representations to match the environments in which we are making choices. Theoretical work has shown that one form of internal representation, called divisive normalization, provides an optimal adaptation when making choices in a specific class of environments. Using a stylized experimental design, subjects faced two distinct choice environments, each characterized by different statistical properties. Our findings show humans appear to use the same mechanism in both environments, suggesting that a divisive representation may be a fixed feature of human cognition.