Adaptive neural coding: from biological to behavioral decision-making

- Empirical decision-making often violates normative choice theory.
- Suboptimal choice can arise from contextual value coding in neural activity.
- Adaptive computations can maximize information coding in constrained neural circuits.
- Biological choice reflects a tradeoff between performance and the cost of computation.

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior.