Efficient coding of cognitive variables underlies dopamine response and choice behavior

Reward expectations based on internal knowledge of the external environment are a core component of adaptive behavior. However, internal knowledge may be inaccurate or incomplete due to errors in sensory measurements. Some features of the environment may also be encoded inaccurately to minimize representational costs associated with their processing. In this study, we investigated how reward expectations are affected by features of internal representations by studying behavior and dopaminergic activity while mice make time-based decisions. We show that several possible representations allow a reinforcement learning agent to model animals' overall performance during the task. However, only a small subset of highly compressed representations simultaneously reproduced the co-variability in animals' choice behavior and dopaminergic activity. Strikingly, these representations predict an unusual distribution of response times that closely match animals' behavior. These results inform how constraints of representational efficiency may be expressed in encoding representations of dynamic cognitive variables used for reward-based computations. Sensory systems compress representations while preserving information. Modeling of dopamine neuron responses and behavior during decision-making indicates that cognitive systems also compress representations as long as overall rewards are preserved.

Automated summary

Reward expectations based on internal knowledge are important for adaptive behavior, but internal knowledge may be inaccurate. This study found that specific highly compressed internal representations can reproduce both choice behavior and dopaminergic activity during time-based decision-making in mice. These findings demonstrate how representational efficiency constraints are expressed in reward-based computations.