Decoding reward-curiosity conflict in decision-making from irrational behaviors

Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment, even if reward is less expected, owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a novel decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model successfully described irrational behaviors depending on the curiosity level. We then proposed a machine learning method to decode temporal curiosity from behavioral data, which enables us to quantitatively compare estimated curiosity and neural activities. By applying it to rat behavioral data, we found that the irrational choices sticking to one option was reflected to the negative curiosity level. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts. Specifically, it could be effective in diagnosing mental disorders.