Ideally, SCIENCE should be “quantitative”. However, in reality, there are many things that are difficult to quantify. Our feeling and conflict are the best examples. How do we define them quantitatively and how do they influence our decision making? To quantitatively define the conflict between reward and curiosity, we developed a decision-making model for a two-choice task by extending the free energy principle. The model described irrational behaviors depending on the curiosity level. We then proposed a machine learning method to decode temporal curiosity from behavioral data. Applying it to rat behavioral data, we found that the rat has negative curiosity, reflecting conservative selection sticking to more certain option and that the level of curiosity was upregulated by the expected future information obtained from uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts.