Higashino, I.; Ito, R.; Okochi, Y.; Inutsuka, K.; Yokoyama, H.; Kato, R.; Yada, Y.; Amemori, K.-i.; Naoki, H.

Humans and animals often face risky situations that require decision-making. Such decisions can be high-risk, high-return at some times, and low-risk, low-return at other times, depending on the balance between optimism and pessimism. However, how this optimism-pessimism bias is regulated across contexts remains unclear. Here, we introduced a computational model of decision-making in a risk-taking task based on the free-energy principle, together with a machine-learning framework that inversely estimates cognitive updating and optimism-pessimism bias from behavioral data. Applying this framework to monkey behavioral data, we found that a monkey quickly and accurately recognized the degree of risk, while frequently switching between optimism and pessimism during the task. In addition, we identified a characteristic control rule for optimism-pessimism bias that is distinct from reward-dependent regulation. Our framework provided a principled tool for understanding the latent cognitive processes underlying risky decision-making in animals and humans.