Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
Department of AI-Brain Integration, Advanced Telecommunications Research Institute International, Kyoto, 619-0288, Japan.
Commun Biol. 2024 May 21;7(1):614. doi: 10.1038/s42003-024-06316-0.
Uncertainty abounds in the real world, and in environments with multiple layers of unobservable hidden states, decision-making requires resolving uncertainties based on mutual inference. Focusing on a spatial navigation problem, we develop a Tiger maze task that involved simultaneously inferring the local hidden state and the global hidden state from probabilistically uncertain observation. We adopt a Bayesian computational approach by proposing a hierarchical inference model. Applying this to human task behaviour, alongside functional magnetic resonance brain imaging, allows us to separate the neural correlates associated with reinforcement and reassessment of belief in hidden states. The imaging results also suggest that different layers of uncertainty differentially involve the basal ganglia and dorsomedial prefrontal cortex, and that the regions responsible are organised along the rostral axis of these areas according to the type of inference and the level of abstraction of the hidden state, i.e. higher-order state inference involves more anterior parts.
现实世界充满了不确定性，在具有多层不可观测隐藏状态的环境中，决策需要基于相互推断来解决不确定性。本文关注于一个空间导航问题，我们开发了一个老虎迷宫任务，该任务涉及从概率不确定的观测中同时推断局部隐藏状态和全局隐藏状态。我们通过提出一个分层推理模型采用贝叶斯计算方法。将其应用于人类任务行为以及功能磁共振脑成像，使我们能够分离与隐藏状态的强化和信念再评估相关的神经关联。成像结果还表明，不同层次的不确定性会使基底神经节和背内侧前额叶皮层产生差异，并且根据隐藏状态的推理类型和抽象程度，负责的区域会沿着这些区域的额轴组织，即高阶状态推断涉及更靠前的部分。
