Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, 8006 Zurich, Switzerland; Department of Economics, The Ohio State University, 1945 North High Street, 410 Arps Hall, Columbus, OH 43210, USA.
Department of Economics, The Ohio State University, 1945 North High Street, 410 Arps Hall, Columbus, OH 43210, USA; Department of Psychology, The Ohio State University, 1827 Neil Avenue, 200E Lazenby Hall, Columbus, OH 43210, USA.
Neuron. 2018 Jun 27;98(6):1282-1293.e4. doi: 10.1016/j.neuron.2018.05.013. Epub 2018 May 31.
The brain is often able to learn complex structures of the environment using a very limited amount of evidence, which is crucial for model-based planning and sequential prediction. However, little is known about the neurocomputational mechanisms of deterministic sequential prediction, as prior work has primarily focused on stochastic transition structures. Here we find that human subjects' beliefs about a sequence of states, captured by reaction times, are well explained by a Bayesian pattern-learning model that tracks beliefs about both the current state and the underlying structure of the environment, taking into account prior beliefs about possible patterns in the sequence. Using functional magnetic resonance imaging, we find distinct neural signatures of uncertainty computations on both levels. These results support the hypothesis that structure learning in the brain employs Bayesian inference.
大脑通常能够使用非常有限的证据来学习环境的复杂结构，这对于基于模型的规划和序列预测至关重要。然而，关于确定性序列预测的神经计算机制知之甚少，因为之前的工作主要集中在随机转移结构上。在这里，我们发现，通过反应时间捕捉到的人类受试者对状态序列的信念可以很好地用一个贝叶斯模式学习模型来解释，该模型跟踪对当前状态和环境的基础结构的信念，同时考虑到对序列中可能模式的先验信念。使用功能磁共振成像，我们在两个层面上都发现了不确定性计算的独特神经特征。这些结果支持了这样的假设，即大脑中的结构学习采用贝叶斯推理。
