Brain networks for confidence weighting and hierarchical inference during probabilistic learning
Brain networks for confidence weighting and hierarchical inference during probabilistic learning
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DOI：
10.1073/pnas.1615773114
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发表时间：
2017-05-09
影响因子：
11.1
通讯作者：
Dehaene, Stanislas
中科院分区：
文献类型：
--
作者：
Meyniel, Florent;Dehaene, Stanislas
当世界随机而无休止地波动时，学习是困难的。经典的学习算法，如具有恒定学习速率的delta规则，不是最优的。从数学上讲，最优学习规则需要根据先验知识和输入证据各自的可靠性对其进行加权。这种“置信度加权”意味着对所学到的知识的可靠性保持准确的估计。在这里，使用fMRI和idealobserver分析，我们证明了大脑的学习算法依赖于置信加权。在功能磁共振成像扫描仪中，成年人试图学习听觉或视觉序列的转移概率，并报告他们对这些估计的信心。他们知道这些转移概率可能在不可预测的时刻同时改变，因此学习问题本质上是分层的。主观信心报告严格遵循理想观察者的预测。特别是，受试者设法将不同程度的信心，以每个学习的转移概率，所需的贝叶斯最优推理。不同的大脑区域跟踪给定当前预测的新观察的可能性，以及对这些预测的信心。这两个信号结合在右额下回，在那里他们在协议的confidenceweighting模型。这个大脑区域也呈现出一个分层过程的特征，这个过程可以解开不同的不确定性来源。总之，我们的研究结果提供的证据表明，信心的感觉是在人类大脑中的概率学习的一个重要组成部分，右额下回主机的听觉和视觉序列的基于信心的统计学习算法。
Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an idealobserver analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidenceweighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.