Meyniel Florent, Dehaene Stanislas
Cognitive Neuroimaging Unit, NeuroSpin Center, Institute of Life Sciences Frédéric Joliot, Fundemental Research Division, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, INSERM, Université Paris-Sud, Université Paris-Saclay, 91191 Gif/Yvette, France;
Chair of Experimental Cognitive Psychology, Collège de France, 75005 Paris, France.
Proc Natl Acad Sci U S A. 2017 May 9;114(19):E3859-E3868. doi: 10.1073/pnas.1615773114. Epub 2017 Apr 24.
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 ideal-observer 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 confidence-weighting 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.
当世界随机且不断波动时，学习就变得困难。经典的学习算法，如具有恒定学习率的增量规则，并非最优。从数学角度来看，最优学习规则需要根据先验知识和新传入证据各自的可靠性对它们进行加权。这种“置信度加权”意味着要对所学内容的可靠性进行准确估计。在此，我们使用功能磁共振成像（fMRI）和理想观察者分析，证明大脑的学习算法依赖于置信度加权。在功能磁共振成像扫描仪中，成年人类试图学习听觉或视觉序列背后的转移概率，并报告他们对这些估计的置信度。他们知道这些转移概率可能在不可预测的时刻同时发生变化，因此学习问题本质上是分层的。主观置信度报告紧密遵循理想观察者得出的预测。特别是，受试者能够按照贝叶斯最优推理的要求，为每个学到的转移概率赋予不同程度的置信度。不同的脑区追踪给定当前预测时新观察结果的可能性以及对这些预测的置信度。这两种信号在右下额叶回中结合，在那里它们按照置信度加权模型运行。这个脑区还呈现出一个分层过程的特征，该过程能够区分不同的不确定性来源。总之，我们的结果提供了证据，表明置信感是人类大脑概率学习的一个基本要素，并且右下额叶回承载了一种用于听觉和视觉序列的基于置信度的统计学习算法。
