School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.
IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.
Nat Neurosci. 2023 Mar;26(3):506-516. doi: 10.1038/s41593-023-01258-y. Epub 2023 Feb 16.
Social networks shape our decisions by constraining what information we learn and from whom. Yet, the mechanisms by which network structures affect individual learning and decision-making remain unclear. Here, by combining a real-time distributed learning task with functional magnetic resonance imaging, computational modeling and social network analysis, we studied how humans learn from observing others' decisions on seven-node networks with varying topological structures. We show that learning on social networks can be approximated by a well-established error-driven process for observational learning, supported by an action prediction error encoded in the lateral prefrontal cortex. Importantly, learning is flexibly weighted toward well-connected neighbors, according to activity in the dorsal anterior cingulate cortex, but only insofar as social observations contain secondhand, potentially intertwining, information. These data suggest a neurocomputational mechanism of network-based filtering on the sources of information, which may give rise to biased learning and the spread of misinformation in an interconnected society.
社交网络通过限制我们学习的信息和来源来塑造我们的决策。然而，网络结构影响个体学习和决策的机制仍不清楚。在这里，我们通过将实时分布式学习任务与功能磁共振成像、计算建模和社交网络分析相结合，研究了人类如何在具有不同拓扑结构的七个节点网络上通过观察他人的决策进行学习。我们表明，社交网络上的学习可以通过一个经过充分验证的错误驱动的观察学习过程来近似，该过程得到了侧前额叶皮层中编码的动作预测误差的支持。重要的是，根据背侧前扣带皮层的活动，学习可以灵活地加权给连接良好的邻居，但前提是社交观察包含二手的、可能交织在一起的信息。这些数据表明，基于信息来源的网络过滤具有神经计算机制，这可能导致在相互关联的社会中产生有偏见的学习和错误信息的传播。
