小柯机器人

北京大学Lusha Zhu研究团队揭示了社交网络实时分布式学习的神经计算机制。相关论文于2023年2月16日发表于国际学术期刊《自然—神经科学》杂志。

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，研究人员探究了人类如何在具有不同拓扑结构的七节点网络上通过观察他人的决策来学习。研究表明，社交网络上的学习与一个由错误驱动的观察学习过程相似，并且外侧前额叶皮层中编码的动作预测误差为该假设提供了支持。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于连接良好的细胞，但仅限于社会观察所包含的二手、可能相互交织的信息。

这些数据表明在相互关联的社会中，存在基于网络过滤信息来源的神经计算机制，这可能会导致有偏见的学习和错误信息的传播。

据悉，社交网络通过限制人们了解的信息和来源重塑人们的决策。然而，尚不清楚网络结构影响个人学习和决策的机制。

附：英文原文

Title: Neurocomputational mechanism of real-time distributed learning on social networks

Author: Jiang, Yaomin, Mi, Qingtian, Zhu, Lusha

Issue&Volume: 2023-02-16

Abstract: 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.

DOI: 10.1038/s41593-023-01258-y

Source: https://www.nature.com/articles/s41593-023-01258-y

Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex