Neurocomputational mechanism of real-time distributed learning on social networks

• Published in: Nature neuroscience 2023-03, Vol.26 (3), p.506-516
• Main Authors: Jiang, Yaomin, Mi, Qingtian, Zhu, Lusha
• Format: Article
• Language: English
• Subjects: 59/36; 631/378/1595; 631/378/2649/1409; 631/378/3919; 631/477; Animal Genetics and Genomics; Behavioral Sciences; Biological Techniques; Biomedical and Life Sciences; Biomedicine; Cognitive tasks; Computational neuroscience; Cortex (cingulate); Decision analysis; Decision making; Functional magnetic resonance imaging; Gyrus Cinguli; Humans; Learning; Magnetic Resonance Imaging; Network analysis; Neurobiology; Neuroimaging; Neurosciences; Observational learning; Prefrontal Cortex; Real time; Social discrimination learning; Social interactions; Social Networking; Social networks; Social organization
• ISSN: 1097-6256; 1546-1726
• DOI: 10.1038/s41593-023-01258-y

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. Information flowing among connected peers shapes our beliefs and decisions. Jiang et al. show that learning on social networks is biased toward well-connected individuals, with the dorsal anterior cingulate cortex tracking connectedness on the network that routes learning.