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Social cognitive theory explains how people learn and acquire knowledgethrough observing others. Recent years have witnessed the rapid development oflarge language models (LLMs), which suggests their potential significance asagents in the society. LLMs, as AI agents, can observe external information,which shapes their cognition and behaviors. However, the extent to whichexternal information influences LLMs' cognition and behaviors remains unclear.This study investigates how external statements and opinions influence LLMs'thoughts and behaviors from a social cognitive perspective. Three experimentswere conducted to explore the effects of external information on LLMs'memories, opinions, and social media behavioral decisions. Sociocognitivefactors, including source authority, social identity, and social role, wereanalyzed to investigate their moderating effects. Results showed that externalinformation can significantly shape LLMs' memories, opinions, and behaviors,with these changes mirroring human social cognitive patterns such as authoritybias, in-group bias, emotional positivity, and emotion contagion. Thisunderscores the challenges in developing safe and unbiased LLMs, and emphasizesthe importance of understanding the susceptibility of LLMs to externalinfluences.

Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns