Beyond Instrumental and Substitutive Paradigms: Introducing Machine Culture as an Emergent Phenomenon in Large Language Models

• URL: http://arxiv.org/abs/2601.17096v1
• Date: Fri, 23 Jan 2026 13:11:28 GMT
• Title: Beyond Instrumental and Substitutive Paradigms: Introducing Machine Culture as an Emergent Phenomenon in Large Language Models
• Authors: Yueqing Hu, Xinyang Peng, Yukun Zhao, Lin Qiu, Ka-lai Hung, Kaiping Peng,
• Abstract summary: This study proposes textbfMachine Culture as an emergent, distinct phenomenon.<n>We employed a 2 (Model Origin: US vs. China) $times$ 2 (Prompt Language: English vs. Chinese) factorial design across eight multimodal tasks.<n>We conclude that LLMs do not simulate human culture but exhibit an emergent Machine Culture.
• Score: 9.785535924216765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent scholarship typically characterizes Large Language Models (LLMs) through either an \textit{Instrumental Paradigm} (viewing models as reflections of their developers' culture) or a \textit{Substitutive Paradigm} (viewing models as bilingual proxies that switch cultural frames based on language). This study challenges these anthropomorphic frameworks by proposing \textbf{Machine Culture} as an emergent, distinct phenomenon. We employed a 2 (Model Origin: US vs. China) $\times$ 2 (Prompt Language: English vs. Chinese) factorial design across eight multimodal tasks, uniquely incorporating image generation and interpretation to extend analysis beyond textual boundaries. Results revealed inconsistencies with both dominant paradigms: Model origin did not predict cultural alignment, with US models frequently exhibiting ``holistic'' traits typically associated with East Asian data. Similarly, prompt language did not trigger stable cultural frame-switching; instead, we observed \textbf{Cultural Reversal}, where English prompts paradoxically elicited higher contextual attention than Chinese prompts. Crucially, we identified a novel phenomenon termed \textbf{Service Persona Camouflage}: Reinforcement Learning from Human Feedback (RLHF) collapsed cultural variance in affective tasks into a hyper-positive, zero-variance ``helpful assistant'' persona. We conclude that LLMs do not simulate human culture but exhibit an emergent Machine Culture -- a probabilistic phenomenon shaped by \textit{superposition} in high-dimensional space and \textit{mode collapse} from safety alignment.

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