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The Cultural Psychology of Large Language Models: Is ChatGPT a Holistic or Analytic Thinker?

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arxiv 2308.14242 v1 pith:Q3U65OL3 submitted 2023-08-28 cs.AI cs.CL

classification cs.AIcs.CL
keywords chatgptcognitiveresearchculturalmodelsthinkingholisticprocess
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The prevalent use of Large Language Models (LLMs) has necessitated studying their mental models, yielding noteworthy theoretical and practical implications. Current research has demonstrated that state-of-the-art LLMs, such as ChatGPT, exhibit certain theory of mind capabilities and possess relatively stable Big Five and/or MBTI personality traits. In addition, cognitive process features form an essential component of these mental models. Research in cultural psychology indicated significant differences in the cognitive processes of Eastern and Western people when processing information and making judgments. While Westerners predominantly exhibit analytical thinking that isolates things from their environment to analyze their nature independently, Easterners often showcase holistic thinking, emphasizing relationships and adopting a global viewpoint. In our research, we probed the cultural cognitive traits of ChatGPT. We employed two scales that directly measure the cognitive process: the Analysis-Holism Scale (AHS) and the Triadic Categorization Task (TCT). Additionally, we used two scales that investigate the value differences shaped by cultural thinking: the Dialectical Self Scale (DSS) and the Self-construal Scale (SCS). In cognitive process tests (AHS/TCT), ChatGPT consistently tends towards Eastern holistic thinking, but regarding value judgments (DSS/SCS), ChatGPT does not significantly lean towards the East or the West. We suggest that the result could be attributed to both the training paradigm and the training data in LLM development. We discuss the potential value of this finding for AI research and directions for future research.

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  1. Psychologically Enhanced AI Agents

    cs.AI 2025-09 conditional novelty 4.0 of 10

    MBTI personality prompts measurably change how LLM agents write stories and play strategic games, with self-reflection before communication supporting cooperative behavior.

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