研究哪些文本特征让人觉得AI有意识，发现自我反思和情感表达最关键。
Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses
- 通过问卷分析8个文本特征对人类感知意识的影响
- 自我反思和情感表达显著提升意识感知，知识过多反而降低
- 不同人群对特征权重差异大，懂AI的人更易认为AI有意识
本研究定量分析了大语言模型（LLM）生成文本中哪些特征会引发人类对人工智能主观意识的感知。基于与Claude 3 Opus对话的99段文本，聚焦元认知自我反思、逻辑推理、共情、情感性、知识、流畅性、意外性和主观表达性八项特征，对123名参与者开展调查。通过回归与聚类分析，发现元认知自我反思及自我情绪表达显著提升感知意识，而过度强调知识则降低感知。参与者被划分为七个子群，各具不同的特征权重模式。此外，先前对LLM了解较多及频繁使用基于LLM的聊天机器人者，更倾向于判定AI具有意识。研究揭示了感知AI意识的多维度与个体化特征，为理解人机交互的心理社会影响提供基础。
原文摘要 · Abstract (English)
This study quantitively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with Claude 3 Opus and focusing on eight features -- metacognitive self-reflection, logical reasoning, empathy, emotionality, knowledge, fluency, unexpectedness, and subjective expressiveness -- we conducted a survey with 123 participants. Using regression and clustering analyses, we investigated how these features influence participants' perceptions of AI consciousness. The results reveal that metacognitive self-reflection and the AI's expression of its own emotions significantly increased perceived consciousness, while a heavy emphasis on knowledge reduced it. Participants clustered into seven subgroups, each showing distinct feature-weighting patterns. Additionally, higher prior knowledge of LLMs and more frequent usage of LLM-based chatbots were associated with greater overall likelihood assessments of AI consciousness. This study underscores the multidimensional and individualized nature of perceived AI consciousness and provides a foundation for better understanding the psychosocial implications of human-AI interaction.
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