在生成式AI中复制人类的社会感知：评估效价-支配性模型
Replicating Human Social Perception in Generative AI: Evaluating the Valence-Dominance Model
AI总结：
本研究探讨多模态生成式AI能否复制效价-支配性社会感知模型，通过PCA发现其提取维度与理论结构高度一致，但部分模型和地区出现第三成分，证明AI可复制人类社会感知关键特征。
摘要：
随着人工智能（AI）的持续发展——特别是在生成式模型方面——一个悬而未决的问题是，这些系统能否复制人类社会感知的基础模型。社会认知中一个成熟的框架表明，社会判断主要沿着两个维度组织：效价（例如，信任度、温暖）和支配性（例如，权力、果断性）。本研究考察了多模态生成式AI系统在评估面部图像时能否重现这种效价-支配性结构，以及它们的表征如何与全球各地区观察到的情况相一致。通过主成分分析（PCA），我们发现提取出的维度密切反映了效价和支配性的理论结构，其特质载荷与既定定义相一致。然而，许多世界地区和生成式AI模型也展现出了第三个成分，其性质和意义值得进一步研究。这些发现表明，多模态生成式AI系统能够复制人类社会感知的关键方面，这引发了关于其对AI驱动的决策和人机交互影响的重要问题。
英文摘要：
As artificial intelligence (AI) continues to advance--particularly in generative models--an open question is whether these systems can replicate foundational models of human social perception. A well-established framework in social cognition suggests that social judgments are organized along two primary dimensions: valence (e.g., trustworthiness, warmth) and dominance (e.g., power, assertiveness). This study examines whether multimodal generative AI systems can reproduce this valence-dominance structure when evaluating facial images and how their representations align with those observed across world regions. Through principal component analysis (PCA), we found that the extracted dimensions closely mirrored the theoretical structure of valence and dominance, with trait loadings aligning with established definitions. However, many world regions and generative AI models also exhibited a third component, the nature and significance of which warrant further investigation. These findings demonstrate that multimodal generative AI systems can replicate key aspects of human social perception, raising important questions about their implications for AI-driven decision-making and human-AI interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
