AI能像人一样判断面孔的亲和力和主导性。
Replicating Human Social Perception in Generative AI: Evaluating the Valence-Dominance Model
- 用主成分分析提取AI对人脸的评价维度。
- 发现多数AI模型与人类一致，识别出亲和力与主导性。
- 部分区域和模型出现第三维度，需进一步研究。
随着生成式AI的快速发展，一个关键问题是这些系统能否复现人类社会认知的基础模型。已有研究表明，社会判断主要沿两个维度展开：亲和力（如可信度、温暖）和主导性（如权力、果断）。本研究考察了多模态生成式AI在评估人脸图像时是否能再现这一亲和力-主导性结构，并分析其跨地区一致性。通过主成分分析（PCA），我们发现提取的维度与理论框架高度吻合，特征载荷与既定定义一致。然而，多个地区和生成式AI模型也表现出第三成分，其性质与意义有待深入探讨。结果表明，多模态生成式AI能够复现人类社会感知的关键特征，引发了关于其在AI决策及人机交互中影响的重要思考。
原文摘要 · Abstract (English)
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.
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