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Replicating Human Social Perception in Generative AI: Evaluating the Valence-Dominance Model

arXiv 2503.04842 ↗First posted 5 Mar 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

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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References · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  4. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  5. 2023Evaluating the Moral Beliefs Encoded in LLMscited by 215
  6. 2022ReCo: Region-Controlled Text-to-Image Generationcited by 179
  7. 2024MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Makingcited by 163
  8. 2022Thinking Fast and Slow in Large Language Modelscited by 137
  9. 2022Evaluating and Inducing Personality in Pre-trained Language Modelscited by 123
  10. 2023Art and the science of generative AI: A deeper divecited by 95
  11. 2023Improving Multimodal Datasets with Image Captioningcited by 93
  12. 2023Large language models predict human sensory judgments across six modalitiescited by 48
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  3. 2023Visual cognition in multimodal large language modelscited by 38
  4. 2022Evidence for Hypodescent in Visual Semantic AIcited by 30
  5. 2024Standards for Belief Representations in LLMscited by 30
  6. 2023Alignment with human representations supports robust few-shot learningcited by 25
  7. 2024Human-like object concept representations emerge naturally in multimodal large language modelscited by 25
  8. 2020Extracting low-dimensional psychological representations from convolutional neural networkscited by 16
  9. 2023Abstract Visual Reasoning Enabled by Languagecited by 15
  10. 2023Divergences in Color Perception between Deep Neural Networks and Humanscited by 4
  11. 2024Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AIcited by 4
  12. 2024Look Ahead Text Understanding and LLM Stitchingcited by 4
  13. 2023Harnessing Collective Intelligence Under a Lack of Cultural Consensuscited by 2

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