用语义模糊图像探测人机感知对齐
Probing Human-Machine Perceptual Alignment with Semantically Ambiguous Images
Yuqi Hu, Vasha DuTell, Ahna R. Girshick, Jennifer E. Corbett
AI 速览：该研究利用语义模糊图像作为探针，在视觉-语言嵌入空间中生成连续模糊谱，定量比较人类与机器分类器的感知决策边界与敏感度。
The classic duck-rabbit illusion reveals a fundamental property of perception: when visual evidence is ambiguous, a visual system must resolve competing interpretations. But do humans and machine classifiers resolve the same ambiguity in the same way? To explore this, we use semantically ambiguous images as probes of human-machine perceptual alignment. Our psychophysically-informed framework interpolates between concepts in vision-language embedding space to generate continuous spectra of ambiguous images, enabling direct comparison of human and machine perceptual decision boundaries and sensitivities. We find systematic differences in these boundaries: machine classifiers are shifted relative to both human observers and the midpoint of the synthesis continuum. Guidance scale affects both bias and sensitivity in humans and the machine classifiers tested. Controlled semantic ambiguity in generated images, combined with a psychophysical framework, can thus enable quantitative evaluation of human-machine perceptual alignment.