计算模型揭示了直观的物理和统计线索,分别有助于液体的视觉感知
Yuting Zhang1, Wenyan Bi1, Yuyang Miao2
1Department of Psychology, Yale University, United States of America.
Cognition
|February 7, 2026
概括
液体感知的计算模型显示,人类使用直观的物理学,而不仅仅是图像统计,以了解粘度. 一个新的基于物理学的模型,Ripple,比统计模型更好地解释人类的判断.
科学领域:
- 计算机视觉 计算机视觉
- 认知科学 认知科学
- 流体动力学 流体动力学
背景情况:
- 由于液体的复杂动力学,液体感知在计算视觉中未被充分探索.
- 现有的模型通常依赖于高级图像和运动统计数据.
研究的目的:
- 调查液体粘度感知的计算基础.
- 将统计模型与基于物理的液体感知模型进行比较.
主要方法:
- 开发并测试了液体粘度感知的计算模型.
- 进行了一项行为研究,人类在不同的液体动画中判断粘度相似性.
- 将模型性能与人类判断进行比较.
主要成果:
- 一个基于物理学的新型模型Ripple显著解释了超出统计模型的人类判断.
- 统计粘度表示在不同的模型架构 (DNN,基础模型) 中趋同.
- 瑞波币的表现仍然与统计模型不同.
结论:
- 液体感知可能不仅仅涉及图像统计.
- 基于模拟的直观物理学在理解液体特性方面发挥着至关重要的作用.
- 未来的研究应该探索视觉感知任务的基于物理的模型.
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