论文标题
概率表示作为高级视觉的基础
Probabilistic representations as building blocks for higher-level vision
论文作者
论文摘要
当前的感知理论表明，大脑将世界的特征表示为概率分布，但是这种不确定的基础可以为日常视觉提供基础吗？感知对象和场景不仅需要了解特征（例如颜色）的分布方式，还需要了解它们的位置以及与哪些其他功能相结合。使用贝叶斯计算模型，我们恢复了人类观察者使用的概率表示，以在干扰素之间寻找奇数刺激。重要的是，我们发现大脑会在特征维度和空间位置之间整合信息，从而与无法进行信息集成相比，导致更精确的表示。我们还发现了代表性的不对称和偏见，展示了它们的空间组织，并解释了这种结构如何反对视觉表示的“摘要统计数据”。我们的结果证实，概率编码的视觉特征与其他特征和特定位置结合，提供了强有力的证明，以表明概率表示如何成为高级视觉的基础。
Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined with. Using a Bayesian computational model, we recovered probabilistic representations used by human observers to search for odd stimuli among distractors. Importantly, we found that the brain integrates information between feature dimensions and spatial locations, leading to more precise representations compared to when information integration is not possible. We also uncovered representational asymmetries and biases, showing their spatial organization and explain how this structure argues against "summary statistics" accounts of visual representations. Our results confirm that probabilistically encoded visual features are bound with other features and to particular locations, providing a powerful demonstration of how probabilistic representations can be a foundation for higher-level vision.
