Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations.

感知偏差被广泛认为提供了了解感知背后的神经计算的窗口。为了理解这些偏差，之前的工作提出了许多概念上不同的、甚至看似矛盾的解释，包括对贝叶斯先验的吸引力、由于有效编码而对先验的排斥以及有限范围内的集中趋势效应。我们提出了一种统一的贝叶斯感知估计偏差理论，该理论源自第一原理。我们从理论上证明了感知偏差的加法分解为对先验的吸引、远离编码精度高的区域的排斥以及远离边界的回归。结果揭示了预测感知偏差方向的简单而普遍的规则。我们的理论解释并产生了关于各种刺激属性（包括方向、颜色和强度）感知偏差的新见解。这些结果为贝叶斯计算的神经实现提供了重要的约束。