Bayesian motion estimation accounts for a surprising bias in 3D vision
Bayesian motion estimation accounts for a surprising bias in 3D vision
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DOI：
10.1073/pnas.0804378105
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发表时间：
2008-08-19
影响因子：
11.1
通讯作者：
Buelthoff, Heinrich H.
中科院分区：
文献类型：
--
作者：
Welchman, Andrew E.;Lam, Judith M.;Buelthoff, Heinrich H.
确定移动物体的接近是一项至关重要的生存技能，这取决于大脑结合横向平移和深度运动的信息。考虑到感知运动对于避障的重要性，令人惊讶的是人类会犯错误，报告说当物体与人类的头部发生碰撞时，物体会错过他们。在这里，我们提供的证据表明，当参与者估计深度运动时观察到的偏差是由于大脑使用有利于慢速度的“先验”造成的。我们使用视觉系统慢速先验形状的独立估计参数制定了用于计算 3D 运动的贝叶斯模型。我们在评估 3D 运动估计中的灵敏度和偏差的单独实验中证明了该模型在解释人类行为方面的成功。我们的结果表明，3D 运动感知中令人惊讶的感知误差反映了估计环境属性时先验概率的重要性。
Determining the approach of a moving object is a vital survival skill that depends on the brain combining information about lateral translation and motion-in-depth. Given the importance of sensing motion for obstacle avoidance, it is surprising that humans make errors, reporting an object will miss them when it is on a collision course with their head. Here we provide evidence that biases observed when participants estimate movement in depth result from the brain's use of a "prior" favoring slow velocity. We formulate a Bayesian model for computing 3D motion using independently estimated parameters for the shape of the visual system's slow velocity prior. We demonstrate the success of this model in accounting for human behavior in separate experiments that assess both sensitivity and bias in 3D motion estimation. Our results show that a surprising perceptual error in 3D motion perception reflects the importance of prior probabilities when estimating environmental properties.