Department of Anatomy and Neurobiology, Washington University, Saint Louis, Missouri, United States of America.
PLoS Comput Biol. 2010 Feb 19;6(2):e1000680. doi: 10.1371/journal.pcbi.1000680.
To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results.
为了稳定我们在空间中的位置，我们既利用视觉信息，也利用非视觉物理运动线索。然而，视觉线索可能是模棱两可的：视觉感知的运动可能是由自身运动、环境运动或两者共同引起的。神经系统必须将模糊的视觉线索与嘈杂的物理运动线索结合起来，以解决这种模糊性并控制我们的身体姿势。在这里，我们开发了一种贝叶斯模型，该模型形式化了神经系统如何解决这个问题。在这个模型中，神经系统结合感官线索来估计身体的运动。我们通过分析证明，只要视觉刺激与我们对身体运动感知的不确定性相比足够快，最优策略就是将视觉感知的运动速度按幂律进行加权。我们发现，该模型既可以解释我们的数据，也可以解释以前发表的结果中，实验诱导的视觉运动对人体姿势行为的非线性影响。
