Department of Physics, Emory University, Atlanta, GA 30322.
Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, GA 30322.
Proc Natl Acad Sci U S A. 2018 Sep 4;115(36):E8538-E8546. doi: 10.1073/pnas.1713020115. Epub 2018 Aug 20.
Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example, that smaller sensory errors are more readily corrected than larger errors and large abrupt (but not gradually introduced) errors lead to weak learning. Here, we propose a theory of sensorimotor learning that explains these observations. The theory posits that the animal controls an entire probability distribution of motor commands rather than trying to produce a single optimal command and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.
传统的感觉运动学习理论假设，动物在面对高斯感觉和运动噪声时，会利用感觉误差信号来寻找最佳的运动指令。然而，大多数这样的理论无法解释常见的行为观察结果，例如，较小的感觉误差比较大的误差更容易纠正，而大的突然（但不是逐渐引入的）误差会导致较弱的学习。在这里，我们提出了一种感觉运动学习理论，可以解释这些观察结果。该理论假设动物控制着整个运动指令的概率分布，而不是试图产生一个单一的最佳指令，并且当新的感觉信息可用时，通过贝叶斯推断产生学习。我们使用来自一只鸣禽，即贝氏金丝雀的数据来测试这个理论，这只鸟在使用微型耳机对听觉反馈进行干扰后，会调整其歌声的音高（基频）。我们观察到所唱音高的分布具有长的、非高斯的尾部，这在我们的理论中解释了所观察到的学习动态。此外，该理论对音高分布形状的动态做出了惊人的预测，我们通过实验证实了这些预测。
