Department of Biology, Emory University, Atlanta, GA 30322, USA.
Proc Natl Acad Sci U S A. 2012 Dec 18;109(51):21099-103. doi: 10.1073/pnas.1213622109. Epub 2012 Dec 3.
The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.
大脑利用感官反馈来纠正行为错误。较大的错误根据定义需要更大的纠正，许多学习模型假设较大的感官反馈错误会导致较大的运动变化。然而，另一种观点认为，较大的错误会使学习效果变差，因为这些错误超出了通常经验范围内的错误，因此不太可能反映准确的反馈。这在声音控制中尤为关键，因为听觉反馈可能会受到环境噪声或感觉处理错误的影响。因此，成功的控制策略必须依赖于反馈来纠正错误，同时忽略可能导致适应不良的声音校正的异常听觉信号。我们假设这些限制导致对较小的强制错误进行的补偿最大，而对较大的错误进行的补偿最小。为了验证这一假设，我们操纵了鸣禽孟加拉雀唱歌时的听觉反馈音高。我们发现，由较大的感官错误驱动的学习，不仅比由较小的错误驱动的学习慢，而且对所施加的错误的补偿也不完整。此外，我们发现一个简单的原则可以解释这些数据：补偿的量与音高产生的基线分布与移位过程中经历的分布之间的重叠成正比。相应地，当音高超出歌曲的基线音高分布时，补偿的分数接近零。我们的数据表明，当感官错误在产生可变性范围内时，它们最能驱动学习，这表明学习受到感觉运动经验统计数据的限制。
