McGill University.
Haskins Laboratories, New Haven, CT.
J Cogn Neurosci. 2018 Mar;30(3):290-306. doi: 10.1162/jocn_a_01204. Epub 2017 Nov 13.
One of the puzzles of learning to talk or play a musical instrument is how we learn which movement produces a particular sound: an audiomotor map. Existing research has used mappings that are already well learned such as controlling a cursor using a computer mouse. By contrast, the acquisition of novel sensorimotor maps was studied by having participants learn arm movements to auditory targets. These sounds did not come from different directions but, like speech, were only distinguished by their frequencies. It is shown that learning involves forming not one but two maps: a point map connecting sensory targets with motor commands and an error map linking sensory errors to motor corrections. Learning a point map is possible even when targets never repeat. Thus, although participants make errors, there is no opportunity to correct them because the target is different on every trial, and therefore learning cannot be driven by error correction. Furthermore, when the opportunity for error correction is provided, it is seen that acquiring error correction is itself a learning process that changes over time and results in an error map. In principle, the error map could be derived from the point map, but instead, these two maps are independently acquired and jointly enable sensorimotor control and learning. A computational model shows that this dual encoding is optimal and simulations based on this architecture predict that learning the two maps results in performance improvements comparable with those observed empirically.
学习说话或演奏乐器的一个难题是,我们如何学习哪个动作产生特定的声音:听觉运动映射。现有研究使用的映射已经很好地学习了,例如使用计算机鼠标控制光标。相比之下,通过让参与者学习手臂运动以达到听觉目标来研究新的感觉运动映射的获取。这些声音不是来自不同的方向,而是像语言一样,只通过它们的频率来区分。结果表明,学习涉及形成不是一个而是两个地图:一个将感觉目标与运动指令连接起来的点地图,以及一个将感觉误差与运动校正连接起来的误差地图。即使目标从不重复,也可以学习点地图。因此,尽管参与者会犯错,但由于每个试验的目标都不同,因此没有机会纠正错误,因此学习不能由错误校正驱动。此外,当提供错误校正的机会时,可以看出获取错误校正本身就是一个学习过程,它会随着时间的推移而变化,并导致出现误差地图。原则上,误差地图可以从点地图中得出,但实际上,这两个地图是独立获取的,共同实现感觉运动控制和学习。一个计算模型表明,这种双重编码是最优的,基于该架构的模拟预测,学习这两个地图会导致与经验观察到的性能提升相当的结果。