Takiyama Ken, Hirashima Masaya, Nozaki Daichi
Brain Science Institute, Tamagawa University, Machida-shi, Tokyo 194-8610, Japan.
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka University, Suita, Osaka 565-0871, Japan.
Nat Commun. 2015 Jan 30;6:5925. doi: 10.1038/ncomms6925.
Diverse features of motor learning have been reported by numerous studies, but no single theoretical framework concurrently accounts for these features. Here, we propose a model for motor learning to explain these features in a unified way by extending a motor primitive framework. The model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). To validate this idea, we perform a behavioural experiment to examine the model's novel prediction: after experiencing an environment in which the movement error is more easily predictable, subsequent motor learning should become faster. The experimental results support our prediction, suggesting that the prospective error might be encoded in the motor primitives. Furthermore, we demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models.
众多研究报告了运动学习的多种特征，但尚无单一的理论框架能同时解释这些特征。在此，我们提出一个运动学习模型，通过扩展运动基元框架以统一的方式解释这些特征。该模型假设运动基元的募集模式由即将进行的运动的预测运动误差（预期误差）决定。为验证这一观点，我们进行了一项行为实验来检验该模型的新预测：在经历了运动误差更容易预测的环境后，后续的运动学习应该会变得更快。实验结果支持了我们的预测，表明预期误差可能在运动基元中进行编码。此外，我们证明该模型具有强大的解释力，能够重现多种已由不同计算模型分别解释的与运动学习相关的现象。
