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Generalization in motor learning

机译:电机学习的概括

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Studies on plasticity in motor function have shown that motor learning generalizes, such that movements in novel situations are affected by previous training. It has been shown that visuomotor rotation learning generalizes more fully when training movements are made to a wide distribution of directions. Here we have found that for dynamics learning, the shape of the generalization gradient is not similarly modifiable by the extent of training within the workspace. Subjects learned to control a robotic device during training and we measured how subsequent movements in a reference direction were affected. Our results show that as the angular separation between training and test directions increased, the extent of generalization was reduced. When training involved multiple targets throughout the workspace, the extent of generalization was no greater than following training to the nearest target alone. Thus a wide range of experience compensating for a dynamics perturbation provided no greater benefit than localized training. Instead, generalization was complete when training involved targets that bounded the reference direction. This suggests that broad generalization of dynamics learning to movements in novel directions depends on interpolation between instances of localized learning..
机译:对电动机功能的可塑性的研究表明,电机学习概括,使得新颖情况的运动受到以前的培训的影响。已经表明,当对训练运动进行广泛分布的方向时,求解器旋转学习概括地概括。在这里,我们发现,对于动态学习,通过工作空间内的训练程度,概括梯度的形状并不类似地修改。学科学会在训练期间控制机器人设备,我们测量了当后在参考方向上的动作如何受到影响。我们的研究结果表明,随着训练与测试方向之间的角度分离,概括的程度降低。当培训涉及整个工作空间的多个目标时,概括的程度不大于独自对最近目标的培训。因此,对于动态扰动的多种经验提供了比局部训练更大的好处。相反,当培训涉及涉及参考方向的目标时,概括是完整的。这表明,在新颖方向上的动态学习动态广泛化取决于本地化学习实例之间的插值。

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