Thoroughman Kurt A, Taylor Jordan A
Department of Biomedical Engineering, Washington University, Saint Louis, Missouri 63130, USA.
J Neurosci. 2005 Sep 28;25(39):8948-53. doi: 10.1523/JNEUROSCI.1771-05.2005.
People routinely learn how to manipulate new tools or make new movements. This learning requires the transformation of sensed movement error into updates of predictive neural control. Here, we demonstrate that the richness of motor training determines not only what we learn but how we learn. Human subjects made reaching movements while holding a robotic arm whose perturbing forces changed directions at the same rate, twice as fast, or four times as fast as the direction of movement, therefore exposing subjects to environments of increasing complexity across movement space. Subjects learned all three environments and learned the low- and medium-complexity environments equally well. We found that subjects lessened their movement-by-movement adaptation and narrowed the spatial extent of generalization to match the environmental complexity. This result demonstrated that people can rapidly reshape the transformation of sense into motor prediction to best learn a new movement task. We then modeled this adaptation using a neural network and found that, to mimic human behavior, the modeled neuronal tuning of movement space needed to narrow and reduce gain with increased environmental complexity. Prominent theories of neural computation have hypothesized that neuronal tuning of space, which determines generalization, should remained fixed during learning so that a combination of neuronal outputs can underlie adaptation simply and flexibly. Here, we challenge those theories with evidence that the neuronal tuning of movement space changed within minutes of training.
人们经常学习如何操控新工具或做出新动作。这种学习需要将感知到的运动误差转化为预测性神经控制的更新。在此，我们证明运动训练的丰富程度不仅决定我们学习什么，还决定我们如何学习。人类受试者在握住一个机械臂时进行伸手动作，该机械臂的干扰力以与运动方向相同的速率、两倍速率或四倍速率改变方向，从而使受试者在整个运动空间中面对复杂度不断增加的环境。受试者学会了所有三种环境，并且对低复杂度和中等复杂度环境的学习效果同样良好。我们发现，受试者减少了逐次运动的适应性，并缩小了泛化的空间范围以匹配环境复杂度。这一结果表明，人们可以迅速重塑从感知到运动预测的转化过程，以便最好地学习一项新的运动任务。然后，我们使用神经网络对这种适应性进行建模，发现为了模拟人类行为，随着环境复杂度增加，建模的运动空间神经元调谐需要变窄并降低增益。神经计算的主流理论假设，决定泛化的空间神经元调谐在学习过程中应保持固定，以便神经元输出的组合能够简单灵活地构成适应性的基础。在此，我们用运动空间神经元调谐在训练数分钟内就发生变化的证据对这些理论提出了挑战。
