Biomedical Engineering Dept., Ben-Gurion Univ. of the Negev, Ben-Gurion 1, Beer-Sheva, Israel.
J Neurophysiol. 2012 Sep;108(6):1646-55. doi: 10.1152/jn.00224.2012. Epub 2012 Jun 13.
Daily interaction with the environment consists of moving with or without objects. Increasing interest in both types of movements drove the creation of computational models to describe reaching movements and, later, to describe a simplified version of object manipulation. The previously suggested models for object manipulation rely on the same optimization criteria as models for reaching movements, yet there is no single model accounting for both tasks that does not require reminimization of the criterion for each environment. We suggest a unified model for both cases: minimum acceleration with constraints for the center of mass (MACM). For point-to-point reaching movement, the model predicts the typical rectilinear path and bell-shaped speed profile as previous criteria. We have derived the predicted trajectories for the case of manipulating a mass-on-spring and show that the predicted trajectories match the observations of a few independent previous experimental studies of human arm movement during a mass-on-spring manipulation. Moreover, the previously reported "unusual" trajectories are also well accounted for by the proposed MACM. We have tested the predictions of the MACM model in 3 experiments with 12 subjects, where we demonstrated that the MACM model is equal or better (Wilcoxon sign-rank test, P < 0.001) in accounting for the data than three other previously proposed models in the conditions tested. Altogether, the MACM model is currently the only model accounting for reaching movements with or without external degrees of freedom. Moreover, it provides predictions about the intermittent nature of the neural control of movements and about the dominant control variable.
日常与环境的互动包括有或没有物体的移动。对这两种运动的日益关注推动了计算模型的创建，用于描述伸手运动，后来又用于描述物体操作的简化版本。之前提出的物体操作模型依赖于与伸手运动模型相同的优化标准，但没有一个单一的模型可以同时解释这两个任务，而不需要为每个环境重新最小化标准。我们建议了一个统一的模型来处理这两种情况：质量中心最小加速度（MACM）。对于点对点伸手运动，该模型预测了典型的直线路径和钟形速度曲线，这与之前的标准一致。我们已经推导出了在操纵质量弹簧的情况下的预测轨迹，并表明预测轨迹与人类手臂在质量弹簧操纵期间的几个独立先前实验研究的观察结果相匹配。此外，所提出的 MACM 还很好地解释了之前报道的“异常”轨迹。我们在 3 项涉及 12 名受试者的实验中测试了 MACM 模型的预测，结果表明，在测试条件下，MACM 模型在解释数据方面与其他三个先前提出的模型相等或更好（Wilcoxon 符号秩检验，P < 0.001）。总的来说，MACM 模型是目前唯一能够解释有无外部自由度的伸手运动的模型。此外，它还提供了关于运动神经控制的间歇性和主导控制变量的预测。
