Departments of Integrative Physiology and Mechanical Engineering, University of Colorado, Boulder, Colorado, United States of America.
Biomedical Engineering Program, University of Colorado, Boulder, Colorado, United States of America.
PLoS One. 2023 Mar 16;18(3):e0282693. doi: 10.1371/journal.pone.0282693. eCollection 2023.
When learning new movements some people make larger kinematic errors than others, interpreted as a reduction in motor-learning ability. Consider a learning task where error-cancelling strategies incur higher effort costs, specifically where subjects reach to targets in a force field. Concluding that those with greater error have learned less has a critical assumption: everyone uses the same error-canceling strategy. Alternatively, it could be that those with greater error may be choosing to sacrifice error reduction in favor of a lower effort movement. Here, we test this hypothesis in a dataset that includes both younger and older adults, where older adults exhibited greater kinematic errors. Utilizing the framework of optimal control theory, we infer subjective costs (i.e., strategies) and internal model accuracy (i.e., proportion of the novel dynamics learned) by fitting a model to each population's trajectory data. Our results demonstrate trajectories are defined by a combination of the amount learned and strategic differences represented by relative cost weights. Based on the model fits, younger adults could have learned between 65-90% of the novel dynamics. Critically, older adults could have learned between 60-85%. Each model fit produces trajectories that match the experimentally observed data, where a lower proportion learned in the model is compensated for by increasing costs on kinematic errors relative to effort. This suggests older and younger adults could be learning to the same extent, but older adults have a higher relative cost on effort compared to younger adults. These results call into question the proposition that older adults learn less than younger adults and provide a potential explanation for the equivocal findings in the literature. Importantly, our findings suggest that the metrics commonly used to probe motor learning paint an incomplete picture, and that to accurately quantify the learning process the subjective costs of movements should be considered.
在学习新动作时,有些人比其他人犯更大的运动学错误,这被解释为运动学习能力的降低。考虑一个学习任务,其中错误消除策略会产生更高的努力成本,特别是在受试者在力场中到达目标的情况下。得出那些有更大错误的人学习得更少的结论有一个关键假设:每个人都使用相同的错误消除策略。或者,那些有更大错误的人可能会选择牺牲错误减少,以换取更低努力的运动。在这里,我们在一个包含年轻和老年成年人的数据集上测试了这个假设,其中老年成年人表现出更大的运动学错误。利用最优控制理论的框架,我们通过将模型拟合到每个群体的轨迹数据来推断主观成本(即策略)和内部模型准确性(即学习新动力学的比例)。我们的结果表明,轨迹是由学习量和策略差异的组合定义的,这些差异由相对成本权重表示。根据模型拟合,年轻成年人可以学习到新动力学的 65-90%。至关重要的是,老年成年人可以学习到 60-85%。每个模型拟合都会产生与实验观察到的数据相匹配的轨迹,其中模型中学习的比例较低会通过相对于努力增加运动学误差的成本来补偿。这表明年轻和老年成年人可以学习到相同的程度,但老年成年人的努力相对成本比年轻成年人高。这些结果对老年人比年轻人学习得少的说法提出了质疑,并为文献中的模糊发现提供了一个潜在的解释。重要的是,我们的研究结果表明,常用于探测运动学习的指标描绘了一个不完整的画面,为了准确量化学习过程,应该考虑运动的主观成本。