Fluid intelligence and working memory support dissociable aspects of learning by physical but not observational practice.
Fluid intelligence and working memory support dissociable aspects of learning by physical but not observational practice.
复制标题
流体智力和工作记忆通过身体练习而非观察练习来支持学习的分离方面。
DOI：
10.1016/j.cognition.2019.04.015
复制
发表时间：
2019
期刊：
影响因子：
3.4
通讯作者：
Apšvalka D
中科院分区：
文献类型：
--
作者：
Apšvalka D
人类有一种通过观察他人来学习的非凡能力，无论是学习打一个精致的结还是弹钢琴。然而，将视觉输入转化为运动技能执行的机制仍不清楚。有人提出，共同的认知和神经机制支持学习运动技能的物理和观察实践。在这里，我们提供了一个新的测试的共同机制假设，通过测试的程度，某些个体差异预测观察以及物理学习。参与者（每组N = 92）在测试阶段进行训练和未经训练的序列之前，要么身体练习五元素按键序列，要么观看类似序列的视频。我们还测量了参与者的认知能力，这些能力以前与学习速度有关，包括工作记忆和流体智力。我们的研究结果表明，工作记忆和流体智力的个体差异预测运动学习的分离方面的改善后，物理实践，但不是观察实践。工作记忆预测从测试前到测试后的一般学习收益，这些收益概括为未训练的序列，而流体智力预测与训练序列相关的序列特定收益。然而，工作记忆和流体智力都不能预测观察学习后的训练收益。因此，这些结果表明物理和观察学习的共享机制假设的局限性。事实上，观察学习的模型需要更新，以反映这种学习在多大程度上是基于与物理学习相比的共享和不同的过程。我们认为，这种差异可能反映了与观察实践相比，物理学习更具故意性，后者在更大程度上依赖于高阶认知资源，如工作记忆和流体智力。
Humans have a remarkable ability to learn by watching others, whether learning to tie an elaborate knot or play the piano. However, the mechanisms that translate visual input into motor skill execution remain unclear. It has been proposed that common cognitive and neural mechanisms underpin learning motor skills by physical and observational practice. Here we provide a novel test of the common mechanism hypothesis by testing the extent to which certain individual differences predict observational as well as physical learning. Participants (N = 92 per group) either physically practiced a five-element key-press sequence or watched videos of similar sequences before physically performing trained and untrained sequences in a test phase. We also measured cognitive abilities across participants that have previously been associated with rates of learning, including working memory and fluid intelligence. Our findings show that individual differences in working memory and fluid intelligence predict improvements in dissociable aspects of motor learning following physical practice, but not observational practice. Working memory predicts general learning gains from pre- to post-test that generalise to untrained sequences, whereas fluid intelligence predicts sequence-specific gains that are tied to trained sequences. However, neither working memory nor fluid intelligence predict training gains following observational learning. Therefore, these results suggest limits to the shared mechanism hypothesis of physical and observational learning. Indeed, models of observational learning need updating to reflect the extent to which such learning is based on shared as well as distinct processes compared to physical learning. We suggest that such differences could reflect the more intentional nature of learning during physical compared to observational practice, which relies to a greater extent on higher-order cognitive resources such as working memory and fluid intelligence.