Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity
Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity
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DOI：
10.1016/j.neuroimage.2017.04.060
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发表时间：
2017-08-01
期刊：
影响因子：
5.7
通讯作者：
Cross, Emily S.
中科院分区：
文献类型：
--
作者：
Gardner, Tom;Aglinskas, Aidas;Cross, Emily S.
观察其他人的动作会激发大脑中一系列被统称为动作观察网络（AON）的感觉运动区域的参与。大量文献记录了与不熟悉的动作相比，在观察或执行熟悉的动作时更稳健的AON响应，以及AON响应的幅度与观察者对所观察或执行的动作的熟悉度之间的正相关。另一方面，新出现的证据表明，AON活动的模式与这些研究结果相反，在某些情况下，不熟悉的行动导致AON参与比熟悉的行动。在试图调和这些相互矛盾的研究结果，一些人提出，AON反应幅度和动作熟悉性之间的关系是非线性的性质。在本研究中，我们使用了一个精心设计的吉他训练干预，以探讨动作熟悉性和AON参与在行动执行和行动观察任务之间的关系。参与者接受fMRI扫描，同时使用扫描仪兼容的低音吉他执行一组吉他序列，并观察第二组序列。然后，参与者在3天内在扫描仪外获得了这些刺激的一半的进一步物理实践或观察经验。然后，参与者返回相同的扫描会话，其中他们执行并观察相同数量的熟悉（训练）和不熟悉（未经训练）的吉他序列。通过感兴趣区域分析，我们提取了在两次扫描期间参与的AON区域内的活动，然后将线性、二次和三次回归模型拟合到这些数据。这些数据最好地支持三次回归模型，表明与AON相关的关键感觉运动脑区内的反应曲线以非线性方式对动作熟悉性作出反应。此外，通过探测的预测误差信号的主观性质，我们显示的结果一致的预测编码帐户的AON参与在行动观察和执行，也考虑到神经效率的变化的影响。
Watching other people move elicits engagement of a collection of sensorimotor brain regions collectively termed the Action Observation Network (AON). An extensive literature documents more robust AON responses when observing or executing familiar compared to unfamiliar actions, as well as a positive correlation between amplitude of AON response and an observer's familiarity with an observed or executed movement. On the other hand, emerging evidence shows patterns of AON activity counter to these findings, whereby in some circumstances, unfamiliar actions lead to greater AON engagement than familiar actions. In an attempt to reconcile these conflicting findings, some have proposed that the relationship between AON response amplitude and action familiarity is nonlinear in nature. In the present study, we used an elaborate guitar training intervention to probe the relationship between movement familiarity and AON engagement during action execution and action observation tasks. Participants underwent fMRI scanning while executing one set of guitar sequences with a scanner-compatible bass guitar and observing a second set of sequences. Participants then acquired further physical practice or observational experience with half of these stimuli outside the scanner across 3 days. Participants then returned for an identical scanning session, wherein they executed and observed equal numbers of familiar (trained) and unfamiliar (untrained) guitar sequences. Via region of interest analyses, we extracted activity within AON regions engaged during both scanning sessions, and then fit linear, quadratic and cubic regression models to these data. The data best support the cubic regression models, suggesting that the response profile within key sensorimotor brain regions associated with the AON respond to action familiarity in a nonlinear manner. Moreover, by probing the subjective nature of the prediction error signal, we show results consistent with a predictive coding account of AON engagement during action observation and execution that also takes into account effects of changes in neural efficiency.