Erlhagen Wolfram, Mukovskiy Albert, Bicho Estela
Departament of Mathematics for Science and Technology, University of Minho, 4800-058 Guimarães, Portugal.
Brain Res. 2006 Apr 14;1083(1):174-88. doi: 10.1016/j.brainres.2006.01.114.
The understanding of other individuals' actions is a fundamental cognitive skill for all species living in social groups. Recent neurophysiological evidence suggests that an observer may achieve the understanding by mapping visual information onto his own motor repertoire to reproduce the action effect. However, due to differences in embodiment, environmental constraints or motor skills, this mapping very often cannot be direct. In this paper, we present a dynamic network model which represents in its layers the functionality of neurons in different interconnected brain areas known to be involved in action observation/execution tasks. The model aims at substantiating the idea that action understanding is a continuous process which combines sensory evidence, prior task knowledge and a goal-directed matching of action observation and action execution. The model is tested in variations of an imitation task in which an observer with dissimilar embodiment tries to reproduce the perceived or inferred end-state of a grasping-placing sequence. We also propose and test a biologically plausible learning scheme which allows establishing during practice a goal-directed organization of the distributed network. The modeling results are discussed with respect to recent experimental findings in action observation/execution studies.
理解其他个体的行为是所有生活在社会群体中的物种的一项基本认知技能。最近的神经生理学证据表明，观察者可以通过将视觉信息映射到自己的运动技能库来重现动作效果，从而实现理解。然而，由于身体构造、环境限制或运动技能的差异，这种映射往往不是直接的。在本文中，我们提出了一个动态网络模型，该模型在其各层中代表了已知参与动作观察/执行任务的不同相互连接脑区中神经元的功能。该模型旨在证实这样一种观点，即动作理解是一个连续的过程，它结合了感官证据、先前的任务知识以及动作观察与动作执行的目标导向匹配。该模型在一个模仿任务的变体中进行了测试，在这个任务中，具有不同身体构造的观察者试图重现抓握 - 放置序列的感知或推断出的最终状态。我们还提出并测试了一种生物学上合理的学习方案，该方案允许在实践过程中建立分布式网络的目标导向组织。结合动作观察/执行研究中的最新实验结果对建模结果进行了讨论。
