Department of Industrial Electronics, University of Minho, 4800-058 Guimarães, Portugal.
Hum Mov Sci. 2011 Oct;30(5):846-68. doi: 10.1016/j.humov.2010.08.012. Epub 2011 Jan 3.
In this paper we present a model for action preparation and decision making in cooperative tasks that is inspired by recent experimental findings about the neuro-cognitive mechanisms supporting joint action in humans. It implements the coordination of actions and goals among the partners as a dynamic process that integrates contextual cues, shared task knowledge and predicted outcome of others' motor behavior. The control architecture is formalized by a system of coupled dynamic neural fields representing a distributed network of local but connected neural populations. Different pools of neurons encode task-relevant information about action means, task goals and context in the form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic model of joint action is evaluated in a task in which a robot and a human jointly construct a toy object. We show that the highly context sensitive mapping from action observation onto appropriate complementary actions allows coping with dynamically changing joint action situations.
在本文中，我们提出了一个模型，用于协作任务中的动作准备和决策，该模型受到了最近关于支持人类联合动作的神经认知机制的实验发现的启发。它将合作伙伴之间的动作和目标协调作为一个动态过程来实现，该过程整合了上下文线索、共享任务知识和对他人运动行为的预测结果。控制架构通过一个耦合的动态神经场系统来形式化，该系统代表了一个局部但连接的神经元群体的分布式网络。不同的神经元池以自我维持的激活模式的形式对动作手段、任务目标和上下文的相关信息进行编码。这些模式由连接的群体的输入触发，并在递归相互作用的影响下随时间不断演变。联合动作的动态模型在一个机器人和一个人共同构建一个玩具物体的任务中进行了评估。我们表明，从动作观察到适当的互补动作的高度上下文敏感映射允许应对动态变化的联合动作情况。
