This paper aims to investigate how adequate cognitive functions for recognizing, predicting, and generating a variety of actions can be developed through iterative learning of action-caused dynamic perceptual patterns. Particularly, we examined the capabilities of mental simulation of one’s own actions as well as the inference of others’ intention because they play a crucial role, especially in social cognition. We propose a dynamic neural network model based on predictive coding which can generate and recognize dynamic visuo-proprioceptive patterns. The proposed model was examined by conducting a set of robotic simulation experiments in which a robot was trained to imitate visually perceived gesture patterns of human subjects in a simulation environment. The experimental results showed that the proposed model was able to develop a predictive model of imitative interaction through iterative learning of large-scale spatio-temporal patterns in visuo-proprioceptive input streams. Also, the experiment verified that the model was able to generate mental imagery of dynamic visuo-proprioceptive patterns without feeding the external inputs. Furthermore, the model was able to recognize the intention of others by minimizing prediction error in the observations of the others’ action patterns in an online manner. These findings suggest that the error minimization principle in predictive coding could provide a primal account for the mirror neuron functions for generating actions as well as recognizing those generated by others in a social cognitive context.

中文翻译：

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使用预测编码框架处理机器人与人之间模仿交互的大规模时空模式

本文旨在研究如何通过对动作引起的动态感知模式的迭代学习来开发用于识别、预测和生成各种动作的足够的认知功能。特别是，我们检查了对自己行为的心理模拟能力以及对他人意图的推断，因为它们起着至关重要的作用，尤其是在社会认知中。我们提出了一种基于预测编码的动态神经网络模型，该模型可以生成和识别动态视觉本体模式。通过进行一组机器人模拟实验来检查所提出的模型，其中训练机器人在模拟环境中模仿人类受试者的视觉感知手势模式。实验结果表明，所提出的模型能够通过迭代学习视觉本体输入流中的大规模时空模式来开发模仿交互的预测模型。此外，实验验证了该模型能够在不提供外部输入的情况下生成动态视觉本体模式的心理意象。此外，该模型能够通过在线方式最大限度地减少观察他人行为模式时的预测误差来识别他人的意图。这些发现表明，预测编码中的错误最小化原则可以为镜像神经元功能提供一个原始解释，用于生成动作以及识别其他人在社会认知环境中生成的动作。