预测编码和主动推理框架下的会计社会认知机制:基于机器人交互平台的综合实验研究

J. Tani
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引用次数: 0

摘要

本小组通过使用预测编码和主动推理框架[1]探索了社会认知可能的神经心理学机制。为了获得更好的理解,我们采取了所谓的合成机器人方法,其中对机器人-人以及机器人-机器人交互进行了一系列实验。特别是,我们研究了代理之间自发耦合和解耦的潜在机制,以及从一个社会环境到另一个社会环境的自主转移。我们还研究了机器人和人类导师如何通过他们的发展互动辅导过程共同开发新颖或创造性的行为。最后,我从我们对人类如何能够感受到机器人的意图或自由意志,或者机器人如何能够在人-机器人循环实验中为人类做到这一点的初步研究中,解决社会认知中的现象学方面的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accounting Social Cognitive Mechanisms by the Framework of Predictive Coding and Active Inference: A Synthetic Experimental Study using Robotics Interaction Platforms
Our group has explored possible neuropsychological mechanisms for social cognition by using predictive coding and active inference frameworks [1]. For the purpose of gaining better understanding, we have taken so-called the synthetic robotics approach wherein a set of experiments have been conducted for robot-human as well as robot-robot interactions. Especially, we examine the underlying mechanisms accounting for spontaneous coupling and decoupling among agents as well as autonomous shifts from one social context to another. We investigate also how can novel or creative behaviors be co-developed by robots and human tutors through their developmental interactive tutoring processes. Finally, I address phenomenological aspects in social cognition from our preliminary examinations on how human can feel intention or free will of the robots or how the robots can possibly do so for the humans in the human-in-the-robot-loop experiment.
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