Information Integration in Predictive Processes: A Mechanistic Grounding of the Self

该项目的总体目标是在处理真正具体化的代理人时，揭示与自我相关的内部表征出现的必要条件。这将基于对Helmholtz机器的研究，这些机器将预测和识别作为最优控制的先决条件。此外，其目的是研究这些过程在多大程度上产生了托诺尼的意识综合信息论(IIT)意义上的高度综合信息。这将提供对隐藏在现象自我之下的机制的洞察。基于本质上是定量的信息论，我们期望识别定性不同类型的实施例之间的转变，从而将我们的工作与梅辛格的体现顺序联系起来。在DFG SPP“主动自我”的这一阶段，控制器体系结构的层次结构将随着粒度的增加而发展，最终导致神经元体系结构。来自Helmholtz机器理论的相应学习算法，即唤醒-睡眠算法的版本，表明与自由能原理密切相关，这将为该项目提供概念和形式上的基础。基于不同粒度的控制器架构，该项目将与Verena V.Hafner的团队合作，分析机器人系统传感器电机回路中的相应信息流。其目的是验证当涉及学习并结合用于预测的正向模型和用于控制的反向模型时，信息集成的增加。

The general aim of the project is to reveal necessary conditions for the emergence of internal representations associated with the self, when dealing with truly embodied agents. This will be based on the study of Helmholtz machines that implement prediction and recognition as prerequisite for optimal control. Furthermore, the aim is to study to what extent these processes generate high integrated information in the sense of Tononi's Integrated Information Theory (IIT) of consciousness. This will provide insights about the mechanisms that underly the phenomenal self. Based on information theory, which is quantitative in nature, we expect to identify transitions between qualitatively different kinds of embodiments, thereby relating our work to Metzinger's orders of embodiment. In this period of the DFG SPP "The Active Self”, a hierarchy of controller architectures with increasing granularity will be developed, ultimately leading to neuronal architectures. Corresponding learning algorithms from the theory of Helmholtz machines, versions of the wake-sleep algorithm, suggest a close connection to the Free Energy Principle, which will provide a conceptual and formal basis for the project. Based on controller architectures with various granularities, the project will analyse corresponding information flows in sensorimotor loops of robotic systems, in collaboration with Verena V. Hafner's group. The aim is to verify the increase of information integration when learning is involved and incorporates a forward model for prediction and an inverse model for control.