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

预测过程中的信息整合：自我的机械基础

• 批准号: 402780474
• 负责人: Professor Dr. Nihat Ay
• 金额: --
• 依托单位: Institut für Informatik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/402780474?language=en
• 关键词: Information; Integration; Predictive; Processes; Mechanistic

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.

该项目的总体目标是揭示与自我相关的内部表征出现的必要条件，当处理真正体现的代理。这将基于对亥姆霍兹机器的研究，该机器将预测和识别作为最优控制的先决条件。此外，我们的目的是研究在多大程度上这些过程中产生的高度集成的信息在意义上托诺尼的集成信息理论（IIT）的意识。这将提供有关现象自我的机制的见解。基于信息理论，这是定量的性质，我们希望确定定性不同种类的实施例之间的过渡，从而将我们的工作与梅青格的订单的实施例。在DFG SPP“主动自我”的这一时期，将开发一个具有越来越大粒度的控制器架构层次，最终导致神经元架构。来自亥姆霍兹机器理论的相应学习算法，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基础。基于具有不同粒度的控制器架构，该项目将与Verena V. Hafner的小组合作，分析机器人系统感觉运动回路中的相应信息流。其目的是验证增加的信息集成时，涉及学习，并结合了预测和控制的逆模型的前向模型。