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  4. SPP 2134 - Teilprojekt: Information Integration in Predictive Processes: A Mechanistic Grounding of the Self
 
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Projekt Titel
SPP 2134 - Teilprojekt: Information Integration in Predictive Processes: A Mechanistic Grounding of the Self
Förderkennzeichen
AY 63/9-2
Funding code
945.03-957
Startdatum
June 1, 2022
Enddatum
May 31, 2025
Gepris ID
402780474
Übergeordnetes Projekt
SPP 2134: Das handelnde Selbst  
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Funder
Deutsche Forschungsgemeinschaft (DFG)  
Institut
Data Science Foundations E-21  
Projektleitung
Ay, Nihat
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.
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