The Integrated Information Theory provides a quantitative approach to consciousness and can be applied to neural networks. An embodied agent controlled by such a network influences and is being influenced by its environment. We present a technique combining different methods in order to examine the information flows among and within the body, the brain and the environment of an agent. This allows us to relate various information flows to each other. We demonstrate the implications of this framework within a simple experimental setup. There, the optimal policy for goal- directed behavior is determined based on the “planning as inference” method, in which the information-geometric em- algorithm is used to optimize the likelihood of the goal. Morphological computation and integrated information are then calculated with respect to the optimal policies. Comparing the dynamics of these measures under changing morphological circumstances highlights the antagonistic relationship between these two concepts. The more morphological computation is involved, the less information integration within the brain is required. Furthermore, we argue that it is necessary to additionally measure the information flow to and from the brain in order to determine the influence of the brain on the behavior of the agent.



Keywords: #Consciousness #Mathematical_Models_of_Consciousness #Langer #Neural_Networks#Information #Morphological_computation #Optimization



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