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. This involves, on the one hand, morphological computation within goal directed action and, on the other hand, integrated information within the controller, the agent's brain. In this article, we combine 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 test this framework in a simple experimental setup. There, we calculate the optimal policy for goal-directed behavior 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. In order to determine the influence of the brain on the behavior of the agent it is necessary to additionally measure the information flow to and from the brain.
翻译:集成信息理论为意识提供了定量的方法,可以应用于神经网络。一个由这种网络控制并受其环境影响的内装剂。一方面,在目标定向行动中进行形态学计算,另一方面,在控制器、代理人大脑内进行形态学计算,在控制器、代理人大脑内进行形态学计算。在本篇文章中,我们结合了不同的方法,以审查一个代理人身体、大脑和环境之间的信息流动。这使我们能够将各种信息流动联系起来。我们用简单的实验设置来测试这个框架。我们根据“作为推论的规划”的方法计算目标定向行为的最佳政策,在这种方法中,信息-地测量仪表成物学计算用于优化目标的概率。然后,根据最佳政策计算出各种方法的形态学计算和综合信息。在变化的形态学环境下,这些措施的动态突出了这两种概念之间的对立关系。在更简单的实验中,我们根据“推断”方法计算出一个目标定向行为的最佳政策。我们根据“规划作为推论”的方法计算出最佳政策,在其中,使用信息-地测量 e-algorithmmm 来优化目标的概率。为了确定该目标的概率,然后,然后根据大脑对大脑的动作和脑活动进行更多的测量到大脑的影响。为了测定,从大脑的动作到大脑对大脑的影响,需要进行更多的测量。