Chapter 1 Reverse Engineering for Biologically-Inspired Cognitive Architectures: A Critical Analysis Andreas Schierwagen Abstract Research initiatives on both sides of the Atlantic try to utilize the opera- tional principles of organisms and brains to develop biologically inspired, artificial cognitive systems. This paper describes the standard way bio-inspiration is gained, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Using Robert Rosen’s modeling relation, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems are decomposable, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed. 1.1 Introduction Wer will was Lebendig’s erkennen und beschreiben, Sucht erst den Geist heraus zu treiben, Dann hat er die Teile in seiner Hand, Fehlt, leider! nur das geistige Band. J.W. GOETHE, Faust, Erster Teil a For some time past, computer science and engineering devote close attention to the functioning of the brain. It has been argued that recent advances in cognitive science and neuroscience have enabled a rich scientific understanding of how cog- nition works in the human brain. Thus, research programs have been initiated by leading research organizations on both sides of the Atlantic to develop new cogni- Institute for Computer Science, Intelligent Systems Department University of Leipzig Leipzig, Germany e-mail: schierwa@informatik.uni-leipzig.de http://www.informatik.uni-leipzig.de/˜schierwa 1 Published as: Schierwagen, A.: Reverse engineering for biologically inspired cognitive architectures: a critical analysis. In: Hernández, C.; Sanz, R.; Gómez-Ramirez, J.; Smith, L.S.; Hussain, A.; Chella, A.; Aleksander, I. (Eds.), From Brains to Systems. Brain-Inspired Cognitive Systems 2010. Advances in Experimental Medicine and Biology Volume 718, 2011, pp. 111-121