In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no best model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical (...) aid, I note that there is no best approach for studying cognition, since different approaches have all advantages and disadvantages, because they study different aspects of cognition from different contexts. This has implications for current debates on proper approaches for cognition: all approaches are a bit proper, but none will be proper enough. I draw remarks on the notion of cognition abstracting from all the approaches used to study it, and propose a simple classification for different types of cognition. (shrink)

Alan M. Turing’s last published work and some posthumously published manuscripts were dedicated to the development of his theory of organic pattern formation. In “The Chemical Basis of Morphogenesis” (1952), he provided an elaborated mathematical formulation of the theory of the origins of biological form that had been first proposed by Sir D’Arcy Wendworth Thompson in On Growth and Form (1917/1942). While arguably his most mathematically detailed and his systematically most ambitious effort, Turing’s morphogenetical writings also form the most thematically (...) self-contained and least philosophically explored part of his work. We dedicate our inquiry to the reasons and the implications of Turing’s choice of biological topic and viewpoint. We will probe for possible factors in Turing’s choice that go beyond availability and acquaintance with On Growth and Form. On these grounds, we will explore how and to what extent his theory of morphogenesis actually ties in with his concept of mechanistic computation. Notably, Thompson’s pioneering work in biological ‘structuralism’ was organicist in outlook and explicitly critical of the Darwinian approaches that were popular with Turing’s cyberneticist contemporaries—and partly used by Turing himself in his proto-connectionist models of learning. Resolving this apparent dichotomy, we demonstrate how Turing’s quest for mechanistic explanations of how organisation emerges in nature leaves room for a non-mechanist view of nature. (shrink)

Context: W. R. Ashby’s work on homeostasis as the basic mechanism underlying all kinds of physiological as well as cognitive functions has aroused renewed interest in cognitive science and related disciplines. Researchers have successfully incorporated some of Ashby’s technical results, such as ultrastability, into modern frameworks (e.g., CTRNN networks). Problem: The recovery of Ashby’s technical contributions has left in the background Ashby’s far more controversial non-technical views, according to which homeostatic adaptation to the environment governs all aspects of all forms (...) of life. This thesis entails that life is fundamentally “heteronomous” and it is conceptually at odds with the autopoiesis framework adopted by Ashby’s recent defenders as well as with the primacy of autonomy in human life that most of the Western philosophical tradition upholds. The paper argues that the use of computer simulations focused on the more conceptual aspects of Ashby’s thought may help us recover, extend and consequently assess an overall view of life as heteronomy. Method: The paper discusses some computer simulations of Ashby’s original electro-mechanical device (the homeostat) that implement his techniques (double-feedback loops and random parameter-switching). Results: First simulation results show that even though Ashby’s claims about homeostatic adaptivity need to be slightly weakened, his overall results are confirmed, thereby suggesting that an extension to virtual robots engaged in minimal cognitive tasks may be successful. Implications: The paper shows that a fuller incorporation of Ashby’s original results into recent cognitive science research may trigger a philosophical and technical reevaluation of the traditional distinction between heteronomous and autonomous behavior. Constructivist content: The research outlined in the paper supports an extended constructionist perspective in which agency as autonomy plays a more limited role. (shrink)

An important part of David Hume’s work is his attempt to put the natural sciences on a firmer foundation by introducing the scientific method into the study of human nature. This investigation resulted in a novel understanding of the mind, which in turn informed Hume’s critical evaluation of the scope and limits of the scientific method as such. However, while these latter reflections continue to influence today’s philosophy of science, his theory of mind is nowadays mainly of interest in terms (...) of philosophical scholarship. This paper aims to show that, even though Hume’s recognition in the cognitive sciences has so far been limited, there is an opportunity to reevaluate his work in the context of more recent scientific developments. In particular, it is argued that we can gain a better understanding of his overall philosophy by tracing the ongoing establishment of the enactive approach. In return, this novel interpretation of Hume’s ‘science of man’ is used as the basis for a consideration of the current and future status of the cognitive sciences. (shrink)

Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this thesis I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote (...) elements to find solutions by actively interacting among themselves. The main premise of the methodology claims that reducing the “friction” of interactions between elements of a system will result in a higher “satisfaction” of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while practical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for improving communication within self-organizing bureaucracies are advanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are discussed. Philosophical implications of the conceptual framework are also put forward. (shrink)

Context: The enactive paradigm in the cognitive sciences is establishing itself as a strong and comprehensive alternative to the computationalist mainstream. However, its own particular historical roots have so far been largely ignored in the historical analyses of the cognitive sciences. Problem: In order to properly assess the enactive paradigm’s theoretical foundations in terms of their validity, novelty and potential future directions of development, it is essential for us to know more about the history of ideas that has led to (...) the current state of affairs. Method: The meaning of the disappearance of the field of cybernetics and the rise of second-order cybernetics is analyzed by taking a closer look at the work of representative figures for each of the phases: Rosenblueth, Wiener and Bigelow for the early wave of cybernetics, Ashby for its culmination, and von Foerster for the development of the second-order approach. Results: It is argued that the disintegration of cybernetics eventually resulted in two distinct scientific traditions, one going from symbolic AI to modern cognitive science on the one hand, and the other leading from second-order cybernetics to the current enactive paradigm. Implications: We can now understand that the extent to which the cognitive sciences have neglected their cybernetic parent is precisely the extent to which cybernetics had already carried the tendencies that would later find fuller expression in second-order cybernetics. (shrink)