Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one‐to‐one structure‐function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience‐based arguments against multiple realization go wrong. *Received October 2009; revised December 2009. †To contact the author, (...) please write to: Department of Philosophy, 260 English‐Philosophy Building, University of Iowa, Iowa City, IA 52242; e‐mail: carrie‐[email protected]. (shrink)

This article addresses the following problems: What is a mechanism, how can it be discovered, and what is the role of the knowledge of mechanisms in scientific explanation and technological control? The proposed answers are these. A mechanism is one of the processes in a concrete system that makes it what it is — for example, metabolism in cells, interneuronal connections in brains, work in factories and offices, research in laboratories, and litigation in courts of law. Because mechanisms are largely (...) or totally imperceptible, they must be conjectured. Once hypothesized they help explain, because a deep scientific explanation is an answer to a question of the form, "How does it work, that is, what makes it tick—what are its mechanisms?" Thus, by contrast with the subsumption of particulars under a generalization, an explanation proper consists in unveiling some lawful mechanism, as when political stability is explained by either coercion, public opinion manipulation, or democratic participation. Finding mechanisms satisfies not only the yearning for understanding, but also the need for control. Key Words: explanation • function • mechanism • process • system • systemism. (shrink)

En este texto se elabora un análisis historiográfico, filosófico y teórico en torno a diversas propuestas de caracterización, demarcación, y explicación de la conducta y la cognición. Asimismo, se construyen argumentos para favorecer una comprensión más integral y elaborada de los programas de investigación acerca de la conducta y la cognición. Con este fin, en la primera sección analizaremos y cuestionaremos tres mitos acerca de la historia de la psicología: la tesis de los paradigmas hegemónicos y los reemplazos revolucionarios, la (...) tesis de los paradigmas incomparables, y la tesis que exige un único paradigma dominante como requisito de cientificidad. Dado que estos mitos se derivan de ciertas ideas problemáticas de Kuhn, en la segunda sección ofreceremos una propuesta alternativa basada en la filosofía de la ciencia de Bunge. En las secciones tercera y cuarta exploraremos los significados de las palabras “conducta” y “cognición”, y su relevancia para corregir ciertos malentendidos que obstaculizan los debates entre los programas de investigación conductistas y cognitivos. En las secciones quinta y la sexta, discutiremos algunos mitos acerca del conductismo y del cognitivismo, y en la última sección ofreceremos algunas conclusiones sobre los temas abordados. Las herramientas que analizaremos permiten establecer un debate más racional entre programas de investigación científica, generar puentes de discusión y desarrollo teórico de una manera más integral y completa, y evitar caer tanto en el dogmatismo y la descalificación injustificada, como en las actitudes eclécticas. (shrink)

In science as in mathematics, it is popular to know little and resent much about category theory. Less well known is how common it is to know little and like much about set theory. The set theory of almost all scientists, and even the average mathematician, is fundamentally different from the formal set theory that is contrasted against category theory. The latter two are often opposed by saying one emphasizes Substance, the other Form. However, in all known systems of mathematics (...) throughout history, mathematicians have moved fluidly between ideas conceived of as thing-like, property-like, and process-like. On the other hand one way to advance science is to better distinguish between thing, property, and process. All this constitutes a distracting background for those interested in, or distressed by, the possible application of category theory to science, and to mathematics as well. (shrink)

In science as in mathematics, it is popular to know little and resent much about category theory. Less well known is how common it is to know little and like much about set theory. The set theory of almost all scientists, and even the average mathematician, is fundamentally different from the formal set theory that is contrasted against category theory. The latter two are often opposed by saying one emphasizes Substance, the other Form. However, in all known systems of mathematics (...) throughout history, mathematicians have moved fluidly between ideas conceived of as thing-like, property-like, and process-like. On the other hand one way to advance science is to better distinguish between thing, property, and process. All this constitutes a distracting background for those interested in, or distressed by, the possible application of category theory to science, and to mathematics as well. (shrink)

In their Minds and Machines essay How would you know if you synthesized a Thinking Thing? (Kary & Mahner, Minds and Machines, 12(1), 61–86, 2002), Kary and Mahner have chosen to occupy a high ground of materialism and empiricism from which to attack the philosophical and methodological positions of believers in artificial intelligence (AI) and artificial life (AL). In this review I discuss some of their main arguments as well as their philosophical foundations. Their central argument: ‘AI is Platonism’, which (...) is based on a particular interpretation of the notion of ‘definition’ and used as a critique against AI, can be counter criticized from two directions: first, Anti-Platonism is not a necessary precondition for criticizing AI, because outspoken Platonist criticism against AI is already known (Penrose, The emperor’s new mind (with a foreword by M. Gardner), 1989). Second, even in case that AI would essentially be ‘Platonism’ this would not be a sufficient argument for proving AI wrong. In my counter criticism I assume a more or less Popperian position by emphasizing the openness of the future: Not by quasi-Scholastic arguments (like Kary and Mahner’s), but only after being confronted with a novel ‘thinking thing’ by future AI engineers we can start to analyze its particular properties (Let me use a history analogon to illustrate my position: In the 19th century, mechanized aviation was widely regarded impossible—only natural organisms (such as birds or bees) could fly, and any science of aerodynamics or aviation did not exist. Only after some non-scientific technicians had confronted their astonished fellows with the first (obviously) flying machine the science of ‘Artificial Aviation’ came into existence, motivated by the need for understanding and mastering that challenging and puzzling new phenomenon). (shrink)