In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...) The author, S. Bent Russell, was an engineer deeply influenced by the neurological hypotheses on nervous conduction of Herbert Spencer, Max Meyer and Edward L. Thorndike. The second is the project, published in 1929, of an electromechanical machine in which the author, the psychologist J.M. Stephens, believed it was possible to embody Thorndike's law of effect. Thus both Bent Russell and Stephens referred to the principles of learning that Thorndike defined as connectionist . Their attempt was that of simulating by machines at least certain simple aspects of inhibition, association and habit formation that are typical of living organisms. I propose to situate their projects within the frame of thediscovery of a simulative (modelling) methodology which I believe might be considered an important topic of the Culture of the Artificial . Certain more recent steps toward such a methodology made by both connectionism of the 1950s and present-day connectionism are briefly pointed out in the paper. (shrink)

This article examines how experimental psychology experienced a revolution as cognitive science replaced behaviorism in the mid-20th century. This transition in the scientific account of human nature involved making normal what had once been normative: borrowing ideas of democratic thinking from political culture and conceptions of good thinking from philosophy of science to describe humans as active, creatively thinking beings, rather than as organisms that simply respond to environmental conditions. Reflexive social and intellectual practices were central to this process as (...) cognitive scientists used anti-positivist philosophy of science simultaneously to justify their own work as valid and also as a model of human thinking. In the process, the normative philosophy of science or ‘good academic thinking’ that cognitive scientists used to reshape the discipline of psychology and characterize themselves became, at the same time, the descriptive model of human nature. (shrink)

Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of neurons (...) is indeed one with precedents, although the implications of this for psychological issues have been interpreted variously — not all versions of connectionism are alike. (shrink)

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, and the upper (...) millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

Reading through Mechanica1 Intelligence, volume III of Alan Turing's Collected Works, one begins to appreciate just how propitious Turing's timing was. If Turing's major accomplishment in ‘On Computable Numbers’ was to expose the epistemological premises built into formalism, his main achievement in the 1940s was to recognize the extent to which this outlook both harmonized with and extended contemporary psychological thought. Turing sought to synthesize these diverse mathematical and psychological elements so as to forge a union between ‘embodied rules’ and (...) ‘learning programs’. Through their joint service in the Mechanist Thesis each would validate the other: and the frameworks from whence each derived. In this paper I will try to show how Turing's psychological thesis forces us to reassess the consequences of establishing AI on the epistemological foundation that underlies behaviourism. (shrink)