This article examines the intellectual and institutional factors that contributed to the col- laboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Historians and scientists alike often refer to the McCulloch–Pitts paper as a landmark event in the history of cybernetics, and fundamental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch–Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Al- though they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch– Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena.

In this article we analyze the methodological commitments of a radical embodied cognition (REC) approach to social interaction and social cognition, specifically with respect to the explanatory framework it adopts. According to many representatives of REC, such as enactivists and the proponents of dynamical and ecological psychology, sociality is to be explained by (1) focusing on the social unit rather than the individuals that comprise it and (2) establishing the regularities that hold on this level rather than modeling the sub-personal mechanisms that could be said to underlie social phenomena. We point out that, despite explicit commitment, such a view implies an implicit rejection of the mechanistic explanation framework widely adopted in traditional cognitive science (TCS), which, in our view, hinders comparability between REC and these approaches. We further argue that such a position is unnecessary and that enactive mechanistic explanation of sociality is both possible and desirable. We examine three distinct objections from REC against mechanistic explanation, which we dub the decomposability, causality and extended cognition worries. In each case we show that these complaints can be alleviated by either appreciation of the full scope of the mechanistic account or adjustments on both mechanistic and REC sides of the debate.

Key words: enactivism, mechanistic explanation, social cognition

In this paper we examine an enactive approach to social cog- nition, a species of radical embodied cognition typically pro- posed as an alternative to traditional cognitive science. Ac- cording to enactivists, social cognition is best explained by reference to the social unit rather than the individuals that par- ticipate in it. We identify a methodological problem in this approach, namely a lack of clarity with respect to the model of explanation it adopts. We review two complaints about a mechanistic explanatory framework, popular in traditional cognitive science, that prevent enactivists from embracing it. We argue that these complaints are unfounded and propose a conceptual model of enactive mechanistic explanation of so- cial cognition.

Key words: enactivism, social cognition, mechanistic expla- nation

Recent work in cognitive science has suggested that there are actual cases in which cognitive processes extend in the physical world beyond the bounds of the brain and the body. We argue that, while transcranial cognition may be both a logical and a nomological possibility, no case has been made for its current existence. In other words, we defend a form of contingent intracranialism about the cognitive.

For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields. Relevance: Artificial life has contributed to philosophy of biology and of cognitive science, thus making it an important field related to constructivism.

While structured as an autobiography, this memoir exemplifies ways in which classic contributions to cybernetics (e.g., by Wiener, McCulloch & Pitts, and von Neumann) have fed into a diversity of current research areas, including the mathematical theory of systems and computation, artificial intelligence and robotics, computational neuroscience, linguistics, and cognitive science. The challenges of brain theory receive special emphasis. Action-oriented perception and schema theory complement neural network modeling in analyzing cerebral cortex, cerebellum, hippocampus, and basal ganglia. Comparative studies of frog, rat, monkey, ape and human not only deepen insights into the human brain but also ground an EvoDevoSocio view of “how the brain got language.” The rapprochement between neuroscience and architecture provides a recent challenge. The essay also assesses some of the social and theological implications of this broad perspective.

Key words: action-oriented perception, ape, architecture, artificial intelligence, automata theory, basal ganglia, brain theory, cerebellum, cerebral cortex, cognitive science, computational neuroscience, cybernetics, frog, hippocampus, human, language evolution, li

Excerpt: As I read the cybernetic literature, I became intrigued that as an approach to the mind which was often described as a predecessor to AI, cybernetics had a much more sophisticated approach to mind than its purported successor. I was soon led to Prof. Herbert Brün’s seminar in experimental composition, and to the archives of the Biological Computer Laboratory (BCL) in the basement of the University of Illinois library. Since then, I have been trying to come to terms with what it was that was so special about the BCL, what allowed it to produce such interesting ideas and projects which seem alien and exotic in comparison to what mainstream AI and Cognitive Science produced in the same era. And yet, despite its appealing philosophical depth and technological novelty, it seems to have been largely ignored or forgotten by mainstream research in these areas. I believe that these are the same concerns that many of the authors of the recent issue of Cybernetics and Human Knowing (Brier & Glanville, 2003) express in regard to the legacy of von Foerster and the BCL. How could such an interesting place, full of interesting things and ideas have just disappeared and been largely forgotten, even in its own home town?

Excerpt: The purpose of this essay is to clarify some of the important senses in which the relationship between the brain and the computer might be considered as one of “modeling.” It also considers the meaning of “simulation” in the relationships between models, computers and brains. While there has been a fairly broad literature emerging on models and simulations in science, these have primarily focused on the physical sciences, rather than the mind and brain. And while the cognitive sciences have often invoked concepts of modeling and simulation, they have been frustratingly inconsistent in their use of these terms, and the implicit relations to their scientific roles. My approach is to consider the early convolution of brain models and computational models in cybernetics, with the aim of clarifying their significance for more current debates in the cognitive sciences. It is my belief that clarifying the historical senses in which the brain and computer serve as models of each other in the historical period prior to the birth of AI and cognitive science is a crucial task for an archeology of AI and the history of cognitive science.

This dissertation reconsiders the nature of scientific models through an historical study of the development of electronic models of the brain by Cybernetics researchers in the 1940s. By examining how these unique models were used in the brain sciences, it develops the concept of a “working model” for the brain sciences. Working models differ from theoretical models in that they are subject to manipulation and interactive experimentation, i.e., they are themselves objects of study and part of material culture. While these electronic brains are often disparaged by historians as toys and publicity stunts, I argue that they mediated between physiological theories of neurons and psychological theories of behavior so as to leverage their compelling material performances against the lack of observational data and sparse theoretical connections between neurology and psychology. I further argue that working models might be used by cognitive science to better understand how the brain develops performative representations of the world.

Open peer commentary on the article “Enaction as a Lived Experience: Towards a Radical Neurophenomenology” by Claire Petitmengin. Upshot: Why has the neurophenomenological approach not been adopted as a common and even obligatory tool in the study of consciousness? I suggest that the problem with the neurophenomenological approach is its effectiveness on the one hand and its almost impossible demands from the scientist on the other: One cannot accept the neurophenomenological approach without rejecting not only the paradigm of cognitive science, but the scientific paradigm as a whole.