A theory of syllogistic reasoning is proposed, derived from the medieval doctrine of 'distribution of terms'. This doctrine may or may not furnish an adequate ground for the logic of the syllogism but does appear to illuminate the psychological processes involved. Syllogistic thinking is shown to have its origins in the approach and avoidance behaviour of pre-verbal organisms and, in verbal (human) organisms, to bridge the gap between the intuitive grasp shown by most of us of the validity of simple (...) logical arguments and the failure of intuition in more complex arguments that require resort to calculation. Some difficulties are considered affecting the use of syllogisms as experimental material. These include failure on the part of the investigator to take account of the fact that a syllogism is always part of a continuing argument in which the topic of the argument is known to all parties and the possibility that subjects may find ways of appearing to solve syllogisms without actually doing so. (shrink)

Parallel distributed processing is transforming the field of cognitive science. Microcognition provides a clear, readable guide to this emerging paradigm from a cognitive philosopher's point of view. It explains and explores the biological basis of PDP, its psychological importance, and its philosophical relevance.

A young scientist and mathematician explores the mystery and complexity of human thought processes from an interdisciplinary point of view.

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all inductive rules are (...) formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems. (shrink)

The author is primarily concerned with the explanation of behavior in regard to (1) the mecanical model, (2) the effects of physical-organic processes on behavior, (3) the lack of understanding between philosophers and psychologists as to sufficient conditions for predicting a behavioral event, (4) conditions leading to expalantions of behavior that could predict behavior exclusive of any antecedent psychological behavior, and (5) variations of the mechanical-model introducing differing sorts of explanation. (staff).

In a fluent, clear, and lively style this translation by two of Maslov's junior colleagues brings the work of the late Soviet scientist S. Yu. Maslov to a wider audience. Maslov was considered by his peers to be a man of genius who was making fundamental contributions in the fields of automatic theorem proving and computational logic. He published little, and those few papers were regarded as notoriously difficult. This book, however, was written for a broad audience of readers and (...) describes elegant examples of applications in such fields as computer science, artificial intelligence, operations research, economic modeling, and biological modeling, among others. The book also brings to light the work by the American mathematician E. L. Post, which inspired Maslov's own work in the development of a general theory and which has been long neglected by mathematicial logicians and systems theorists in the United States. The book's first chapter introduces the Rules of the Game. Part I, Mathematics of Calculi, covers E. L. Post's canonical systems, deductive systems and algorithms, and probabilistic calculi and deductive information. Part II, Horizonal Modeling, takes up a "toy" economy, the calculi of technological possibilities, and the development of rules. Part III, Vertical Modeling, deals with the topics of "to fight and to search" and the consequences of the asymmetry of cognitive mechanisms. Vladimir Lifschitz is affiliated with the Department of Computer Science at Stanford University, and Michael Gelfond with the Department of Electrical Engineering and Computer Science at the University of Texas, El Paso. Theory of Deductive Systems and Its Applicationsis included in the Foundation of Computing Series, edited by Michael Garey. (shrink)

The following values have no corresponding Zotero field: PB - J. Wiley Chichester,, UK.

Writings by a thinker—a psychiatrist, a philosopher, a cybernetician, and a poet—whose ideas about mind and brain were far ahead of his time. Warren S. McCulloch was an original thinker, in many respects far ahead of his time. McCulloch, who was a psychiatrist, a philosopher, a teacher, a mathematician, and a poet, termed his work “experimental epistemology.” He said, “There is one answer, only one, toward which I've groped for thirty years: to find out how brains work.” Embodiments of Mind, (...) first published more than fifty years ago, teems with intriguing concepts about the mind/brain that are highly relevant to recent developments in neuroscience and neural networks. It includes two classic papers coauthored with Walter Pitts, one of which applies Boolean algebra to neurons considered as gates, and the other of which shows the kind of nervous circuitry that could be used in perceiving universals. These first models are part of the basis of artificial intelligence. Chapters range from “What Is a Number, that a Man May Know It, and a Man, that He May Know a Number,” and “Why the Mind Is in the Head,” to “What the Frog's Eye Tells the Frog's Brain” (with Jerome Lettvin, Humberto Maturana, and Walter Pitts), “Machines that Think and Want,” and “A Logical Calculus of the Ideas Immanent in Nervous Activity” (with Walter Pitts). Embodiments of Mind concludes with a selection of McCulloch's poems and sonnets. This reissued edition offers a new foreword and a biographical essay by McCulloch's one-time research assistant, the neuroscientist and computer scientist Michael Arbib. (shrink)