Human beings have the unique ability to view the world in a detached way: We can think about the world in terms that transcend our own experience or interest, and consider the world from a vantage point that is, in Nagel's words, "nowhere in particular". At the same time, each of us is a particular person in a particular place, each with his own "personal" view of the world, a view that we can recognize as just one aspect of the (...) whole. How do we reconcile these two standpoints--intellectually, morally, and practically? To what extent are they irreconcilable and to what extent can they be integrated? Thomas Nagel's ambitious and lively book tackles this fundamental issue, arguing that our divided nature is the root of a whole range of philosophical problems, touching, as it does, every aspect of human life. He deals with its manifestations in such fields of philosophy as: the mind-body problem, personal identity, knowledge and skepticism, thought and reality, free will, ethics, the relation between moral and other values, the meaning of life, and death. Excessive objectification has been a malady of recent analytic philosophy, claims Nagel, it has led to implausible forms of reductionism in the philosophy of mind and elsewhere. The solution is not to inhibit the objectifying impulse, but to insist that it learn to live alongside the internal perspectives that cannot be either discarded or objectified. Reconciliation between the two standpoints, in the end, is not always possible. (shrink)

Elements of Episodic Memory is a classic text in the psychology literature. It had a significant influence on research in the area has been much sought after in recent years. Finally, it has now been made available again with this reissue, the text unchanged from the original.

Intentional explanation and attributions of mentality -- International systems -- Reply to Arbib and Gunderson -- Brain writing and mind reading -- The nature of theory in psychology -- Skinner skinned -- Why the law of effect will not go away -- A cure for the common code? -- Artificial intelligence as philosophy and as psychology -- Objects of consciousness and the nature of experience -- Are dreams experiences? -- Toward a cognitive theory of consciousness -- Two approaches to mental (...) images -- Why you can't make a computer that feels pain -- Free will and personhood -- Mechanism and responsibility -- The abilities of men and machines -- Conditions of personhood -- On giving libertarians what they say they want -- How to change your mind -- Where am I? (shrink)

Philosophical Analysis Its Development between the Two World Wars.

Unlike the first English translation in 1974, this edition contains the text corresponding to Brentano's original 1874 edition.

Imre Lakatos' philosophical and scientific papers are published here in two volumes. Volume I brings together his very influential but scattered papers on the philosophy of the physical sciences, and includes one important unpublished essay on the effect of Newton's scientific achievement. Volume II presents his work on the philosophy of mathematics (much of it unpublished), together with some critical essays on contemporary philosophers of science and some famous polemical writings on political and educational issues. Imre Lakatos had an influence (...) out of all proportion to the length of his philosophical career. This collection exhibits and confirms the originality, range and the essential unity of his work. It demonstrates too the force and spirit he brought to every issue with which he engaged, from his most abstract mathematical work to his passionate 'Letter to the director of the LSE'. Lakatos' ideas are now the focus of widespread and increasing interest, and these volumes should make possible for the first time their study as a whole and their proper assessment. (shrink)

Tested the 2-process theory of detection, search, and attention presented by the current authors in a series of experiments. The studies demonstrate the qualitative difference between 2 modes of information processing: automatic detection and controlled search; trace the course of the learning of automatic detection, of categories, and of automatic-attention responses; and show the dependence of automatic detection on attending responses and demonstrate how such responses interrupt controlled processing and interfere with the focusing of attention. The learning of categories is (...) shown to improve controlled search performance. A general framework for human information processing is proposed. The framework emphasizes the roles of automatic and controlled processing. The theory is compared to and contrasted with extant models of search and attention. (shrink)

This is a unique book. It is excellently written, crammed with information, wise and a pleasure to read.' ---Daniel C. Dennett, Tufts University.

Ethologists and others studying animal behavior in a spirit are in need of a descriptive language and method that are neither anachronistically bound by behaviorist scruples nor prematurely committed to particular Just such an interim descriptive method can be found in intentional system theory. The use of intentional system theory is illustrated with the case of the apparently communicative behavior of vervet monkeys. A way of using the theory to generate data - including usable, testable data - is sketched. The (...) underlying assumptions of this approach can be seen to ally it directly with theorizing in evolutionary biology, which has recently come under attack from Stephen Gould and Richard Lewontin, who castigate it as the Their arguments, which are strongly analogous to B. F, Skinner's arguments against point to certain pitfalls that attend the careless exercise of such thinking (and rival varieties of thinking as well), but do not constitute a fundamental objection to either adaptationist theorizing or its cousin, intentional system theory. (shrink)

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do.The basic assumption is that vision depends on the eye which is connected to the brain. The (...) author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision—and what this book is about. (shrink)

This systematic investigation of computation and mental phenomena by a noted psychologist and computer scientist argues that cognition is a form of computation, that the semantic contents of mental states are encoded in the same general way as computer representations are encoded. It is a rich and sustained investigation of the assumptions underlying the directions cognitive science research is taking. 1 The Explanatory Vocabulary of Cognition 2 The Explanatory Role of Representations 3 The Relevance of Computation 4 The Psychological Reality (...) of Programs: Strong Equivalence 5 Constraining Functional Architecture 6 The Bridge from Physical to Symbolic: Transduction 7 Functional Architecture and Analogue Processes 8 Mental Imagery and Functional Architecture 9 Epilogue: What is Cognitive Science the Science of? (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...) e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...) defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise. (shrink)

What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call "strong" AI from "weak" or "cautious" AI. According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. (...) But according to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to. (shrink)

What are 'emotions'? This book offers a balanced survey of facts and theory.

Cognitive science typically postulates unconscious mental phenomena, computational or otherwise, to explain cognitive capacities. The mental phenomena in question are supposed to be inaccessible in principle to consciousness. I try to show that this is a mistake, because all unconscious intentionality must be accessible in principle to consciousness; we have no notion of intrinsic intentionality except in terms of its accessibility to consciousness. I call this claim the The argument for it proceeds in six steps. The essential point is that (...) intrinsic intentionality has aspectual shape: Our mental representations represent the world under specific aspects, and these aspectual features are essential to a mental state's being the state that it is. (shrink)

Little, Brown, 1992 Review by Glenn Branch on Jul 5th 1999 Volume: 3, Number: 27.

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)

In this study on deduction, the authors argue that people reason by imagining the relevant state of affairs, ie building an internal model of it, formulating a tentative conclusion based on this model and then searching for alternative models.

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)

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

This book develops a new theory of determinism that offers fresh insights into questions of how intentions and other mental events relate to neural events, how both come about, and how both result in actions. Honderich tests his theory against neuroscience, quantum theory, and possible philosophical refutations, and discusses the consequences of determinism and near-determinism for life-hopes, knowledge, and personal feelings.

Through the use of such "folk" concepts as belief, desire, intention, and expectation, Daniel Dennett asserts in this first full scale presentation of...

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)