It has long been one of the most fundamental problems of philosophy, and it is now, John Searle writes, "the most important problem in the biological sciences": What is consciousness? Is my inner awareness of myself something separate from my body? In what began as a series of essays in The New York Review of Books, John Searle evaluates the positions on consciousness of such well-known scientists and philosophers as Francis Crick, Gerald Edelman, Roger Penrose, Daniel Dennett, David Chalmers, and (...) Israel Rosenfield. He challenges claims that the mind works like a computer, and that brain functions can be reproduced by computer programs. With a sharp eye for confusion and contradiction, he points out which avenues of current research are most likely to come up with a biological examination of how conscious states are caused by the brain. Only when we understand how the brain works will we solve the mystery of consciousness, and only then will we begin to understand issues ranging from artificial intelligence to our very nature as human beings. (shrink)

In What Is Thought? Eric Baum proposes a computational explanation of thought.

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John Searle's (...) attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

There are different ways to present a Presidential Address to the APA; the one I have chosen is simply to report on work that I am doing right now, on work in progress. I am going to present some of my further explorations into the computational model of the mind.\**.

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)

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)

Thought experiments have been used by philosophers for centuries, especially in the study of personal identity where they appear to have been used extensively and indiscriminately. Despite their prevalence, the use of thought experiments in this area of philosophy has been criticized in recent times. Bernard Williams criticizes the conclusions that are drawn from some experiments, and retells one of these experiments from a different perspective, a retelling which leads to a seemingly opposing result. Wilkes criticizes the method of thought (...) experimentation itself, suggesting that the results drawn from the experiments are tainted by a faulty method. This paper examines both these types of objection, and concludes that neither can be sustained. (shrink)

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to (...) the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?". (shrink)

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

As Louisiana and Cuba emerged from slavery in the late nineteenth century, each faced the question of what rights former slaves could claim. Degrees of Freedom compares and contrasts these two societies in which slavery was destroyed by war, and citizenship was redefined through social and political upheaval. Both Louisiana and Cuba were rich in sugar plantations that depended on an enslaved labor force. After abolition, on both sides of the Gulf of Mexico, ordinary people-cane cutters and cigar workers, laundresses (...) and labor organizers-forged alliances to protect and expand the freedoms they had won. But by the beginning of the twentieth century, Louisiana and Cuba diverged sharply in the meanings attributed to race and color in public life, and in the boundaries placed on citizenship. Louisiana had taken the path of disenfranchisement and state-mandated racial segregation; Cuba had enacted universal manhood suffrage and had seen the emergence of a transracial conception of the nation. What might explain these differences? Moving through the cane fields, small farms, and cities of Louisiana and Cuba, Rebecca Scott skillfully observes the people, places, legislation, and leadership that shaped how these societies adjusted to the abolition of slavery. The two distinctive worlds also come together, as Cuban exiles take refuge in New Orleans in the 1880s, and black soldiers from Louisiana garrison small towns in eastern Cuba during the 1899 U.S. military occupation. Crafting her narrative from the words and deeds of the actors themselves, Scott brings to life the historical drama of race and citizenship in postemancipation societies. (shrink)

This book offers a novel analysis of the widely-used but ill-understood technique of thought experiment. The author argues that the powers and limits of this methodology can be traced to the fact that when the contemplation of an imaginary scenario brings us to new knowledge, it does so by forcing us to make sense of exceptional cases.

To compute is to execute an algorithm. More precisely, to say that a device or organ computes is to say that there exists a modelling relationship of a certain kind between it and a formal specification of an algorithm and supporting architecture. The key issue is to delimit the phrase of a certain kind. I call this the problem of distinguishing between standard and nonstandard models of computation. The successful drawing of this distinction guards Turing's 1936 analysis of computation against (...) a difficulty that has persistently been raised against it, and undercuts various objections that have been made to the computational theory of mind. (shrink)

Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic model of the mind. Nonsymbolic modeling turns (...) out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem. (shrink)

Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. Nonsymbolic modeling (...) turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem. (shrink)

Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that (...) artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them. (shrink)

Against Norton's claim that all thought experiments can be reduced to explicit arguments, I defend Brown's position that certain thought experiments yield a priori knowledge. They do this, I argue, not by allowing us to perceive “Platonic universals” (Brown), even though they may contain non-propositional components that are epistemically indispensable, but by helping to identify certain tacit presuppositions or “natural interpretations” (Feyerabend's term) that lead to a contradiction when the phenomenon is described in terms of them, and by suggesting a (...) new natural interpretation in terms of which the phenomenon can be redescribed free of contradiction. (shrink)

In this paper I argue that (at least many) philosophical thought experiments are unreliable. But I argue that this notion of unreliability has to be understood relative to the goal of thought experiments as knowledge producing. And relative to that goal many thought experiments in science are just as unreliable. But in fact thought experiments in science play a varied role and I will suggest that knowledge production is a goal only under quite limited circumstances. I defend the view that (...) these circumstances can (sometimes) arise in philosophy as well. (shrink)

This book explores the scope and limits of the concept of personDS a vexed question in contemporary philosophy. The author begins by questioning the methodology of thought-experimentation, arguing that it engenders inconclusive and unconvincing results, and that truth is stranger than fiction. She then examines an assortment of real-life conditions, including infancy, insanity andx dementia, dissociated states, and split brains. The popular faith in continuity of consciousness, and the unity of the person is subjected to sustained criticism. The author concludes (...) with a look at different views of the person found in Homer, Aristotle, the post-Cartesians, and contemporary cognitive science. (shrink)