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)

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)

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)

The computational view of mind rests on certain intuitions regarding the fundamental similarity between computation and cognition. We examine some of these intuitions and suggest that they derive from the fact that computers and human organisms are both physical systems whose behavior is correctly described as being governed by rules acting on symbolic representations. Some of the implications of this view are discussed. It is suggested that a fundamental hypothesis of this approach is that there is a natural domain of (...) human functioning that can be addressed exclusively in terms of a formal symbolic or algorithmic vocabulary or level of analysis. Much of the paper elaborates various conditions that need to be met if a literal view of mental activity as computation is to serve as the basis for explanatory theories. The coherence of such a view depends on there being a principled distinction between functions whose explanation requires that we posit internal representations and those that we can appropriately describe as merely instantiating causal physical or biological laws. In this paper the distinction is empirically grounded in a methodological criterion called the " cognitive impenetrability condition." Functions are said to be cognitively impenetrable if they cannot be influenced by such purely cognitive factors as goals, beliefs, inferences, tacit knowledge, and so on. Such a criterion makes it possible to empirically separate the fixed capacities of mind from the particular representations and algorithms used on specific occasions. In order for computational theories to avoid being ad hoc, they must deal effectively with the "degrees of freedom" problem by constraining the extent to which they can be arbitrarily adjusted post hoc to fit some particular set of observations. This in turn requires that the fixed architectural function and the algorithms be independently validated. It is argued that the architectural assumptions implicit in many contemporary models run afoul of the cognitive impenetrability condition, since the required fixed functions are demonstrably sensitive to tacit knowledge and goals. The paper concludes with some tactical suggestions for the development of computational cognitive theories. (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)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be grounded (...) in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

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)

Each of the chapters in this volume devotes considerable attention to defining and elaborating the notion of the frame problem-one of the hard problems of artificial intelligence. Not only do the chapters clarify the problems at hand, they shed light on the different approaches taken by those in artificial intelligence and by certain philosophers who have been concerned with related problems in their field. The book should therefore not be read merely as a discussion of the frame problem narrowly conceived, (...) but also as a general analysis of what could be a major challenge to the design of computer systems exhibiting general intelligence. (shrink)

Since the BBS article in which Premack and Woodruff (1978) asked “Does the chimpanzee have a theory of mind?,” it has been repeatedly claimed that there is observational and experimental evidence that apes have mental state concepts, such as “want” and “know.” Unlike research on the development of theory of mind in childhood, however, no substantial progress has been made through this work with nonhuman primates. A survey of empirical studies of imitation, self-recognition, social relationships, deception, role-taking, and perspective-taking suggests (...) that in every case where nonhuman primate behavior has been interpreted as a sign of theory of mind, it could instead have occurred by chance or as a product of nonmentalistic processes such as associative learning or inferences based on nonmental categories. Arguments to the effect that, in spite of this, the theory of mind hypothesis should be accepted because it is more parsimonious than alternatives or because it is supported by convergent evidence are not compelling. Such arguments are based on unsupportable assumptions about the role of parsimony in science and either ignore the requirement that convergent evidence proceed from independent assumptions, or fail to show that it supports the theory of mind hypothesis over nonmentalist alternatives. Progress in research on theory of mind requires experimental procedures that can distinguish the theory of mind hypothesis from nonmentalist alternatives. A procedure that may have this potential is proposed. It uses conditional discrimination training and transfer tests to determine whether chimpanzees have the concept “see.” Commentators are invited to identify flaws in the procedure and to suggest alternatives. Key Words: apes; associative learning; concepts; convergence; deception; evolution of intelligence; folk psychology; imitation; mental state attribution; monkeys; parsimony; perspective-taking; primates; role-taking; self-recognition; social cognition; social intelligence; theory of mind. (shrink)

There are many possible approaches to the mind/brain problem. One of the most prominent, and perhaps the most practical, is to ignore it.

Connectionism and computationalism are currently vying for hegemony in cognitive modeling. At first glance the opposition seems incoherent, because connectionism is itself computational, but the form of computationalism that has been the prime candidate for encoding the "language of thought" has been symbolic computationalism (Dietrich 1990, Fodor 1975, Harnad 1990c; Newell 1980; Pylyshyn 1984), whereas connectionism is nonsymbolic (Fodor & Pylyshyn 1988, or, as some have hopefully dubbed it, "subsymbolic" Smolensky 1988). This paper will examine what is and is not (...) a symbol system. A hybrid nonsymbolic/symbolic system will be sketched in which the meanings of the symbols are grounded bottom-up in the system's capacity to discriminate and identify the objects they refer to. Neural nets are one possible mechanism for learning the invariants in the analog sensory projection on which successful categorization is based. "Categorical perception" (Harnad 1987a), in which similarity space is "warped" in the service of categorization, turns out to be exhibited by both people and nets, and may mediate the constraints exerted by the analog world of objects on the formal world of symbols. (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)

Many special problems crop up when evolutionary theory turns, quite naturally, to the question of the adaptive value and causal role of consciousness in human and nonhuman organisms. One problem is that -- unless we are to be dualists, treating it as an independent nonphysical force -- consciousness could not have had an independent adaptive function of its own, over and above whatever behavioral and physiological functions it "supervenes" on, because evolution is completely blind to the difference between a conscious (...) organism and a functionally equivalent (Turing Indistinguishable) nonconscious "Zombie" organism: In other words, the Blind Watchmaker, a functionalist if ever there was one, is no more a mind reader than we are. Hence Turing-Indistinguishability = Darwin-Indistinguishability. It by no means follows from this, however, that human behavior is therefore to be explained only by the push-pull dynamics of Zombie determinism, as dictated by calculations of "inclusive fitness" and "evolutionarily stable strategies." We are conscious, and, more important, that consciousness is piggy-backing somehow on the vast complex of unobservable internal activity -- call it "cognition" -- that is really responsible for generating all of our behavioral capacities. Hence, except in the palpable presence of the irrational (e.g., our sexual urges) where distal Darwinian factors still have some proximal sway, it is as sensible to seek a Darwinian rather than a cognitive explanation for most of our current behavior as it is to seek a cosmological rather than an engineering explanation of an automobile's behavior. Let evolutionary theory explain what shaped our cognitive capacity (Steklis & Harnad 1976; Harnad 1996, but let cognitive theory explain our resulting behavior. (shrink)

The vivid terms, "Top-down" and "Bottom-up" have become popular in several different contexts in cognitive science. My task today is to sort out some different meanings and comment on the relations between them, and their implications for cognitive science.

When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual (...) mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one. (shrink)

The paper examines the nature of the behavioral evidence underlying attributions of intelligence in the case of human beings, and how this might be extended to other kinds of cognitive system, in the spirit of the original Turing Test. I consider Harnad's Total Turing Test, which involves successful performance of both linguistic and robotic behavior, and which is often thought to incorporate the very same range of empirical data that is available in the human case. However, I argue that the (...) TTT is still too weak, because it only tests the capabilities of particular tokens within a preexisting context of intelligent behavior. What is needed is a test of the cognitive type, as manifested through a number of exemplary tokens, in order to confirm that the cognitive type is able to produce the context of intelligent behavior presupposed by tests such as the TT and TTT. (shrink)

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind.

I have discussed the frame problem and the Turing test at length, but I have not attempted to spell out what I think the implications of the frame problem ...

Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished (...) from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about. (shrink)

Harnad and I agree that the Chinese Room Argument deals a knockout blow to Strong AI, but beyond that point we do not agree on much at all. So let's begin by pondering the implications of the Chinese Room. The Chinese Room shows that a system, me for example, could pass the Turing Test for understanding Chinese, for example, and could implement any program you like and still not understand a word of Chinese. Now, why? What does the genuine Chinese (...) speaker have that I in the Chinese Room do not have? The answer is obvious. I, in the Chinese room, am manipulating a bunch of formal symbols; but the Chinese speaker has more than symbols, he knows what they mean. That is, in addition to the syntax of Chinese, the genuine Chinese speaker has a semantics in the form of meaning, understanding, and mental contents generally. (shrink)

This target article argues that the Turing test implicitly rests on a "naive psychology," a naturally evolved psychological faculty which is used to predict and understand the behaviour of others in complex societies. This natural faculty is an important and implicit bias in the observer's tendency to ascribe mentality to the system in the test. The paper analyses the effects of this naive psychology on the Turing test, both from the side of the system and the side of the observer, (...) and then proposes and justifies an inverted version of the test which allows the processes of ascription to be analysed more directly than in the standard version. (shrink)

The Mind/Body Problem is about causation not correlation. And its solution will require a mechanism in which the mental component somehow manages to play a causal role of its own, rather than just supervening superflously on other, nonmental components that look, for all the world, as if they can do the full causal job perfectly well without it. Correlations confirm that M does indeed "supervene" on B, but causality is needed to show how/why M is not supererogatory; and that's the (...) hard part. (shrink)

Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is (...) "everything" a body with a mind can do? Turing's original "pen-pal" version (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body. (shrink)

It is important to understand that the Turing Test is not, nor was it intended to be, a trick; how well one can fool someone is not a measure of scientific progress. The TT is an empirical criterion: It sets AI's empirical goal to be to generate human-scale performance capacity. This goal will be met when the candidate's performance is totally indistinguishable from a human's. Until then, the TT simply represents what it is that AI must endeavor eventually to accomplish (...) scientifically. (shrink)

Heyes's literature review of deception, imitation, and self-recognition is inadequate, misleading, and erroneous. The anaesthetic artifact hypothesis of self-recognition is unsupported by the data she herself examines. Her proposed experiment is tantalizing, indicating that theory of mind is simply a Turing test.

Critique of Computationalism as merely projecting hermeneutics (i.e., meaning originating from the mind of an external interpreter) onto otherwise intrinsically meaningless symbols. Projecting an interpretation onto a symbol system results in its being reflected back, in a spuriously self-confirming way.

Critique of Computationalism as merely projecting hermeneutics (i.e., meaning originating from the mind of an external interpreter) onto otherwise intrinsically meaningless symbols.

Artificial life can take two forms: synthetic and virtual. In principle, the materials and properties of synthetic living systems could differ radically from those of natural living systems yet still resemble them enough to be really alive if they are grounded in the relevant causal interactions with the real world. Virtual (purely computational) "living" systems, in contrast, are just ungrounded symbol systems that are systematically interpretable as if they were alive; in reality they are no more alive than a virtual (...) furnace is hot. Virtual systems are better viewed as "symbolic oracles" that can be used (interpreted) to predict and explain real systems, but not to instantiate them. The vitalistic overinterpretation of virtual life is related to the animistic overinterpretation of virtual minds and is probably based on an implicit (and possibly erroneous) intuition that living things have actual or potential mental lives. (shrink)

It is hypothesized that words originated as the names of perceptual categories and that two forms of representation underlying perceptual categorization -- iconic and categorical representations -- served to ground a third, symbolic, form of representation. The third form of representation made it possible to name and describe our environment, chiefly in terms of categories, their memberships, and their invariant features. Symbolic representations can be shared because they are intertranslatable. Both categorization and translation are approximate rather than exact, but the (...) approximation can be made as close as we wish. This is the central property of that universal mechanism for sharing descriptions that we call natural language. (shrink)

Harnad 's proposed "robotic upgrade" of Turing's Test, from a test of linguistic capacity alone to a Total Turing Test of linguistic and sensorimotor capacity, conflicts with his claim that no behavioral test provides even probable warrant for attributions of thought because there is "no evidence" [p.45] of consciousness besides "private experience" [p.52]. Intuitive, scientific, and philosophical considerations Harnad offers in favor of his proposed upgrade are unconvincing. I agree with Harnad that distinguishing real from "as if" thought on the (...) basis of consciousness testing as sufficient warrant for mental attribution) has the skeptical consequence Harnad accepts -- "there is in fact no evidence for me that anyone else but me has a mind" [p.45]. I disagree with his acceptance of it! It would be better to give up the neo-Cartesian "faith" [p.52] in private conscious experience underlying Harnad 's allegiance to Searle's controversial Chinese Room "Experiment" than give up all claim to know others think. It would be better to allow that Turing's Test evidences -- even strongly evidences -- thought. (shrink)

Cognitive science is a form of "reverse engineering" (as Dennett has dubbed it). We are trying to explain the mind by building (or explaining the functional principles of) systems that have minds. A "Turing" hierarchy of empirical constraints can be applied to this task, from t1, toy models that capture only an arbitrary fragment of our performance capacity, to T2, the standard "pen-pal" Turing Test (total symbolic capacity), to T3, the Total Turing Test (total symbolic plus robotic capacity), to T4 (...) (T3 plus internal [neuromolecular] indistinguishability). All scientific theories are underdetermined by data. What is the right level of empirical constraint for cognitive theory? I will argue that T2 is underconstrained (because of the Symbol Grounding Problem and Searle's Chinese Room Argument) and that T4 is overconstrained (because we don't know what neural data, if any, are relevant). T3 is the level at which we solve the "other minds" problem in everyday life, the one at which evolution operates (the Blind Watchmaker is no mind-reader either) and the one at which symbol systems can be grounded in the robotic capacity to name and manipulate the objects their symbols are about. I will illustrate this with a toy model for an important component of T3 -- categorization -- using neural nets that learn category invariance by "warping" similarity space the way it is warped in human categorical perception: within-category similarities are amplified and between-category similarities are attenuated. This analog "shape" constraint is the grounding inherited by the arbitrarily shaped symbol that names the category and by all the symbol combinations it enters into. No matter how tightly one constrains any such model, however, it will always be more underdetermined than normal scientific and engineering theory. This will remain the ineliminable legacy of the mind/body problem. (shrink)

Behavioral scientists studied behavior; cognitive scientists study what generates behavior. Cognitive science is hence theoretical behaviorism (or behaviorism is experimental cognitivism). Behavior is data for a cognitive theorist. What counts as a theory of behavior? In this paper, a methodological constraint on theory construction -- "neoconstructivism" -- will be proposed (by analogy with constructivism in mathematics): Cognitive theory must be computable; given an encoding of the input to a behaving system, a theory must be able to compute (an encoding of) (...) its outputs. It is a mistake to conclude, however, that this constraint requires cognitive theory to be computational, or that it follows from this that cognition is computation. (shrink)