Lerdahl and Jackendoff's Generative Theory of Tonal Music (GTTM) is an important contribution to cognitive science. Jackendoff claims it is a computationalist theory and that the mental representations it postulates are unconscious. Thus GTTM looks to be a kind of cognitive science remote from the folk-psychological. I argue that this picture of GTTM is mistaken: GTTM is at least as much music analysis as cognitive science. Jackendoff's metatheory fails to explain how a listener can tell that a structural description corresponds (...) to the way she hears, how analytically minded listeners can communicate about their hearing, and how a reader of their book can comprehend it. I suggest an alternative construal, on which GTTM's analytical vocabulary functions as a public language and its mental representations are perceptual beliefs. Interesting philosophical problems ensue about knowledge of musical structure and knowledge about what structures one hears. There is a paradox: one wants an analysis to be true to a hearing, yet to be illuminating. Though analysis and hearing coincide in content at an abstract level, they do not coincide in conceptual content. What sort of knowledge then underlies the inference from perceptual to music-analytical representation? I argue that such knowledge is a priori. (shrink)

Functionalism in the philosophy of mind is here criticized from the perspective of a more naturalistic and less compromising form of materialism. Parallels are explored between the problem of cognitive activity and the somewhat more settled problem of vital activity. The lessons drawn suggest that functionalism in the philosophy of mind may be both counterproductive as a research strategy, and false as a substantive position.

The use of the computer metaphor has led to the proposal of mind architecture (Pylyshyn 1984; Newell 1990) as a model of the organization of the mind. The dualist computational model, however, has, since the earliest days of psychological functionalism, required that the concepts mind architecture and brain architecture be remote from each other. The development of both connectionism and neurocomputational science, has sought to dispense with this dualism and provide general models of consciousness – a uniform cognitive architecture –, (...) which is in general reductionist, but which retains the computer metaphor. This paper examines, in the first place, the concepts of mind architecture and brain architecture, in order to evaluate the syntheses which have recently been offered. It then moves on to show how modifications which have been made to classical functionalist mind architectures, with the aim of making them compatible with brain architectures, are unable to resolve some of the most serious problems of functionalism. Some suggestions are given as to why it is not possible to relate mind structures and brain structures by using neurocomputational approaches, and finally the question is raised of the validity of reductionism in a theory which sets out to unite mind and brain architectures. (shrink)

I examine an intuitive property of folk-psychological explanations I call self-sufficiency. I argue that individualism cannot honor this property and work toward distilling an account of psychological explanation that does honor it, given some fairly standard assumptions. In doing so, my preference for an Externalist individuation of intentional state will emerge unambiguously. The assumptions I rely on are fairly standard but not uncontroversial. Yet not always do I attempt to defend them from objections. My goal is an account of folk (...) psychology consistent with our every-day practices rather than the deduction of an idealized psychology from first principles. I conclude with some applications offered as evidence that the goal was achieved. (shrink)

This paper describes a generative theory of bugs. It claims that all bugs of a procedural skill can be derived by a highly constrained form of problem solving acting on incomplete procedures. These procedures are characterized by formal deletion operations that model incomplete learning and forgetting. The problem solver and the deletion operator have been constrained to make it impossible to derive “star‐bugs”—algorithms that are so absurd that expert diagnosticians agree that the alogorithm will never be observed as a bug. (...) Hence, the theory not only generates the observed bugs, it fails to generate star‐bugs.The theory has been tested on an extensive data base of bugs for multidigit subtraction that was collected with the aid of the diagnostic systems buggy and debuggy. In addition to predicting bug occurrence, by adoption of additional hypotheses, the theory also makes predictions about the frequency and stability of bugs, as well as the occurrence of certain latencies in processing time during testing. Arguments are given that the theory can be applied to domains other than subtraction and that it can be extended to provide a theory of procedural learning that accounts for bug acquisition. Lastly, particular care has been taken to make the theory principled so that it can not be tailored to fit any possible data. (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)

It is generally accepted that there is something special about reasoning by using mental images. The question of how it is special, however, has never been satisfactorily spelled out, despite more than thirty years of research in the post-behaviorist tradition. This article considers some of the general motivation for the assumption that entertaining mental images involves inspecting a picture-like object. It sets out a distinction between phenomena attributable to the nature of mind to what is called the cognitive architecture, and (...) ones that are attributable to tacit knowledge used to simulate what would happen in a visual situation. With this distinction in mind, the paper then considers in detail the widely held assumption that in some important sense images are spatially displayed or are depictive, and that examining images uses the same mechanisms that are deployed in visual perception. I argue that the assumption of the spatial or depictive nature of images is only explanatory if taken literally, as a claim about how images are physically instantiated in the brain, and that the literal view fails for a number of empirical reasons – for example, because of the cognitive penetrability of the phenomena cited in its favor. Similarly, while it is arguably the case that imagery and vision involve some of the same mechanisms, this tells us very little about the nature of mental imagery and does not support claims about the pictorial nature of mental images. Finally, I consider whether recent neuroscience evidence clarifies the debate over the nature of mental images. I claim that when such questions as whether images are depictive or spatial are formulated more clearly, the evidence does not provide support for the picture-theory over a symbol-structure theory of mental imagery. Even if all the empirical claims were true, they do not warrant the conclusion that many people have drawn from them: that mental images are depictive or are displayed in some (possibly cortical) space. Such a conclusion is incompatible with what is known about how images function in thought. We are then left with the provisional counterintuitive conclusion that the available evidence does not support rejection of what I call the “null hypothesis”; namely, that reasoning with mental images involves the same form of representation and the same processes as that of reasoning in general, except that the content or subject matter of thoughts experienced as images includes information about how things would look. (shrink)

In their critique of connectionist models Fodor and Pylyshyn (1988) dismiss such models as not being cognitive or psychological. Evaluating Fodor and Pylyshyn's critique requires examining what is required in characterizating models as 'cognitive'. The present discussion examines the various senses of this term. It argues the answer to the title question seems to vary with these different senses. Indeed, by one sense of the term, neither representa-tionalism nor connectionism is cognitive. General ramifications of such an appraisal are discussed and (...) alternative avenues for cognitive research are suggested. (shrink)

This paper addresses a recent argument of the Churchlands against the "linguistic-rationalist" tradition exemplified by current cognitive-computational psychology. Because of its commitment to methodological solipsism--the argument goes--computational psychology cannot provide an account of how organisms are able to represent and "hook up to" the world. First I attempt to determine the exact nature of this charge and its relation to the Churchlands' long-standing polemic against 'folk psychology' and the linguistic-rationalist methodology. I then turn my attention to the Churchlands' account of (...) what it is for computational psychology to be methodologically solipsistic. I argue that there is no reason to suppose that methodological solipsism commits one to a purely syntactic theory of the mind (of the kind that Stephen Stich has recently advocated): the formality constraints that methodological solipsism imposes on psychological explanation do not exclude 'essential' reference to the representational content of mental states, as long as this content is construed in the 'narrow' sense. I conclude by raising a problem for computational psychology that may provide some real cause for concern. (shrink)