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1989, Synthese
There is a prevalent notion among cognitive scientists and philosophers of mind that computers are merely formal symbol manipulators, performing the actions they do solely on the basis of the syntactic properties of the symbols they manipulate. This view of computers has allowed some philosophers to divorce semantics from computational explanations. Semantic content, then, becomes something one adds to computational explanations to get psychological explanations. Other philosophers, such as Stephen Stich, have taken a stronger view, advocating doing away with semantics entirely. This paper argues that a correct account of computation requires us to attribute content to computational processes in order to explain which functions are being computed. This entails that computational psychology must countenance mental representations. Since anti-semantic positions are incompatible with computational psychology thus construed, they ought to be rejected. Lastly, I argue that in an important sense, computers are not formal symbol manipulators.
Minds and Machines 9: 347-381 1999, 1999
Over the past several decades, the philosophical community has witnessed the emergence of an important new paradigm for understanding the mind.1 The paradigm is that of machine computation, and its influence has been felt not only in philosophy, but also in all of the empirical disciplines devoted to the study of cognition. Of the several strategies for applying the resources provided by computer and cognitive science to the philosophy of mind, the one that has gained the most attention from philosophers has been the ‘Computational Theory of Mind’ (CTM). CTM was first articulated by Hilary Putnam (1960, 1961), but finds perhaps its most consistent and enduring advocate in Jerry Fodor (1975, 1980, 1981, 1987, 1990, 1994). It is this theory, and not any broader interpretations of what it would be for the mind to be a computer, that I wish to address in this paper. What I shall argue here is that the notion of ‘symbolic representation’ employed by CTM is fundamentally unsuited to providing an explanation of the intentionality of mental states (a major goal of CTM), and that this result undercuts a second major goal of CTM, sometimes refered to as the ‘vindication of intentional psychology.’ This line of argument is related to the discussions of ‘derived intentionality’ by Searle (1980, 1983, 1984) and Sayre (1986, 1987). But whereas those discussions seem to be concerned with the causal dependence of familiar sorts of symbolic representation upon meaningbestowing acts, my claim is rather that there is not one but several notions of ‘meaning’ to be had, and that the notions that are applicable to symbols are conceptually dependent upon the notion that is applicable to mental states in the fashion that Aristotle refered to as paronymy. That is, an analysis of the notions of ‘meaning’ applicable to symbols reveals that they contain presuppositions about meaningful mental states, much as Aristotle’s analysis of the sense of ‘healthy’ that is applied to foods reveals that it means ‘conducive to having a healthy body,’ and hence any attempt to explain ‘mental semantics’ in terms of the semantics of symbols is doomed to circularity and regress. I shall argue, however, that this does not have the consequence that computationalism is bankrupt as a paradigm for cognitive science, as it is possible to reconstruct CTM in a fashion that avoids these difficulties and makes it a viable research framework for psychology, albeit at the cost of losing its claims to explain intentionality and to vindicate intentional psychology. I have argued elsewhere (Horst, 1996) that local special sciences such as psychology do not require vindication in the form of demonstrating their reducibility to more fundamental theories, and hence failure to make good on these philosophical promises need not compromise the broad range of work in empirical cognitive science motivated by the computer paradigm in ways that do not depend on these problematic treatments of symbols.
Advances in multimedia and interactive technologies book series, 2018
The concepts of computation and representation have played fundamental role in cognitive science since its inception in the middle of the past century. In the last decades, they are subject to attacks from different anti-computationalist, or 'post-cognitivist', schools. As shown thereafter, the latter are likely a step back to conventional natural science with its poor explanative power towards cognitive phenomena, while rigorous symbolism is prone to paradoxes and lacks any biological realism. So, only the middle, weak-computationalist, path remains, though it promises quite a long and winding road to a satisfactory cognitive theory. If there are ways in which philosophers can aid cognitive researchers, surely one of them is by helping those researchers determine the appropriateness of employing notions such as representation in various contexts. William Ramsey (Ramsey 1997, 36) What is now known as the 'cognitive revolution' in the sciences of (human) cognition arose as a reaction to neo-behaviorism at a time when the real computer revolution gave psychologists and linguists conceptual tools for the scientific study of mind, whereas before that the intention to avoid psychological terms had been seen as a good tone in scientific researches. The original version of this neo-menalistic view was probably too straightforwardly copied from computer science and included two basic elements: computations that are performed over symbolic representations. That is, computations and representations have become two conceptual pillars on which the cognitive paradigm rests. They remained beyond doubt and suspicion at the classical stage of cognitive sciences, which in the literature is usually referred to as 'symbolism', 'classicism', and 'computationalism'. However, the emergence of connectionism put these basic concepts into 2 question: if repeated re-calculation of weights of inter-neural connections is computation, and changing activation patterns are representations, then the very meaning of these terms now needs to be clarified: perhaps, by transferring them to a lower ,'hardware' level of explanation. The signal to this reconsidering was the attack, well-known by the recent history, entertained by Fodor and Pylyshyn on semantic capabilities of connectionist networks (Fodor and Pylyshin, 1988) and the ensuing discussion with Smolensky (Smolensky, 1988) on the applicability of systematicity, compositionality and productivity as properties of the semantics of connectionist representations. An important milestone in this discussion was an article by Ramsey (Ramsey, 1997), in he showed that the notion of representation is not necessaty in the most common types of neural networks. Senior Researcher, Institute of Philosophy, Russian Academy of Sciences. Candidate (PhD) of Philosophy. 1 The latter term, as we shall see further, is not entirely accurate. 2
The COmputational Metaphor and Cognitive Psychology, 1989
Minds & Machines, 2012
This paper deals with the question: which notion of computation (if any) is essential for explaining cognition? Five answers are discussed in the paper. 1. The classicist answer: symbolic (digital) computation is required for explaining cognition. 2. The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition. 3. The connectionist answer: sub-symbolic computation is required for explaining cognition. 4. The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition. 5. The extreme dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation.
Behavioral and Brain Sciences, 1980
Abstract: 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 ...
The emergence of cognitive science as a multi-disciplinary investigation into the nature of mind has historically revolved around the core assumption that the central ‘cognitive’ aspects of mind are computational in character. Although there is some disagreement and philosophical speculation concerning the precise formulation of this ‘core assumption’ it is generally agreed that computationalism in some form lies at the heart of cognitive science as it is currently conceived. Von Eckardt’s recent work on this topic is useful in enabling us to get a sense of the scope of the computational assumption. She makes clear that there are two rather different ways in which we could understand cognitive science’s commitment to computationalism and hence two ways to understand the claim that the ‘mind is a computer’, by appeal to either (1) A mathematical theory of computability or (2) A theory of data-processing or information-processing. Importantly, she also argues that although there are many aspects of claim that the ‘mind is a computer’ that can be nicely captured by Boyd’s account of the way scientific metaphors are employed, not to direct attention to the hitherto unnoticed, but to encourage investigation of the unknown. Nonetheless, cognitive scientists are not making the claim that the ‘mind is a computer’ in a metaphorical sense. If Von Eckhardt is correct, when cognitive scientists assume the ‘mind is a computer’ and give a sense to the notion of the computer in the sense of (2) above, they are making a literal claim about the nature of mind (Von Eckardt, 1993, p. 116). And as she points out that if one reads (2) in a theoretically committed way then there is no a priori reason to exclude the organic brain from the list of entities that might fall under the description of being a ‘computer’. Important, we can truly describe it as a data-processing (or information-processing) device. What is useful about Von Eckardt’s general analysis of computationalism’s core assumption is that it provides a clear angle from which to view the flaws of computationalism. This paper defends the claim that if there is an account of information adequate to capture those aspects of mind that we regard as essential to mentality it is one that requires us to surrender the idea that the mind is a computer.
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