In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.

The ability to make informative comparisons is central to human cognition. Comparison involves aligning two representations and placing their elements into correspondence. Detecting correspondences is a necessary component of analogical inference, recognition, categorization, schema formation, and similarity judgment. Connectionist Analogy Builder (CAB) determines correspondences through a simple iterative computation that matches elements in one representation with elements playing compatible roles in the other representation while simultaneously enforcing structural constraints. CAB shows promise as a process model of comparison as its (...) performance can be related to human performance (e.g., solution trajectory, error patterns, time‐on‐task). Furthermore, CAB's bounded working memory allows it to account for the inherent capacity limitations of human processing. CAB's strengths are its parsimony, transparency of operations, and ability to generate performance predictions. In this paper, CAB is evaluated against benchmark phenomena from the analogy literature. (shrink)

Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for (...) fully distributed models, and its applicability can be extended through comparisons with a number of classic mathematical models of behaviour. There are reasons why localist models have been underused, though these often misconstrue the localist position. In particular, many conclusions about connectionist representation, based on neuroscientific observation, can be called into question. There are still some problems inherent in the application of fully distributed systems and some inadequacies in proposed solutions to these problems. In the domain of psychological modelling, localist modelling is to be preferred. Key Words: choice; competition; connectionist modelling; consolidation; distributed; localist; neural networks; reaction-time. (shrink)

Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and (...) powerful models of memory, perception, motor control, categorization, and reasoning. What makes the connectionist approach unique is not its variety of representational possibilities or its departure from explicit rule-based models, or even its preoccupation with the brain metaphor. Rather, it is that connectionist models can be used to explore systematically the complex interaction between learning and representation, as we try to demonstrate through the analysis of several large networks. (shrink)

The aim of this paper is to demonstrate a _prima facie_ tension between our commonsense conception of ourselves as thinkers and the connectionist programme for modelling cognitive processes. The language of thought hypothesis plays a pivotal role. The connectionist paradigm is opposed to the language of thought; and there is an argument for the language of thought that draws on features of the commonsense scheme of thoughts, concepts, and inference. Most of the paper (Sections 3-7) is taken up (...) with the argument for the language of thought hypothesis. The argument for an opposition between connectionism and the language of thought comes towards the end (Section 8), along with some discussion of the potential eliminativist consequences (Sections 9 and. (shrink)

The introduction of connectionist or parallel distributed processing (PDP) systems to model cognitive functions has raised the question of the possible relations between these models and traditional information processing models which employ rules to manipulate representations. After presenting a brief account of PDP models and two ways in which they are commonly interpreted by those seeking to use them to explain cognitive functions, I present two ways one might relate these models to traditional information processing models and so not (...) totally repudiate the tradition of modelling cognition through systems of rules and representations. The proposal that seems most promising is that PDP-type structures might provide the underlying framework in which a rule and representation model might be implemented. To show how one might pursue such a strategy, I discuss recent research by Barsalou on the instability of concepts and show how that might be accounted for in a system whose microstructure had a PDP architecture. I also outline how adopting a multi-leveled view of the mind, where on one level the mind employed a PDP-type system and at another level constituted a rule processing system, would allow researchers to relocate some problems which seemed difficult to explain at one level, such as the capacity for concept learning, to another level where it could be handled in a straightforward manner. (shrink)

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence (...) problems, and the representation of complex concepts. (shrink)

Hubert and Stuart Dreyfus have tried to place connectionism and artificial intelligence in a broader historical and intellectual context. This history associates connectionism with neuroscience, conceptual holism, and nonrationalism, and artificial intelligence with conceptual atomism, rationalism, and formal logic. The present paper argues that the Dreyfus account of connectionism and artificial intelligence is both historically and philosophically misleading.

[Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of generalizations, (...) not just generalize correctly to individual cases; (2) The need to be able to match two or more complex short-term information structures, to enable rapid generalization from recent examples rather than from long-term memories; (3) The need to represent and reason with anomalous combinations of concepts; (4) The need to perform embedded reasoning. This presents special problems for systems using non-concatenative representations (as in mainstream connectionist approaches). We also touched on vague quantification in attitude report complements. Neither this topic nor that of analogies between short-term structures (point 2) has been adequately addressed in the symbolic framework, let alone in connectionism. -/- The opportunities and problems covered are put forward as things worth being optimistic about or pessimistic about, respectively. They are not put forward as decisive arguments for or against connectionism. The hope is that this chapter contributes to a greater understanding of the connectionist/symbolist gap by presenting some unusual issues and by throwing new light on some well known ones. (shrink)

Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf.

Something remarkable is happening in the cognitive sciences. After a quarter of a century of cognitive models that were inspired by the metaphor of the digital computer, the newest cognitive models are inspired by the properties of the brain itself. Variously referred to as connectionist, parallel distributed processing, or neutral network models, they explore the idea that complex intellectual operations can be carried out by large networks of simple, neuron-like units. The units themselves are identical, very low-level and 'stupid'. (...) Intelligent performance is derived from the pattern of connection strengths between units, and the fundamental cognitive activity is pattern recognition and completion. Connectionism and the Mind provides an introduction to this newly emerging approach to understanding the mind. The first few chapters focus on network architecture, offering accessible treatment of the equations that describe learning and the propagation of activation. Furthermore, the reader is walked step-by-step through the activities of networks engaged in pattern recognition, learning, and cognitive tasks such as memory retrieval and prototype formation. The remainder of the book addresses the implications of connectionism for theories of the mind, both philosophical and psychological. Foe example: What Role is played by pattern recognition and completion as basic as cognitive functions? Connectionist models have particular strength in learning and pattern recognition; should they be limited to those functions, or can they provide an overall account of cognitive functioning? In particular, can connectionist models provide an adequate account of the ability to employ linguistic and other symbol systems, or must an adequate system incorporate symbol processing as a basic cognitive capacity? Finally, Connectionism and the Mind examines the relation of connectionist models to philosophical accounts of propositional attitudes, and to a variety of other inquiries in cognitive psychology, linguistics, developmental psychology, artificial intelligence and neuroscience. (shrink)

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how knowledge (...) about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter‐free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions and conflicting multiple inheritance situations during inheritance, and the best‐match or partial‐match computation during recognition. The paper also identifies constraints that must be satisfied by the conceptual structure in order to arrive at an efficient parallel realization. (shrink)

This paper examines Alvin Goldman's discussion of acceptance and uncertainty in chapter 15 of his book, Epistemology and Cognition. Goldman discusses how acceptance and rejection of beliefs might be understood in terms of "winner-take-all" connectionist networks. The paper answers some of the questions he raises in his epistemic evaluation of connectionist programs. The major tool for doing this is a connectionist model of explanatory coherence judgments (Thagard, Behavioral and Brain Sciences, 1989). Finally, there is a discussion of (...) problems for Goldman's general epistemological project that arise if one adopts a different approach to connectionism based on distributed representations. (shrink)

This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can (...) be raised about the potential of distributed representations to allow large amounts of complexly structured information to be adequately encoded and processed. It is questionable whether connectionist models that are claimed to effectively represent structured information can be scaled up to a realistic extent. We conclude that the frame problem provides a difficulty to connectionism that is no less serious than the obstacle it constitutes for classical cognitive science. (shrink)

Abstract:In this paper I assess the explanatory role of internal representations in connectionist models of cognition. Focusing on both the internal‘hidden’units and the connection weights between units, I argue that the standard reasons for viewing these components as representations are inadequate to bestow an explanatorily useful notion of representation. Hence, nothing would be lost from connectionist accounts of cognitive processes if we were to stop viewing the weights and hidden units as internal representations.

This paper discusses the relation between cognitive and implementational levels of analysis. Chalmers (1990, 1993) argues that a connectionist implementation of a classical cognitive architecture possesses a compositional semantics, and therefore undercuts Fodor and Pylyshyn's (1988) argument that connectionist networks cannot possess a compositional semantics. I argue that Chalmers argument misconstrues the relation between cognitive and implementational levels of analysis. This paper clarifies the distinction, and shows that while Fodor and Pylyshyn's argument survives Chalmers' critique, it cannot be (...) used to establish the irrelevance of neurophysiological implementation to cognitive modeling; some aspects of Chater and Oaksford's (1990) response to Fodor and Pylyshyn, though not all, are therefore cogent. (shrink)

Ramsey, Stick and Garon (1991) argue that if the correct theory of mind is some parallel distributed processing theory, then folk psychology must be false. Their idea is that if the nodes and connections that encode one representation are causally active then all representations encoded by the same set of nodes and connections are also causally active. We present a clear, and concrete, counterexample to RSG's argument. In conclusion, we suggest that folk psychology and connectionism are best understood as complementary (...) theories. Each has different limitations, yet each will co-evolve with the other in an overlapping domain of 'normal' psychology. (shrink)

I sketch a theory of cognitive representation from recent "connectionist" cognitive science. I then argue that (i) this theory is reducible to neuroscientific theories, yet (ii) its kinds are multiply realized at a neurobiological level. This argument demonstrates that multiple realizability alone is no barrier to the reducibility of psychological theories. I conclude that the multiple realizability argument, the most influential argument against psychophysical reductionism, should be abandoned.

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)

Connectionism seems to avoid many of the problems of classical artificial intelligence, but has it avoided all of them? In this thesis I examine the problem that Intentionality, the directedness of thought to an object, raises for connectionism. As a preliminary approach, I consider the role of Intentionality in classical artificial intelligence from the programmer’s point of view. In this investigation, one problem I identify with classical artificial intelligence is that the Intentionality of the programmer seems to be projected onto (...) the system, rather than the programmer creating a system whereby Intentionality arises intrinsically within the system. -/- In considering the current practice of connectionism, the same problem with Intentionality reappears. The assignment of Intentional content to input or output nodes in a neural network likewise projects the Intentionality of the programmer onto the system, and that projection is often reinforced by the training process of the neural network. However, connectionism seems to have an advantage over classical artificial intelligence in this respect, in that there is a form of neural network in which the network itself organizes the output nodes. The challenge is to utilize these self-organizing networks without having the programmer project Intentionality onto the system in an act of interpretation. -/- I suggest that the way to overcome these problems in connectionism is to embody the neural network within a world, whether physical or virtual, and to allow the system to develop concepts purely empirically. In this manner, connectionism can make scientific observations concerning the nature of cognition without the risk of contamination from the programmer’s own Intentionality. (shrink)

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space (...) for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are _dissociable_, and on the other, by the _classical_ computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of _connectionism; _and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: _phenomenal experience consists in the explicit_ _representation of information in neurally realized PDP networks_.. (shrink)

Subjects exposed to strings of letters generated by a finite state grammar can later classify grammatical and nongrammatical test strings, even though they cannot adequately say what the rules of the grammar are (e.g., Reber, 1989). The MINERVA 2 (Hintzman, 1986) and Medin and Schaffer (1978) memory‐array models and a number of connectionist outoassociator models are tested against experimental data by deriving mainly parameter‐free predictions from the models of the rank order of classification difficulty of test strings. The importance (...) of different assumptions regarding the coding of features (How should the absence of a feature be coded? Should single letters or digrams be coded?), the learning rule used (Hebb rule vs. delta rule), and the connectivity (Should features be predicted only by previous features in the string, or by all features simultaneously?) is investigated by determining the performance of the models with and without each assumption. Only one class of connectionist model (the simultaneous delta rule) passes all the tests. It is shown that this class of model can be regarded by abstracting a set of representative but incomplete rules of the grammar. (shrink)

There is much in The Sensory Order that recommends the oft-made claim that Hayek anticipated connectionist theories of mind. To the extent that this is so, contemporary arguments against and for connectionism, as advanced by Jerry Fodor, Zenon Pylyshyn, and John Searle, are shown as applicable to theoretical psychology. However, the final section of this chapter highlights an important disanalogy between theoretical psychology and connectionist theories of mind.

In Connectionism and the Philosophy of Psychology, Horgan and Tienson (1996) argue that cognitive processes, pace classicism, are not governed by exceptionless, “representation-level” rules; they are instead the work of defeasible cognitive tendencies subserved by the non-linear dynamics of the brain’s neural networks. Many theorists are sympathetic with the dynamical characterisation of connectionism and the general (re)conception of cognition that it affords. But in all the excitement surrounding the connectionist revolution in cognitive science, it has largely gone unnoticed that (...) connectionism adds to the traditional focus on computational processes, a new focus – one on the vehicles of mental representation, on the entities that carry content through the mind. Indeed, if Horgan and Tienson’s dynamical characterisation of connectionism is on the right track, then so intimate is the relationship between computational processes and representational vehicles, that connectionist cognitive science is committed to a resemblance theory of mental content. (shrink)

Connectionism—also known as parallel distributed processing, or neural network modeling—offers promise as a framework to unite clinical and cognitive psychology, and as a tool for studying conscious and unconscious mental activity. This paper describes a neural network model of the case study of Lucy R., from Freud and Breuer's Studies on Hysteria. Though very simple in architecture, the network spontaneously displays analogues of repression and hallucination, corresponding to Lucy R.'s symptoms. Salient elements of Lucy's conscious experience are represented in the (...) model by the activations of neuronlike processors in a fully interconnected network, without hidden units. The model learns to associate elements of experience that were associated in Lucy's case history. Some of these configurations of elements were traumatic for Lucy; trauma is modeled in the network by "emphatic learning," learning accomplished at an abnormally high learning rate. The model suggests that changing associations among conscious elements are sufficient to generate the symptoms Freud observed: apparent repression, hallucination, and recovery through therapy. In the case of Lucy R., Freud's theoretical inference regarding active but unconscious thought is not required by his data. Instead, the unconscious can be understood as a set of complex dispositions embodied in connections between elements of conscious experience. (shrink)

Uses connectionism (neural networks) to extract the "gist" of a story in order to represent a context going forward for the disambiguation of incoming words as a text is processed.

Classicists and connectionists alike claim to be able to explain systematicity. The proposed classicist explanation, I argue, is little more than a promissory note, one that classicists have no idea how to redeem. Smolensky's (1995) proposed connectionist explanation fares little better: it is not vulnerable to recent classicist objections, but it nonetheless fails, particularly if one requires, as some classicists do, that explanations of systematicity take the form of a‘functional analysis’. Nonetheless, there are, I argue, reasons for cautious optimism (...) about the prospects of a connectionist explanation. (shrink)

This is an overview of recent philosophical discussion about connectionism and the foundations of cognitive science. Connectionist modeling in cognitive science is described. Three broad conceptions of the mind are characterized, and their comparative strengths and weaknesses are discussed: (1) the classical computation conception in cognitive science; (2) a popular foundational interpretation of connectionism that John Tienson and I call “non‐sentential computationalism”; and (3) an alternative interpretation of connectionism we call “dynamical cognition.” Also discussed are two recent philosophical attempts (...) to enlist connectionism in defense of eliminativism about folk psychology. (shrink)

In this paper, I define tacit knowledge as a kind of causal-explanatory structure, mirroring the derivational structure in the theory that is tacitly known. On this definition, tacit knowledge does not have to be explicitly represented. I then take the notion of a modular theory, and project the idea of modularity to several different levels of description: in particular, to the processing level and the neurophysiological level. The fundamental description of a connectionist network lies at a level between the (...) processing level and the physiological level. At this level, connectionism involves a characteristic departure from modularity, and a correlative absence of syntactic structure. This is linked to the fact that tacit knowledge descriptions of networks are only approximately true. A consequence is that strict causal systematicity in cognitive processes poses a problem for the connectionist programme. (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)

This Special Issue on Connectionist Models of Human Language Processing provides an opportunity for an appraisal both of specific connectionist models and of the status and utility of connectionist models of language in general. This introduction provides the background for the papers in the Special Issue. The development of connectionist models of language is traced, from their intellectual origins, to the state of current research. Key themes that arise throughout different areas of connectionist psycholinguistics are (...) highlighted, and recent developments in speech processing, morphology, sentence processing, language production, and reading are described. We argue that connectionist psycholinguistics has already had a significant impact on the psychology of language, and that connectionist models are likely to have an important influence on future research. (shrink)

Recently some philosophers have urged that connectionist artificial intelligence is (potentially) eliminative for the propositional attitudes of folk psychology. At the same time, however, these philosophers have also insisted that since philosophy of science has failed to provide criteria distinguishing ontologically retentive from eliminative theory changes, the resulting eliminativism is not principled. Application of some resources developed within the semantic view of scientific theories, particularly recent formal work on the theory reduction relation, reveals these philosophers to be wrong in (...) this second contention, yet by and large correct in the first. (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.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns (...) and verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a single-system network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology. (shrink)

Theories of language production have long been expressed as connectionist models. We outline the issues and challenges that must be addressed by connectionist models of lexical access and grammatical encoding, and review three recent models. The models illustrate the value of an interactive activation approach to lexical access in production, the need for sequential output in both phonological and grammatical encoding, and the potential for accounting for structural effects on errors and structural priming from learning.

For the past three decades linguistic theory has been based on the assumption that sentences are interpreted and constructed by the brain by means of computational processes analogous to those of a serial-digital computer. The recent interest in devices based on the neural network or parallel distributed processor (PDP) principle raises the possibility ("eliminative connectionism") that such devices may ultimately replace the S-D computer as the model for the interpretation and generation of language by the brain. An analysis of the (...) differences between the two models suggests that the effect of such a development would be to steer linguistic theory towards a return to the empiricism and behaviorism which prevailed before it was driven by Chomsky towards nativism and mentalism. Linguists, however, will not be persuaded to return to such a theory unless and until it can deal with the phenomenon of novel sentence construction as effectively as its nativist/mentalist rival. (shrink)

We think the best prospect for a naturalistic explanation of phenomenal consciousness is to be found at the confluence of two influential ideas about the mind. The first is the _computational _ _theory of mind_: the theory that treats human cognitive processes as disciplined operations over neurally realised representing vehicles.1 The second is the _representationalist theory of _ _consciousness_: the theory that takes the phenomenal character of conscious experiences (the “what-it-is-likeness”) to be constituted by their representational content.2 Together these two (...) theories suggest that phenomenal consciousness might be explicable in terms of the representational content of the neurally realised representing vehicles that are generated and manipulated in the course of cognition. The simplest and most elegant hypothesis that one might entertain in this regard is that conscious experiences are identical to (i.e., are one and the same as) the brain’s representing vehicles. (shrink)

This paper offers both a theoretical and an experimental perspective on the relationship between connectionist and Classical (symbol-processing) models. Firstly, a serious flaw in Fodor and Pylyshyn’s argument against connectionism is pointed out: if, in fact, a part of their argument is valid, then it establishes a conclusion quite different from that which they intend, a conclusion which is demonstrably false. The source of this flaw is traced to an underestimation of the differences between localist and distributed representation. It (...) has been claimed that distributed representations cannot support systematic operations, or that if they can, then they will be mere implementations of traditional ideas. This paper presents experimental evidence against this conclusion: distributed representations can be used to support direct structure-sensitive operations, in a man- ner quite unlike the Classical approach. Finally, it is argued that even if Fodor and Pylyshyn’s argument that connectionist models of compositionality must be mere implementations were correct, then this would still not be a serious argument against connectionism as a theory of mind. (shrink)

The prelims comprise: Introduction The Computational Theory of Mind The Symbol‐system Paradigm The Connectionist Computational Paradigm How are Paradigms Related?

Andy Clark; VI*—Connectionist Minds, Proceedings of the Aristotelian Society, Volume 90, Issue 1, 1 June 1990, Pages 83–102, https://doi.org/10.1093/aristotelia.

A competence model describes the abstract structure of a solution to some problem. or class of problems, facing the would-be intelligent system. Competence models can be quite derailed, specifying far more than merely the function to be computed. But for all that, they are pitched at some level of abstraction from the details of any particular algorithm or processing strategy which may be said to realize the competence. Indeed, it is the point and virtue of such models to specify some (...) equivalence class of algorithms/processing strategies so that the common properties highlighted by the chosen class may feature in psychologically interesting accounts. A question arises concerning the type of relation a theorist might expect to hold between such a competence model and a psychologically real processing strategy. Classical work in cognitive science expects the actual processing to depend on explicit or tacit knowledge of the competence theory. Connectionist work, for reasons to be explained, represents a departure from this norm. But the precise way in which a connectionist approach may disturb the satisfying classical symmetry of competence and processing has yet to be properly specified. A standard ?Newtonian? connectionist account, due to Paul Smolensky, is discussed and contrasted with a somewhat different ?rogue? account. A standard connectionist understanding has it that a classical competence theory describes an idealized subset of a network's behaviour. But the network's behaviour is not to be explained by its embodying explicit or tacit knowledge of the information laid out in the competence theory. A rogue model, by contrast, posits either two systems, or two aspects of a single system, such that one system does indeed embody the knowledge laid out in the competence theory. (shrink)