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)

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two (...) types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance. (shrink)

O artigo propõe um balanço atualizado da abordagem computacional da mente, minudenciando aspectos conceituais e críticos. O balanço é pautado por três afirmações ‒ α) A mente humana é um sistema computacional; β) A mente humana pode ser descrita como um sistema computacional; γ) Sistemas computacionais precisam de conteúdo representacional ‒, a partir das quais mostro que o computacionalismo clássico se articula em termos de α∧γ e que as vertentes contemporâneas são melhor caracterizadas em termos de α∧~γ ou β∧~γ. Por (...) fim, após analisar uma série de objeções, argumentamos que o computacionalismo do século XXI é um programa de pesquisa filosoficamente relevante e que os críticos da abordagem computacional da mente incorrem em anacronismo quando se limitam a criticar as vertentes clássicas. (shrink)

The problem of representing the spatial structure of images, which arises in visual object processing, is commonly described using terminology borrowed from propositional theories of cognition, notably, the concept of compositionality. The classical propositional stance mandates representations composed of symbols, which stand for atomic or composite entities and enter into arbitrarily nested relationships. We argue that the main desiderata of a representational system—productivity and systematicity—can (indeed, for a number of reasons, should) be achieved without recourse to the classical, proposition‐like compositionality. (...) We show how this can be done, by describing a systematic and productive model of the representation of visual structure, which relies on static rather than dynamic binding and uses coarsely coded rather than atomic shape primitives. (shrink)

In their provocative 1988 paper, Fodor and Pylyshyn issued a formidable challenge to connectionists, i.e. to provide a non‐classical explanation of the empirical phenomenon of systematicity in cognitive agents. Since the appearance of F&P's challenge, a number of connectionist systems have emerged which prima facie meet this challenge. However, Fodor and McLaughlin (1990) advance an argument, based upon a general principle of nomological necessity, to show that one of these systems (Smolensky's) could not satisfy the Fodor‐Pylyshyn challenge. Yet, if Fodor (...) and McLaughlin's analysis is correct, it is doubtful whether any existing connectionist system would fare better than Smolensky's. In the view of Fodor and McLaughlin, humans and classical architectures display systematicity as a matter of nomological necessity (necessity by virtue of natural law), but connectionist architectures do not. However, I argue that the Fodor‐Pylyshyn‐McLaughlin appeal to nomological necessity is untenable. There is a sense in which neither classical nor connectionist architectures possess nomological (or‘nomic’) necessity. However, the sense in which classical architectures do possess nomic necessity applies equally well to at least some connectionist architectures. Representational constituents can have causal efficacy within both classical and connectionist architectures. (shrink)

The emphasis in the connectionist sentence‐processing literature on distributed representation and emergence of grammar from such systems can easily obscure the often close relations between connectionist and symbolist systems. This paper argues that the Simple Recurrent Network (SRN) models proposed by Jordan (1989) and Elman (1990) are more directly related to stochastic Part‐of‐Speech (POS) Taggers than to parsers or grammars as such, while auto‐associative memory models of the kind pioneered by Longuet–Higgins, Willshaw, Pollack and others may be useful for grammar (...) induction from a network‐based conceptual structure as well as for structure‐building. These observations suggest some interesting new directions for specifically connectionist sentence processing research, including more efficient representations for finite state machines, and acquisition devices based on a distinctively connectionist basis for grounded symbolist conceptual structure. (shrink)

The emphasis in the connectionist sentence‐processing literature on distributed representation and emergence of grammar from such systems can easily obscure the often close relations between connectionist and symbolist systems. This paper argues that the Simple Recurrent Network (SRN) models proposed by Jordan (1989) and Elman (1990) are more directly related to stochastic Part‐of‐Speech (POS) Taggers than to parsers or grammars as such, while auto‐associative memory models of the kind pioneered by Longuet–Higgins, Willshaw, Pollack and others may be useful for grammar (...) induction from a network‐based conceptual structure as well as for structure‐building. These observations suggest some interesting new directions for specifically connectionist sentence processing research, including more efficient representations for finite state machines, and acquisition devices based on a distinctively connectionist basis for grounded symbolist conceptual structure. (shrink)

Are minds really dynamical or are they really symbolic? Because minds are bundles of computations, and because computation is always a matter of interpretation of one system by another, minds are necessarily symbolic. Because minds, along with everything else in the universe, are physical, and insofar as the laws of physics are dynamical, minds are necessarily dynamical systems. Thus, the short answer to the opening question is “yes.” It makes sense to ask further whether some of the computations that constitute (...) a human mind are constrained by functional, algorithmic, or implementational factors to be essentially of the discrete symbolic variety (even if they supervene on an apparently continuous dynamical substrate). I suggest that here too the answer is “yes” and discuss the need for such discrete, symbolic cognitive computations in communication-related tasks. (shrink)