This study addresses the dynamical nature of a “representation‐hungry” cognitive task involving an imagined action. In our experiment, participants were handed rods that systematically increased or decreased in length on subsequent trials. Participants were asked to judge whether or not they thought they could reach for a distant object with the hand‐held rod. The results are in agreement with a dynamical model, extended from Tuller, Case, Ding, and Kelso (1994). The dynamical effects observed in this study suggest that predictive judgments (...) regarding the possibility or impossibility of a certain action can be understood in terms of dynamically evolving basins of attraction instead of as depending on representational structures. (shrink)

The proponents of embodied cognition often try to present their research program as the next step in the evolution of standard cognitive science. The domain of standard cognitive science is fairly clearly circumscribed (perception, memory, attention, language, problem solving, learning). Its ontological commitments, that is, its commitments to various theoretical entities, are overt: cognition involves algorithmic processes upon symbolic representations. As a research program, embodied cognition exhibits much greater latitude in subject matter, ontological commitment, and methodology than does standard cognitive (...) science. The proponents of embodied cognition to explain the aspects of human cognition are using the importance of embodied interaction with the environment, which is a dynamic relation. The cause of disagreement between these two approaches is regarding the role assumed by the notion of representation. The discussion about the contrast between embodied cognition and standard cognitive science is incomplete without Gibson’s ecological theory of perception and connectionist account of cognition. I will briefly contrast these important theories with computational view of cognition, highlighting the debate over role of representation. Embodied cognition has incorporated rather extensively a variety of insights emerging from research both in ecological psychology and in connectionism. The way I have followed to contrast embodied cognition with standard cognitive science, involves concentration on those several themes that appear to be prominent in the body of work that is often seen as illustrative of embodied cognition. This strategy has the advantage of postponing hard questions about “the” subject matter, ontological commitments, and methods of embodied cognition until more is understood about the particular interests and goals that embodied cognition theorists often pursue. This approach might show embodied cognition to be poorly unified, suggesting that the embodied cognition label should be abandoned in favor of several labels that reflect more accurately the distinct projects that have been clumped together under a single title. Alternatively, it might show that, in fact, there are some overarching commitments that bring tighter unity to the various bodies of work within embodied cognition that seem thematically only loosely related. The contrast between these two approaches is highlighted not only the basis of a priori argument but major experiments have been mentioned, to show the weight of the assumptions of both the contrasting approaches. (shrink)

The proponents of embodied cognition often try to present their research program as the next step in the evolution of standard cognitive science. The domain of standard cognitive science is fairly clearly circumscribed (perception, memory, attention, language, problem solving, learning). Its ontological commitments, that is, its commitments to various theoretical entities, are overt: cognition involves algorithmic processes upon symbolic representations. As a research program, embodied cognition exhibits much greater latitude in subject matter, ontological commitment, and methodology than does standard cognitive (...) science. The proponents of embodied cognition to explain the aspects of human cognition are using the importance of embodied interaction with the environment, which is a dynamic relation. The cause of disagreement between these two approaches is regarding the role assumed by the notion of representation. The discussion about the contrast between embodied cognition and standard cognitive science is incomplete without Gibson’s ecological theory of perception and connectionist account of cognition. I will briefly contrast these important theories with computational view of cognition, highlighting the debate over role of representation. Embodied cognition has incorporated rather extensively a variety of insights emerging from research both in ecological psychology and in connectionism. The way I have followed to contrast embodied cognition with standard cognitive science, involves concentration on those several themes that appear to be prominent in the body of work that is often seen as illustrative of embodied cognition. This strategy has the advantage of postponing hard questions about “the” subject matter, ontological commitments, and methods of embodied cognition until more is understood about the particular interests and goals that embodied cognition theorists often pursue. This approach might show embodied cognition to be poorly unified, suggesting that the embodied cognition label should be abandoned in favor of several labels that reflect more accurately the distinct projects that have been clumped together under a single title. Alternatively, it might show that, in fact, there are some overarching commitments that bring tighter unity to the various bodies of work within embodied cognition that seem thematically only loosely related. The contrast between these two approaches is highlighted not only the basis of a priori argument but major experiments have been mentioned, to show the weight of the assumptions of both the contrasting approaches. (shrink)

This study addresses the dynamical nature of a “representation‐hungry” cognitive task involving an imagined action. In our experiment, participants were handed rods that systematically increased or decreased in length on subsequent trials. Participants were asked to judge whether or not they thought they could reach for a distant object with the hand‐held rod. The results are in agreement with a dynamical model, extended from Tuller, Case, Ding, and Kelso (1994). The dynamical effects observed in this study suggest that predictive judgments (...) regarding the possibility or impossibility of a certain action can be understood in terms of dynamically evolving basins of attraction instead of as depending on representational structures. (shrink)

Computationalist theories of mind require brain symbols, that is, neural events that represent kinds or instances of kinds. Standard models of computation require multiple inscriptions of symbols with the same representational content. The satisfaction of two conditions makes it easy to see how this requirement is met in computers, but we have no reason to think that these conditions are satisfied in the brain. Thus, if we wish to give computationalist explanations of human cognition, without committing ourselvesa priori to a (...) strong and unsupported claim in neuroscience, we must first either explain how we can provide multiple brain symbols with the same content, or explain how we can abandon standard models of computation. It is argued that both of these alternatives require us to explain the execution of complex tasks that have a cognition-like structure. Circularity or regress are thus threatened, unless noncomputationalist principles can provide the required explanations. But in the latter case, we do not know that noncomputationalist principles might not bear most of the weight of explaining cognition. Four possible types of computationalist theory are discussed; none appears to provide a promising solution to the problem. Thus, despite known difficulties in noncomputationalist investigations, we have every reason to pursue the search for noncomputationalist principles in cognitive theory. (shrink)

This article surveys the philosophical literature on theoretical linguistics. The focus of the paper is centred around the major debates in the philosophy of linguistics, past and present, with specific relation to how they connect to the philosophy of science. Specific issues such as scientific realism in linguistics, the scientific status of grammars, the methodological underpinnings of formal semantics, and the integration of linguistics into the larger cognitive sciences form the crux of the discussion.

In this article, I describe a novel position on the semantics of artificial intelligence. I present a problem for the current artificial neural networks used in machine learning, specifically with relation to natural language tasks. I then propose that from a metasemantic level, meaning in machines can best be interpreted as radically contextualist. Finally, I consider what this might mean for human‐level semantic competence from a comparative perspective.

Dead reckoning is a feature of the navigation behaviour shown by several creatures, including the desert ant. Recent work by C. Randy Gallistel shows that some connectionist models of dead reckoning face important challenges. These challenges are thought to arise from essential features of the connectionist approach, and have therefore been taken to show that connectionist models are unable to explain even the most primitive of psychological phenomena. I show that Gallistel’s challenges are successfully met by one recent connectionist model, (...) proposed by Ulysses Bernardet, Sergi Bermúdez i Badia, and Paul F.M.J. Verschure. The success of this model suggests that there are ways to implement dead reckoning with neural circuits that fall within the bounds of what many people regard as neurobiologically plausible, and so that the wholesale dismissal of the connectionist modelling project remains premature. (shrink)

The rapid development of connectionist models in computer science and of powerful computational tools in neuroscience has encouraged eliminativist materialist philosophers to propose specific alternatives to traditional mentalistic theories of mind. One of the problems associated with such a move is that elimination of the mental would seem to remove access to ideas like truth as the foundations of normative epistemology. Thus, a successful elimination of propositional or sentential theories of mind must not only replace them for purposes of our (...) psychology, it must also replace them for purposes of the evaluation of our theories and explanations, psychological and otherwise. This paper briefly reviews eliminativist arguments for doubting the correctness of sentential accounts of explanation, understanding, and normative evaluation. It then considers Paul Churchland's (1989) proposed alternative norms, which are framed neurocomputationally. The alternative is found wanting in several specific ways. The arguments for eliminating propositionally-based norms are then re-examined and it is suggested that the need for wholesale elimination is overstated. A clear gap in the traditional epistemological story is identified, however, and a more modest set of norms is proposed as a way of filling this gap, rather than as a way of entirely replacing the traditional framework. (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)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question 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. (shrink)

This paper deals with the problem of understanding semiosis and meaning in cognitive systems. To this aim we argue for a unified two-factor account according to which both external and internal information are non-independent aspects of meaning, thus contributing as a whole in determining its nature. To overcome the difficulties stemming from this approach we put forward a theoretical scheme based on the definition of a suitable representation space endowed with a set of transformations, and we show how it can (...) be implemented, in the case of a single agent, by a neural network architecture. Numerical experiments conducted on different instances of the latter show that similar representations are developed as a consequence of the fact that these instances are facing a similar semantic task. This allows to model social and environmental influences through a system of interacting agents, each described by a specific implementation of this model architecture. (shrink)

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)

Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it (...) offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest. (shrink)

The notion of a "mental representation" is, arguably, in the first instance a theoretical construct of cognitive science. As such, it is a basic concept of the Computational Theory of Mind, according to which cognitive states and processes are constituted by the occurrence, transformation and storage (in the mind/brain) of information-bearing structures (representations) of one kind or another.

The standard approach to model how human beings understand natural languages is the symbolic, compositional approach according to which the meaning of a complex expression is a function of the meanings of its constituents. In other words, meaning plays a fundamental role in the model. In this work, because of the polysemous, flexible, dynamic, and contextual structure of natural languages, this approach is rejected. Instead, a connectionist model which eliminates the concept of meaning is proposed.

English: In this paper I indicate why I consider 'experimental philosophy' to be good news, though not as good as it could be, and not as all that new. I'll argue that there is no need to restrict experimental philosophy to eliciting intuitions through questionnaires. I'll indicate that good examples of experimental philosophy already exist in Artificial Intelligence and Cognitive Neuroscience. Dutch: Hieronder wil ik proberen aan te geven waarom ik de experimentele filosofie weliswaar als goed nieuws beschouw, maar niet (...) half zo goed als zou kunnen, en ook niet als echt nieuw. Qua filosofische experimenten beperkt de XF-beweging zich vooralsnog onnodig door haar focus op het eliciteren van intuïties, terwijl er reeds lang diverse goede voorbeelden van XF bestaan op het gebied van de kunstmatige intelligentie en de cognitieve neurowetenschap. (shrink)