Symbols and Symbol Systems

It is easy to give a list of cognitive processes. They are things like learning, memory, concept formation, reasoning, maybe emotion, and so on. It is not easy to say, of these things that are called cognitive, what makes them so? Knowing the answer is one very important reason to be interested in the mark of the cognitive. In this paper, consider some answers that we think do not work and then offer one of our own which ties cognition to (...) actions explained via the having of reasons. (shrink)

Response to "Embodied artificial intelligence", a commentary by Ron Chrisley.

Among such social-philosophic notions as society, culture, civilization, system, human, sense, sign, truth and others, concept “symbol” takes a special place. Most of the researchers meet the view, that symbol possesses an important place in the development of culture as a social phenomenon. The role of symbol in cultures birth and development is characterized by antipathy and polysemy. However revelation of the symbol role in spiritual processes of cultural transitions is beyond question one of the urgent philosophic issues. Symbol is (...) a form of access to over-sensible culture’s substance, it functions as a money-box and hearth of culture senses, which are included by the systems of signs, images, metaphors into the circulation of world and human relations. (shrink)

Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive (...) science, shifting from relatively minor status to increasing importance. Nevertheless, researchers wonder whether grounded mechanisms lie at the heart of the cognitive system or are peripheral to classic symbolic mechanisms. Although grounded cognition is currently dominated by demonstration experiments in the absence of well-developed theories, the area is likely to become increasingly theory driven over the next 30 years. Another likely development is the increased incorporation of grounding mechanisms into cognitive architectures and into accounts of classic cognitive phenomena. As this incorporation occurs, much functionality of these architectures and phenomena is likely to remain, along with many original mechanisms. Future theories of grounded cognition are likely to be heavily influenced by both cognitive neuroscience and social neuroscience, and also by developmental science and robotics. Aspects from the three major perspectives in cognitive science—classic symbolic architectures, statistical/dynamical systems, and grounded cognition—will probably be integrated increasingly in future theories, each capturing indispensable aspects of intelligence. (shrink)

Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated action, and development. Particular attention is given to abstract concepts, language, and computational mechanisms.

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

The notion of a ‘ symbol ’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘ symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that (...) give rise to disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘ symbol ’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

Dans un texte désormais célèbre, Ferdinand de Saussure insiste sur l’arbitraire du signe dont il vante les qualités. Toutefois il s’avère que le symbole, signe non arbitraire, dans la mesure où il existe un rapport entre ce qui représente et ce qui est représenté, joue un rôle fondamental dans la plupart des activités humaines, qu’elles soient scientifiques, artistiques ou religieuses. C’est cette dimension symbolique, sa portée, son fonctionnement et sa signification dans des domaines aussi variés que la chimie, la théologie, (...) les mathématiques, le code de la route et bien d’autres qui est l’objet du livre La Pointure du symbole. -/- Jean-Yves Béziau, franco-suisse, est docteur en logique mathématique et docteur en philosophie. Il a poursuivi des recherches en France, au Brésil, en Suisse, aux États-Unis (UCLA et Stanford), en Pologne et développé la logique universelle. Éditeur-en-chef de la revue Logica Universalis et de la collection Studies in Universal Logic (Springer), il est actuellement professeur à l’Université Fédérale de Rio de Janeiro et membre de l’Académie brésilienne de Philosophie. SOMMAIRE -/- PRÉFACE L’arbitraire du signe face à la puissance du symbole Jean-Yves BÉZIAU La logique et la théorie de la notation (sémiotique) de Peirce (Traduit de l’anglais par Jean-Marie Chevalier) Irving H. ANELLIS Langage symbolique de Genèse 2-3 Lytta BASSET -/- Mécanique quantique : quelle réalité derrière les symboles ? Hans BECK -/- Quels langages et images pour représenter le corps humain ? Sarah CARVALLO Des jeux symboliques aux rituels collectifs. Quelques apports de la psychologie du développement à l’étude du symbolisme Fabrice CLÉMENT Les panneaux de signalisation (Traduit de l’anglais par Fabien Shang) Robert DEWAR Remarques sur l’émergence des activités symboliques Jean LASSÈGUE Les illustrations du "Songe de Poliphile" (1499). Notule sur les hiéroglyphes de Francesca Colonna Pierre-Alain MARIAUX Signes de vie Jeremy NARBY Visualising relations in society and economics. Otto Neuraths Isotype-method against the background of his economic thought Elisabeth NEMETH Algèbre et logique symboliques : arbitraire du signe et langage formel Marie-José DURAND – Amirouche MOKTEFI Les symboles mathématiques, signes du Ciel Jean-Claude PONT La mathématique : un langage mathématique ? Alain M. ROBERT. (shrink)

The substitution of knowledge to information as the entity that organizations process and deliver raises a number of questions concerning the nature of knowledge. The dispute on the codifiability of tacit knowledge and that juxtaposing the epistemology of practice vs. the epistemology of possession can be better faced by revisiting two crucial debates. One concerns the nature of cognition and the other the famous mind-body problem. Cognition can be associated with the capability of manipulating symbols, like in the traditional computational (...) view of organizations, interpreting facts or symbols, like in the narrative approach to organization theory, or developing mental states (events), like argued by the growing field of organizational cognition. Applied to the study of organizations, the mind-body problem concerns the possibility (if any) and the forms in which organizational mental events, like trust, identity, cultures, etc., can be derived from the structural aspects (technological, cognitive or communication networks) of organizations. By siding in extreme opposite positions, the two epistemologies appear irreducible one another and pay its own inner consistency with remarkable difficulties in describing and explaining some empirical phenomena. Conversely, by legitimating the existence of both tacit and explicit knowledge, by emphasizing the space of human interactions, and by assuming that mental events can be explained with the structural aspects of organizations, Nonaka's SECI model seems an interesting middle way between the two rival epistemologies. (shrink)

In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s original statement and his (...) notes taken at a lecture by Jeff erson, are edited and commented for the fi rst time. Th e originals are in the Regenstein Library of the University of Chicago. Th e introduction highlights elements of the debate that included neurophysiology, mathematics, the mind-body-machine problem, and consciousness and shows that Turing’s approach, as documented here, does not lend itself to reductionism. (shrink)

While I agree in general with Stevan Harnad's symbol grounding proposal, I do not believe "transduction" (or "analog process") PER SE is useful in distinguishing between what might best be described as different "degrees" of grounding and, hence, for determining whether a particular system might be capable of cognition. By 'degrees of grounding' I mean whether the effects of grounding go "all the way through" or not. Why is transduction limited in this regard? Because transduction is a physical process which (...) does not speak to the issue of representation, and, therefore, does not explain HOW the informational aspects of signals impinging on sensory surfaces become embodied as symbols or HOW those symbols subsequently cause behavior, both of which, I believe, are important to grounding and to a system's cognitive capacity. Immunity to Searle's Chinese Room (CR) argument does not ensure that a particular system is cognitive, and whether or not a particular degree of groundedness enables a system to pass the Total Turing Test (TTT) may never be determined. (shrink)

Stevan Harnad and I seem to be thinking about many of the same issues. Sometimes we agree, sometimes we don't; but I always find his reasoning refreshing, his positions sensible, and the problems with which he's concerned to be of central importance to cognitive science. His "Grounding Symbols in the Analog World with Neural Nets" (= GS) is no exception. And GS not only exemplifies Harnad's virtues, it also provides a springboard for diving into Harnad- Bringsjord terrain.

Noam Chomsky and Frances Egan argue that David Marr’s computational theory of vision is not intentional, claiming that the formal scientific theory does not include description of visual content. They also argue that the theory is internalist in the sense of not describing things physically external to the perceiver. They argue that these claims hold for computational theories of vision in general. Beyond theories of vision, they argue that representational content does not figure as a topic within formal computational theories (...) in cognitive science. I demonstrate that Chomsky’s and Egan’s claims about Marr’s theory are false. Marr’s computational theory contains a mathematical theory of visual content, based on empirical psychophysical evidence. It also contains mathematical descriptions of distal physical surfaces and objects, and of their optic projection to the perceiver. Much computational research on vision contains these types of intentional and externalist components within the formal, mathematical, theories. Chomsky’s and Egan’s claims demonstrate inadequate study and understanding of Marr’s work and other research in this area. Computational theories of vision, by containing empirically based mathematical theories of visual content, to this extent present naturalizations of semantics. (shrink)

ABSENCE is the claim that, if a symbol appears on a map, then absence of the symbol from some map coordinate signifies absence of the corresponding property from the corresponding location. This claim is highly intuitive and widely endorsed. And if it is true, then cartographic representation is strikingly different from linguistic representation. I argue, however, that ABSENCE is false of various maps and that we have no reason to believe it is true of any maps. The intuition to the (...) contrary results from mistaking what a map simply conveys for what it literally represents. (shrink)

Upshot: Written by recognized experts in their fields, the book is a set of essays that deals with the influences of early cybernetics, computational theory, artificial intelligence, and connectionist networks on the historical development of computational-representational theories of cognition. In this review, I question the relevance of computability arguments and Jonasian phenomenology, which has been extensively invoked in recent discussions of autopoiesis and Ashby’s homeostats. Although the book deals only indirectly with constructivist approaches to cognition, it is useful reading for (...) those interested in machine-based models of mind. (shrink)

Kripke (Wittgenstein on rules and private language: an elementary exposition. Harvard University Press, Cambridge Mass, 1982 ) rejected a naturalistic dispositional account of meaning (hereafter semantic dispositionalism) in a skeptical argument about rule-following he attributes to Wittgenstein (Philosophical investigation. Basil Blackwell, Oxford, 1958 ). Most philosophers who oppose Kripke’s criticisms of semantic dispositionalism take the stance that the argument proves too much: semantic dispositionalism is similar to much of our respected science in some important aspects, and hence to discard the (...) former would mean to give up the latter, which is obviously wrong. In this paper, I shall discuss and reject a recent defense of Kripke by Kusch (Analysis 65(2):156–163 2005 ; Sceptical guide to meaning and rules: defending Kripke’s Wittgenstein. McGill-Queen’s, London, 2006 ). Kusch attempts to show that semantic dispositionalism differs from the sciences, and consequently, Kripke’s attack can only target semantic dispositionalism, but not the sciences. Specifically, Kusch identifies some important features of the sciences with regard to how it employs idealization and ceteris paribus clauses, and argues that the ways in which semantic dispositionalism uses them are dramatically different. I argue that, upon close examination, the two are more similar than otherwise in each of those features. (shrink)

Kant on how certain experiences might give us considerations counting in favor of the real possibility of certain things. -/- .

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated structures along (...) an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term. (shrink)

Two very different insights motivate characterizing the brain as a computer. One depends on mathematical theory that defines computability in a highly abstract sense. Here the foundational idea is that of a Turing machine. Not an actual machine, the Turing machine is really a conceptual way of making the point that any well-defined function could be executed, step by step, according to simple 'if-you-are-in-state-P-and-have-input-Q-then-do-R' rules, given enough time (maybe infinite time) [see COMPUTATION]. Insofar as the brain is a device whose (...) input and output can be characterized in terms of some mathematical function -- however complicated -- then in that very abstract sense, it can be mimicked by a Turning machine. Given what is known so far brains do seem to depend on cause-effect operations, and hence brains appear to be, in some formal sense, equivalent to a Turing machine [see CHURCH-TURING THESIS]. On its own, however, this reveals nothing at all of how the mind-brain actually works. The second insight depends on looking at the brain as a biological device that processes information from the environment to build complex representations that enable the brain to make predictions and select advantageous behaviors. Where necessary to avoid ambiguity, we will refer to the first notion of computation as algorithmic computation, and the second as information processing computation. (shrink)

What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing views, is that the way in which these material, conventional symbol-structures do their work is by being translated into some kind of content-matching inner code. One alternative to this view is the tempting but thoroughly elusive idea that we somehow think in some natural language (such as English). (...) In the present treatment I explore a third option, which I shall call the "complementarity" view of language. According to this third view the actual symbol structures of a given language add cognitive value by complementing (without being replicated by) the more basic modes of operation and representation endemic to the biological brain. The "cognitive bonus" that language brings is, on this model, not to be cashed out either via the ultimately mysterious notion of "thinking in a given natural language" or via some process of exhaustive translation into another inner code. Instead, we should try to think in terms of a kind of coordination dynamics in which the forms and structures of a language qua material symbol system play a key and irreducible role. Understanding language as a complementary cognitive resource is, I argue, an important part of the much larger project (sometimes glossed in terms of the "extended mind") of understanding human cognition as essentially and multiply hybrid: as involving a complex interplay between internal biological resources and external non-biological resources. (shrink)

Robert Cummins presents a series of essays motivated by the following question: Is the mind a collection of beliefs and desires that respond to and condition our feeling and perceptual experiences, or is this just a natural way to talk about it? What sort of conceptual framework do we need to understand what is really going on in our brains?

"This is an important new Cummins work.

We again press the case for computationalism by considering the latest in ill- conceived attacks on this foundational idea. We briefly but clearly define and delimit computationalism and then consider three authors from a new anti- computationalist collection.

Mostly philosophers cause trouble. I know because on alternate Thursdays I am one -- and I live in a philosophy department where I watch all of them cause trouble. Everyone in artificial intelligence knows how much trouble philosophers can cause (and in particular, we know how much trouble one philosopher -- John Searle -- has caused). And, we know where they tend to cause it: in knowledge representation and the semantics of data structures. This essay is about a recent case (...) of this sort of thing. One of the take-home messages will be that AI ought to redouble its efforts t o understand concepts. (shrink)

Instead of using low-level neurophysiology mimicking and exploratory programming methods commonly used in the machine consciousness field, the hierarchical Operational Architectonics (OA) framework of brain and mind functioning proposes an alternative conceptual-theoretical framework as a new direction in the area of model-driven machine (robot) consciousness engineering. The unified brain-mind theoretical OA model explicitly captures (though in an informal way) the basic essence of brain functional architecture, which indeed constitutes a theory of consciousness. The OA describes the neurophysiological basis of the (...) phenomenal level of brain organization. In this context the problem of producing man-made “machine” consciousness and “artificial” thought is a matter of duplicating all levels of the operational architectonics hierarchy (with its inherent rules and mechanisms) found in the brain electromagnetic field. We hope that the conceptual-theoretical framework described in this paper will stimulate the interest of mathematicians and/or computer scientists to abstract and formalize principles of hierarchy of brain operations which are the building blocks for phenomenal consciousness and thought. (shrink)

The author argues that our best hope for addressing world problems is to teach critical thinking through the study of language and all symbol systems. This means removing disciplinary boundaries so that we can focus more effectively on solving common problems. Human survival also depends upon our critical analysis of electronic media and our wise uses of technology. Critical thinking via all symbol systems is more likely to generate humane actions. Therefore, education—not governments or armies or institutions or private corporations—is (...) our best hope. (shrink)

Concept representation is still an open problem in the field of ontology engineering and, more generally, of knowledge representation. In particular, the issue of representing “non classical” concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions, remains unresolved. In this paper we review empirical evidence from cognitive psychology, according to which concept representation is not a unitary phenomenon. On this basis, we sketch some proposals for concept representation, taking into account suggestions from psychological research. In (...) particular, it seems that human beings employ both prototype-based and exemplar-based representations in order to represent non classical concepts. We suggest that a similar, hybrid prototype-exemplar based approach could also prove useful in the field of knowledge representation technology. Finally, we propose conceptual spaces as a suitable framework for developing some aspects of this proposal. (shrink)

The problem of concept representation is relevant for many sub-fields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs (...) is that the notion of a concept is, to some extent, heterogeneous, and encompasses different cognitive phenomena. This results in a strain between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In some ways artificial intelligence research shows traces of this situation. In this paper, we propose an analysis of this current state of affairs. Since it is our opinion that a mature methodology with which to approach knowledge representation and knowledge engineering should also take advantage of the empirical results of cognitive psychology concerning human abilities, we outline some proposals for concept representation in formal ontologies, which take into account suggestions from psychological research. Our basic assumption is that knowledge representation systems whose design takes into account evidence from experimental psychology may therefore give better results in many applications. (shrink)

Symbolism is central to the work of Rene Guenon although he never fully explains the roles of symbols in his thought, nor his own interpretive processes. The aim of this study is to clarify those issues. The discussion is preceded by a brief introduction to published responses to Guenon and a biographical sketch . Guenon's symbolic studies can be understood only in the context of his fundamental ideas. That context is provided in Chapter Three. Chapter Four shows that: a symbol (...) is that which, by its own nature, expresses higher levels of reality than its own in the universal hierarchy. Since all symbols are related to the Absolute Principle by virtue of that hierarchy, the Laws of Correspondence and Analogy are two governing principles of interpretation. Symbols function to reveal the inexpressible, to veil the sacred, and to prepare the initiate for realization of higher levels of being. Symbols are multivalent, coherent and never self-contradictory; their meanings may be lawfully superposed and/or transposed between hierarchical orders. Interpretations must include examination of the symbols' viewpoint, their double significances, and awareness of the order to which they belong. Since symbols are real "things" in their own orders, they never lose their intrinsic meaning. Guenon interprets a symbol by examination of its place and function in widely differing cultural and religious traditions, by searching out analogies and correspondences, by comparing symbolic forms, by relating a symbol to its contexts and to universal cycles, by etymologies and the traditional sciences of letters and numbers, and by noticing to which sets of symbols a given one belongs. These processes are not systematic rules, but result in coherent understanding. Chapter Five shows how Guenon uses symbols to express, to explain and to substantiate his basic ideas which are also the context for their interpretation, a process that is deductive and circular. Throughout this study, brief comparisons are made with the ideas of Mircea Eliade and Carl Jung. (shrink)

According to "computationalism" (Newell, 1980; Pylyshyn 1984; Dietrich 1990), mental states are computational states, so if one wishes to build a mind, one is actually looking for the right program to run on a digital computer. A computer program is a semantically interpretable formal symbol system consisting of rules for manipulating symbols on the basis of their shapes, which are arbitrary in relation to what they can be systematically interpreted as meaning. According to computationalism, every physical implementation of the right (...) symbol system will have mental states. (shrink)

There are many entry points into the problem of categorization. Two particularly important ones are the so-called top-down and bottom-up approaches. Top-down approaches such as artificial intelligence begin with the symbolic names and descriptions for some categories already given; computer programs are written to manipulate the symbols. Cognitive modeling involves the further assumption that such symbol-interactions resemble the way our brains do categorization. An explicit expectation of the top-down approach is that it will eventually join with the bottom-up approach, which (...) tries to model how the hardware of the brain works: sensory systems, motor systems and neural activity in general. The assumption is that the symbolic cognitive functions will be implemented in brain function and linked to the sense organs and the organs of movement in roughly the way a program is implemented in a computer, with its links to peripheral devices such as transducers and effectors. (shrink)