Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the symbol grounding problem : How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their shapes, be (...) grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: iconic representations, which are analogs of the proximal sensory projections of distal objects and events, and categorical representations, which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their categorical representations. Higher-order symbolic representations, grounded in these elementary symbols, consist of symbol strings describing category membership relations. Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic module, however; the symbolic functions would emerge as an intrinsically dedicated symbol system as a consequence of the bottom-up grounding of categories ' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded. (shrink)

The concept of representation has become almost inextricably bound to the concept of symbol systems. the concepts is nowhere more prevalent than in descriptions of "internal representations." These representations are thought to occur in an internal symbol system that allows the brain to store and use information. In this paper we explore a different approach to understanding psychological processes, one that retains a commitment to representations and computations but that is not based on the idea that information must be stored (...) and manipulated in symbol systems. In particular, we suggest that the notion of a symbol system as currently understood construes psychological processes in terms of a specific type of computational system, in which a control function "reads," "interprets," and manipulates discrete entities called "symbols." We argue that other types of computational systems may provide a more appropriate characterization of psychological processes. One implication of our argument is the need to consider the constraints placed on computational theories in psychology by the nature of the computing device itself, the human brain. Perhaps surprisingly, this implication leads us to the conclusion that a "functionalist" conception of psychological processes (discussed below) does not entail that physiology is irrelevant to psychology, as has been maintained by prominent adherents of the symbol-systems approach. (shrink)

Exactly how do the sign/symbol/token systems of endo- and exo-biosemiosis differ from those of cognitive semiosis? Do the biological messages that integrate metabolism have conceptual meaning? Semantic information has two subsets: Descriptive and Prescriptive. Prescriptive information instructs or directly produces nontrivial function. In cognitive semiosis, prescriptive information requires anticipation and “choice with intent” at bona fide decision nodes. Prescriptive information either tells us what choices to make, or it is a recordation of wise choices already made. Symbol systems allow recordation (...) of deliberate choices and the transmission of linear digital prescriptive information. Formal symbol selection can be instantiated into physicality using physical symbol vehicles (tokens). Material symbol systems (MSS) formally assign representational meaning to physical objects. Even verbal semiosis instantiates meaning into physical sound waves using an MSS. Formal function can also be incorporated into physicality through the use of dynamically-inert (dynamically-incoherent or -decoupled) configurable switch-settings in conceptual circuits. This paper examines the degree to which biosemiosis conforms to the essential formal criteria of prescriptive semiosis and cybernetic management. (shrink)

This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition that, in (...) order to secure successful representation in science, collective representational resources must be available. Such resources may take the form of (amongst others) mathematical formalisms, diagrammatic methods, notational rules, or—in the case of material models—conventions regarding the use and manipulation of the constituent parts. More often than not, an abstract characterization of such resources tells only half the story, as they are constituted equally by the pattern of (practical and theoretical) activities—such as instances of manipulation or inference—of the researchers who deploy them. In other words, representational resources need to be sustained by a social practice; this is what renders them collective representational resources in the first place. (shrink)

Cognitive, or knowledge, agents, who are in some way aware of describing their own view of the world (based on their mental concepts), need to become concerned with the expressions of their own conceptions. My main supposition is that agents’ conceptions are mainly expressed in the form of linguistic expressions that are spoken, written, and represented based on e.g. letters, numbers, or symbols. This research especially focuses on symbolic conceptions (that are agents’ conceptions that are manifested in the form (...) of their symbols). I attempt to logically (and using ????LSYM) analyse symbolic conceptions in terminological systems. ????LSYM is an assertional fragment of my developed Conception Language (????L), which is utilised as a formal-logical system for representing and explicating agents’ conceptions of the world. Keywords: assertional logic; cognitive agent; knowledge agent; conception language; conception; symbol; symbolic conception. (shrink)

It has been suggested that iconic graphical signs evolve into symbolic graphical signs through repeated usage. This article reports a series of interactive graphical communication experiments using a ‘pictionary’ task to establish the conditions under which the evolution might occur. Experiment 1 rules out a simple repetition based account in favor of an account that requires feedback and interaction between communicators. Experiment 2 shows how the degree of interaction affects the evolution of signs according to a process of grounding. (...) Experiment 3 confirms the prediction that those not involved directly in the interaction have trouble interpreting the graphical signs produced in Experiment 1. On the basis of these results, this article argues that icons evolve into symbols as a consequence of the systematic shift in the locus of information from the sign to the users' memory of the sign's usage supported by an interactive grounding process. (shrink)

People often solve spatially presented cognitive problems more easily than their nonspatial counterparts. We explain this phenomenon by characterizing space as an inter-modality that provides common structure to different specific perceptual modalities. The usefulness of spatial structure for knowledge processing on different levels of granularity and for interaction between internal and external processes is described. Map representations are discussed as examples in which the usefulness of spatially organized symbols is particularly evident. External representations and processes can enhance internal representations and (...) processes effectively when the same structures and principles can be implicitly assumed. (shrink)

Nelson Goodman’s theory of symbol systems expounded in his Languages of Art has been frequently criticized on many counts (cf. list of secondary literature in the entry “Goodman’s Aesthetics” of Stanford Encyclopedia of Philosophy and Sect. 3 below). Yet it exerts a strong influence and is treated as one of the major twentieth-century theories on the subject. While many of Goodman’s controversial theses are criticized, the technical notions he used to formulate them seem to have been treated as neutral tools. (...) One such technical notion is that of the density of symbol systems. This serves to distinguish linguistic symbols from pictorial representations (after Goodman entirely rejected resemblance in that role) and is a crucial part of Goodman’s explanation of what constitutes aesthetic experience (and so indirectly what is art). Thus its significance for Goodman’s theory is fundamental. The aim of this paper is a detailed, logical analysis of this notion. It turns out that Goodman’s definition is highly problematic and cannot be applied to symbol systems in the way Goodman envisaged. To conclude, Goodman’s theory is problematic not just because of its controversial theses but also because of logical problems with the technical notions used at its very core. Hence the controversial claims are not simply contestable, but inaccurately expressed. (shrink)

After reviewing the papers in this special issue, I must conclude that brains are not syntactic engines, but control systems that orient to biological, interindividual, and cultural norms. By themselves, syntactic constraints both underdetermine and overdetermine cognitive operations. So, rather than serving as the basis for general cognition, they are just another kind of empirically acquired constraint. In humans, symbols emerge from a particular sensorimotor activity through a process of contextual broadening that depends on the coordination of conscious and nonconscious (...) processing. This process provides the representational freedom and stability that constitute the human brain’s solution to the frame problem and symbol grounding problem. Symbol formation and grounding is an ongoing process of generalising constraints from particular contexts, selectively enlisting their use, and re-automating them. This process is central to the self-creation of a language-using person with beliefs, agency, and identity. (shrink)

There is disagreement over the notion of representation in cognitive science. Many investigators equate representations with symbols, that is, with syntactically defined elements in an internal symbol system. In recent years there have been two challenges to this orthodoxy. First, a number of philosophers, including many outside the symbolist orthodoxy, have argued that "representation" should be understood in its classical sense, as denoting a "stands for" relation between representation and represented. Second, there has been a growing challenge to orthodoxy under (...) the banner of connectionism. Although this connectionist challenge has evoked emotionally-charged rebukes from the symbolist camp, connectionists as a group have not articulated a conception of representation to replace the symbolist view. Nonetheless, most agree on the need for a nonsymbolic notion of representation. This paper advances a "stands for" sense of representation as primary by incorporating it into a general approach to cognitive science consonant with the connectionism. The idea is to marry connectionism to a particular version of functionalism, viz., one that builds its notion of "function" on the similarity between functional analysis in biology and in psychology. It builds on my earlier work adapting and revising Marr's tri-level approach to cognition. My proposal frees Marr's analysis from its moorings in the orthodox symbolic view of representation and allies it with a notion of functional analysis akin to that proposed by Cummins, developed further by Haugeland, and invoked by Millikan and Dretske. Unlike these authors, however, I do not wed representation to a general belief-desire analysis of behavior. Rather, I follow what I take to be the lesson of psychological practice, according to which the investigator does not seek to explain behavior in general but seeks to analyze the cognitive capacities that underlie behavior, such as vision, memory, learning, and linguistic capacities. Accordingly, ascriptions of representational content are made not by working back from belief-desire ascriptions, but in the context of forming psychological models to account for specific cognitive capacities. Because conceptions of representation in cognitive science typically are embedded in a general approach to the study of cognition, arguments for the comparative plausibility of a particular conception of representation must scout these larger frameworks. I therefore begin by characterizing the interlocking set of assumptions that gave life to the orthodox symbolist approach, paying special attention to the complementarity between representation and process that naturally arises from those assumptions. I then consider two versions of an alternative approach to representation: Dretske's and my own. Finally, I urge the merits of the "cognitive capacities" over the "belief-desire" approach to the subject-matter of cognitive science. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was (...) theoretically based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, (...) have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

Although Barsalou is right in identifying the importance of perceptual symbols as a means of carrying certain kinds of content, he is wrong in playing down the inferential resources available to amodal symbols. I argue that the case for perceptual symbol systems amounts to a false dichotomy and that it is feasible to help oneself to both kinds of content as extreme ends on a content continuum. The continuum thesis I advance argues for the inferential content at one end and (...) perceptual content at the other. In between the extremes, symbols might have aspects that are either perceptual or propositional-linguistic in character. I argue that this way of characterising the issue preserves the good sense of Barsalou's recognition of perceptual representations and yet avoids the tendency to minimise the gains won with symbolic representations vital to contemporary cognitive science. (shrink)

Barsalou's theory of perceptual symbol systems is considered from a metacognitive perspective. Two examples are discussed in terms of the proposed perceptual symbol theory. First, recent results in research on feeling-of-knowing judgement are used to argue for a representation of familiarity with input cues. This representation should support implicit memory. Second, the ability of maintaining a theory of other people's beliefs (theory of mind) is considered and it is suggested that a purely simulation-based view is insufficient to explain the available (...) evidence. Both examples characterize areas where Barsalou's theory would benefit from additional detail. (shrink)

Not long ago the standard view in cognitive science was that representations are symbols in an internal representational system or language of thought and that psychological processes are computations defined over such representations. This orthodoxy has been challenged by adherents of functional analysis and by connectionists. Functional analysis as practiced by Marr is consistent with an analysis of representation that grants primacy to a stands for conception of representation. Connectionism is also compatible with this notion of representation; when conjoined with (...) functional analysis, it provides a means of analyzing psychological systems in term of rules and representations without becoming committed to symbolism. Direct theorists, who rejected the orthodox symbolist conception of representation because it violated their strictures against cognitive mediational mechanisms, should find it possible to accept rules-and-representations and information-processing analyses of the mechanisms of information pickup couched in terms of functional analysis. (shrink)

The current methods in psychoanalytic studies of God images and representations have focused almost exclusively on individual, internal processes. This article examines how psychological anthropologists go about formulating symbolic representations of deity in their research, in comparison with the object relations method of God- representations. Drawing on Melford Spiro's integrative proposal for interpreting the mental and collective representations in religious symbol systems, this paper proposes that there is a need for a comprehensive model of the representational process in the (...) Eastern world in order to suit its cultural traditions. The author uses both theoretical and historical materials as well as personal narrative throughout its entirety to balance the two in a mutual and coherent flow of understanding. Noting the culturally patterned interactions with culturally postulated God-symbols, the object relations method of God- representations will be utilized to probe how God is both created and found on a collective level as well as individual level. (shrink)

The goal of this paper is to develop a systematic taxonomy of cognitive artifacts, i.e., human-made, physical objects that functionally contribute to performing a cognitive task. First, I identify the target domain by conceptualizing the category of cognitive artifacts as a functional kind: a kind of artifact that is defined purely by its function. Next, on the basis of their informational properties, I develop a set of related subcategories in which cognitive artifacts with similar properties can be grouped. In this (...) taxonomy, I distinguish between three taxa, those of family, genus, and species. The family includes all cognitive artifacts without further specifying their informational properties. Two genera are then distinguished: representational and non-representational (or ecological) cognitive artifacts. These genera are further divided into species. In case of representational artifacts, these species are iconic, indexical, or symbolic. In case of ecological artifacts, these species are spatial or structural. Within species, token artifacts are identified. The proposed taxonomy is an important first step towards a better understanding of the range and variety of cognitive artifacts and is a helpful point of departure, both for conceptualizing how different artifacts augment or impair cognitive performance and how they transform and are integrated into our cognitive system and practices. (shrink)

Cognition is commonly taken to be computational manipulation of representations. These representations are assumed to be digital, but it is not usually specified what that means and what relevance it has for the theory. I propose a specification for being a digital state in a digital system, especially a digital computational system. The specification shows that identification of digital states requires functional directedness, either for someone or for the system of which it is a part. In the case or digital (...) representations, to be a token of a representational type, where the function of the type is to represent. [An earlier version of this paper was discussed in the web-conference "Interdisciplines" https://web.archive.org/web/20100221125700/http://www.interdisciplines.org/adaptation/papers/7 ]. (shrink)

The distinction between non-symbolic and symbolic number is poorly addressed by the authors despite being relevant in numerical cognition, and even more important in light of the proposal that the approximate number system represents rational numbers. Although evidence on non-symbolic number and ratios fits with ANS representations, the case for symbolic number and rational numbers is still open.

We develop a general covariant categorical modeling theory of natural systems’ behavior based on the fundamental functorial processes of representation and localization-globalization. In the first part of this study we analyze the process of representation. Representation constitutes a categorical modeling relation that signifies the semantic bidirectional process of correspondence between natural systems and formal symbolic systems. The notion of formal systems is substantiated by algebraic rings of observable attributes of natural systems. In this perspective, the distinction between simple and (...) complex systems is reflected in the appropriate qualification of their corresponding rings of observables. The crucial distinguishing requirement with respect to the coordinatizing rings of observables has to do with the property of global commutativity. The global information structure representing the behavior of a complex system is modeled functorially in terms of its Spectrum functor. Due to the fact that, the fundamental process of representation is bidirectional by construction, it admits a precise categorical formulation in terms of the syntactic language of adjoint functors, constituting thus, a categorical adjunction. The left adjoint functor of this adjunction signifies the process of encoding the information related with phenomena of natural systems in terms of coordinatizing rings of observables, whereas, the right adjoint functor signifies the inverse process of information decoding, which, can be used for making predictions about the behavior of natural systems. (shrink)

Representation is a central part of models in cognitive science, but recently this idea has come under attack. Researchers advocating perceptual symbol systems, situated action, embodied cognition, and dynamical systems have argued against central assumptions of the classical representational approach to mind. We review the core assumptions of the dominant view of representation and the four suggested alternatives. We argue that representation should remain a core part of cognitive science, but that the insights from these alternative approaches must be incorporated (...) into models of cognitive processing. (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)

The main aim of this article is to present and defend a thesis according to which conceptual representations of some types of mental states are encoded in the same neural structures that underlie the first-personal experience of those states. To support this proposal here, I will put forth a novel account of the cognitive function played by ‘shared representations’ of emotions and bodily sensations, i.e. neural structures that are active when one experiences a mental state of a certain type as (...) well as when one observes someone else experiencing a state of the same type. I will argue that shared representations in fact constitute vehicles of certain mental state concepts (more precisely, concepts of specific types of emotions and somatosensory states). The main line of arguing for this will consist in showing that shared representations exhibit specific, ‘conceptual’ functional properties:(1) causal effect on forming metacognitive judgments, (2) cognitive penetrability, (3) diversity of input types. (shrink)

Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.

The second chapter spells out Buridan’s conception of logic as a practical science, teaching us, as logica docens, to heed the valid rules of reasoning embedded in our logical practice, logica utens. The chapter also deals with the particular difficulties of Buridan’s approach, considering his idea of the radical conventionality of written and spoken languages, consisting of token-symbols that owe their meaningfulness to the natural representational system of the human mind. This is the fundamental idea that naturally leads to the (...) nominalist conception of a mental language. On this conception, mental language itself is a compositional semantic system of naturally representative token-symbols, the singular mental acts of singular human minds. (shrink)

In the first section of the article, we examine some recent criticisms of the connectionist enterprise: first, that connectionist models are fundamentally behaviorist in nature (and, therefore, non-cognitive), and second that connectionist models are fundamentally associationist in nature (and, therefore, cognitively weak). We argue that, for a limited class of connectionist models (feed-forward, pattern-associator models), the first criticism is unavoidable. With respect to the second criticism, we propose that connectionist modelsare fundamentally associationist but that this is appropriate for building models (...) of human cognition. However, we do accept the point that there are cognitive capacities for which any purely associative model cannot provide a satisfactory account. The implication that we draw from is this is not that associationist models and mechanisms should be scrapped, but rather that they should be enhanced.In the next section of the article, we identify a set of connectionist approaches which are characterized by “active symbols” — recurrent circuits which are the basis of knowledge representation. We claim that such approaches avoid criticisms of behaviorism and are, in principle, capable of supporting full cognition. In the final section of the article, we speculate at some length about what we believe would be the characteristics of a fully realized active symbol system. This includes both potential problems and possible solutions (for example, mechanisms needed to control activity in a complex recurrent network) as well as the promise of such systems (in particular, the emergence of knowledge structures which would constitute genuine internal models). (shrink)

According to the Computational/Representational Theory of Thought (CRTT ? Language of Thought Hypothesis, or LOTH), propositional attitudes, such as belief, desire, and the like, are triadic relations among subjects, propositions, and internal mental representations. These representations form a representational _system_ physically realized in the brain of sufficiently sophisticated cognitive organisms. Further, this system of representations has a combinatorial syntax and semantics, but the processes that operate on the representations are causally sensitive only to their syntax, not to their semantics. On (...) this approach, a first pass account of propositional attitudes is the following (cf. Field 1978: 37 and Fodor 1987: 17). (shrink)

This study focuses on concepts and, ultimately, their possible implementation in brains. Especially salient is analysis of Jerry Fodor's work. The view of concepts found therein is one where many of both are "simple": to be ascribed or to token most concepts doesn't require being ascribed or tokening any other concepts, and most symbols lack "parts" which are themselves symbols. This is, I think, a very popular, and mistaken, view. ;In chapter 1, I argue that Fodor's theory of content is, (...) contra its goals, neither naturalistic nor atomistic. A deep mistake undermines both, viz. that there's no way to individuate a mental symbol non-semantically. This leads to chapter 2, where I explore the much neglected notion of Mentalese syntax. I argue that there are three possible principles of syntactic type individuation, and that these turn out not only to be incompatible but also inadequate for a "language of thought." I conclude that the idea of simple mental symbols is in trouble. ;This then allows me to argue in chapter 3 that we don't have to fear intentional holism , which sanctions the prevalence of complex concepts. I next sketch, in chapter 4, the sort of complex symbols needed to implement complex concepts. The chapter's bulk consists in an empirical argument for the complexity view via seven domains of current cognitive science, including concept context dependency, typicality effects, association, and reasoning. ;Chapter 5 concludes by exploring connectionism. I argue that, despite problems, connectionism seems on the right track in a way that Fodor's computationalism isn't. For example, it's more explicitly brain-like; in dropping the idea of mental computation over symbols it's free from the syntax problems of chapter 2; and, most important, it's highly compatible with the complexity view, which best characterized the cognitive processes of chapter 4. (shrink)

No one need deny the importance of language to thought and cognition. At the same time, there is a tendency in studies of mind and mental functioning to assume that properties and principles of linguistic, or language-like, forms of representation must hold of forms of thought and representation in general. Consideration of a wider range of symbol systems shows that this is not so. In turn, various claims and arguments in cognitive theory that depend on assumptions applicable only to linguistic (...) systems, do not go through or become difficult to state in a manner that makes them both interesting and plausible. (shrink)

There is disagreement over the notion of representation in cognitive science. Many investigators equate representations with symbols, that is, with syntactically defined elements in an internal symbol system. In recent years there have been two challenges to this orthodoxy. First, a number of philosophers, including many outside the symbolist orthodoxy, have argued that "representation" should be understood in its classical sense, as denoting a "stands for" relation between representation and represented. Second, there has been a growing challenge to orthodoxy under (...) the banner of connectionism. Although this connectionist challenge has evoked emotionally-charged rebukes from the symbolist camp, connectionists as a group have not articulated a conception of representation to replace the symbolist view. Nonetheless, most agree on the need for a nonsymbolic notion of representation. This paper advances a "stands for" sense of representation as primary by incorporating it into a general approach to cognitive science consonant with the connectionism. The idea is to marry connectionism to a particular version of functionalism, viz., one that builds its notion of "function" on the similarity between functional analysis in biology and in psychology. It builds on my earlier work adapting and revising Marr's tri-level approach to cognition. My proposal frees Marr's analysis from its moorings in the orthodox symbolic view of representation and allies it with a notion of functional analysis akin to that proposed by Cummins, developed further by Haugeland, and invoked by Millikan and Dretske. Unlike these authors, however, I do not wed representation to a general belief-desire analysis of behavior. Rather, I follow what I take to be the lesson of psychological practice, according to which the investigator does not seek to explain behavior in general but seeks to analyze the cognitive capacities that underlie behavior, such as vision, memory, learning, and linguistic capacities. Accordingly, ascriptions of representational content are made not by working back from belief-desire ascriptions, but in the context of forming psychological models to account for specific cognitive capacities. Because conceptions of representation in cognitive science typically are embedded in a general approach to the study of cognition, arguments for the comparative plausibility of a particular conception of representation must scout these larger frameworks. I therefore begin by characterizing the interlocking set of assumptions that gave life to the orthodox symbolist approach, paying special attention to the complementarity between representation and process that naturally arises from those assumptions. I then consider two versions of an alternative approach to representation: Dretske's and my own. Finally, I urge the merits of the "cognitive capacities" over the "belief-desire" approach to the subject-matter of cognitive science. (shrink)

Robert Cummins [(1996) Representations, targets and attitudes, Cambridge, MA: Bradford/MIT, p. 1] has characterized the vexed problem of mental representation as "the topic in the philosophy of mind for some time now." This remark is something of an understatement. The same topic was central to the famous controversy between Nicolas Malebranche and Antoine Arnauld in the 17th century and remained central to the entire philosophical tradition of "ideas" in the writings of Locke, Berkeley, Hume, Reid and Kant. However, the scholarly, (...) exegetical literature has almost no overlap with that of contemporary cognitive science. I show that the recurrence of certain deep perplexities about the mind is a systematic and pervasive pattern arising not only throughout history, but also in a number of independent domains today such as debates over visual imagery, symbolic systems and others. Such historical and contemporary convergences suggest that the fundamental issues cannot arise essentially from the theoretical guise they take in any particular case. (shrink)

In sensorimotor integration, representation involves an anticipatory model of the action to be performed. This model integrates efferent signals (motor commands), its reafferent consequences (sensory consequences of an organism’s own motor action), and other afferences (sensory signals) originated by stimuli independent of the action performed. Representation, a form of internal modeling, is invoked to explain the fact that behavior oriented to the achievement of future goals is relatively independent from the immediate environment. Internal modeling explains how a cognitive system achieves (...) its goals despite variations in the environment with insufficient and noisy sensory–perceptual data. In a self that acts intentionally on the environment, knowledge is dependent upon the necessity to guide actions directed toward an aim. The self-inner model, a representation of internal and external environments (including reafferent and afferent messages) and also of the behavior plans and desirable future states (aims) and efferent intentions (motor planning and motor command messages), is intrinsically linked to a thinking capacity, which is supposed to emerge from the binding of multiple influences. Thinking emerges when higher behavior strategies are considered possible and capable of leading to aims or the fulfillment of intentions. In this model, symbolization processes are projective and anticipatory and, in this way, beyond present referents. Symbolization occurs linked to action planning, command, and regulation in mental simulation. Meaning is related to an inner sense of a self that acts over the environment. (shrink)

No one need deny the importance of language to thought and cognition. At the same time, there is a tendency in studies of mind and mental functioning to assume that properties and principles of linguistic, or language-like, forms of representation must hold of forms of thought and representation in general. Consideration of a wider range of symbol systems shows that this is not so. In turn, various claims and arguments in cognitive theory that depend on assumptions applicable only to linguistic (...) systems, do not go through or become difficult to state in a manner that makes them both interesting and plausible. (shrink)

In this wide-ranging book the author presents his critique of the contemporary portrayal of cognition, an analysis of the conceptual foundations of cognitive science and a proposal for a new concept of the mind. Shanon argues that the representational account is seriously lacking and that far from serving as a basis of cognitive activity, representations are the products of such activity. He proposes an alternative view of the mind in which the basic capability of the cognitive system is not the (...) manipulation of symbols but rather action in the world. His book offers a different outlook on the phenomenon of consciousness and presents a new conception of psychological theory and explanation. This revised second edition includes a new Postscript. (shrink)

Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved: " • Just how modular is the mind? – a debate initially pitting encapsulated mechanisms against (...) highly interactive ones. • Does the mind process language-like representations according to formal rules? – a debate initially pitting symbolic architectures against less language-like architectures. " Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language processing—involves rules operating on language-like representations actually predates cognitive science. In traditional philosophy of mind, mental life is construed as involving propositional attitudes—that is, such attitudes towards propositions as believing, fearing, and desiring that they be true—and logical inferences from them. On this view, if a person desires that a proposition be true and believes that if she performs a certain action it will become true, she will make the inference and perform the action. (shrink)

This paper develops a theory of analog representation. We first argue that the mark of the analog is to be found in the nature of a representational system’s interpretation function, rather than in its vehicles or contents alone. We then develop the rulebound structure theory of analog representation, according to which analog systems are those that use interpretive rules to map syntactic structural features onto semantic structural features. The theory involves three degree-theoretic measures that capture three independent ways in which (...) a system can be more or less analog. We explain how our theory improves upon prior accounts of analog representation, provides plausible diagnoses for novel challenge cases, extends to hybrid systems that are partially analog and partially symbolic, and accounts for some of the advantages and disadvantages of representing analogically versus symbolically. (shrink)

Markman and Dietrich¹ recently recommended extending our understanding of representation to incorporate insights from some “alternative” theories of cognition: perceptual symbol systems, situated action, embodied cognition, and dynamical systems. In particular, they suggest that allowances be made for new types of representation which had been previously under-emphasized in cognitive science. The amendments they recommend are based upon the assumption that the alternative positions each agree with the classical view that cognition requires representations, internal mediating states that bear information.² In the (...) case of one of the alternatives, dynamical systems³, this is simply false: many dynamically-oriented cognitive scientists are anti-representationalists.^4,5,6. (shrink)

The symbol grounding problem is concerned with the question of how the knowledge used in AI programs, expressed as tokens in one form or another or simply symbols, could be grounded to the outside world. By grounding the symbols, it is meant that the system will know the actual objects, events, or states of affairs in the world to which each symbol refers and thus be worldly-wise. Solving this problem, it was hoped, would enable the program to understand its own (...) action and hence be truly intelligent ). The problem becomes more acute after a challenge posed by Searle in his now famous Chinese Room Gedanken experiment. Searle argued that no AI programs can be said to understand or have other cognitive states, if all they do is formal symbol manipulation. (shrink)

Many recent cognitive studies reveal that human cognition is inherently perceptual, sharing systems with perception at both the conceptual and the neural levels. This paper introduces Barsalou's theory of perceptual symbols and explores its implications for philosophy of science. If perceptual symbols lie in the heart of conceptual processing, the process of attribute selection during concept representation, which is critical for defining similarity and thus for comparing taxonomies, can no longer be determined solely by background beliefs. The analogous nature of (...) perceptual symbols and the spatial nature of intraconceptual relations impose new constraints on attribute selection. These constraints help people with different background beliefs select compatible attributes, which constitute a common "platform" for taxonomy comparison. (shrink)

Symbolic arithmetic is fundamental to science, technology and economics, but its acquisition by children typically requires years of effort, instruction and drill1,2. When adults perform mental arithmetic, they activate nonsymbolic, approximate number representations3,4, and their performance suffers if this nonsymbolic system is impaired5. Nonsymbolic number representations also allow adults, children, and even infants to add or subtract pairs of dot arrays and to compare the resulting sum or difference to a third array, provided that only approximate accuracy is required6–10. (...) Here we report that young children, who have mastered verbal counting and are on the threshold of arithmetic instruction, can build on their nonsymbolic number system to perform symbolic addition and subtraction11–15. Children across a broad socio-economic spectrum solved symbolic problems involving approximate addition or subtraction of large numbers, both in a laboratory test and in a school setting. Aspects of symbolic arithmetic therefore lie within the reach of children who have learned no algorithms for manipulating numerical symbols. Our findings help to delimit the sources of children’s difficulties learning symbolic arithmetic, and they suggest ways to enhance children’s engagement with formal mathematics. We presented children with approximate symbolic arithmetic problems in a format that parallels previous tests of non-symbolic arithmetic in preschool children8,9. In the first experiment, five- to six-year-old children were given problems such as ‘‘If you had twenty-four stickers and I gave you twenty-seven more, would you have more or less than thirty-five stickers?’’. Children performed well above chance (65.0%, t1952.77, P 5 0.012) without resorting to guessing or comparison strategies that could serve as alternatives to arithmetic. Children who have been taught no symbolic arithmetic therefore have some ability to perform symbolic addition problems. The children’s performance nevertheless fell short of performance on non-symbolic arithmetic tasks using equivalent addition problems with numbers presented as arrays of dots and with the addition operation conveyed by successive motions of the dots into a box (71.3% correct, F1,345 4.26, P 5 0.047)8.. (shrink)

Erratum to: Synthese 191:1925–1930 DOI:10.1007/s11229-013-0380-3 The authors were unaware that points in their article appeared in “Caveats for Causal Reasoning with Equilibrium Models,” by Denver Dash and Marek Druzdzel, published in S. Benferhat and P. Besnard : European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty 2001, Lecture Notes in Artificial Intelligence 2143, pp. 192–203. The authors were unaware of this essay and would like to apologize to the authors for failing to cite their excellent work.