This work aims to contribute to the explanation of cognitive capacities that are essential to human intelligence: commonsense and situation holism. The attempt to explain them within the field of cognitive sciences raises a foundational challenge. How can human cognition distinguish what’s relevant and what’s not in an open-ended set of contexts? The challenge is characterized by a circularity. Potential solutions end up relying on the very capacity that they should be explaining, i.e. the sensitivity to what’s contextually relevant. The (...) argument here developed is an attempt to get out of this circularity. It is grounded on a bi-dimensional analysis of the problem. The first one is representational and is about how cognitive knowledge on what’s relevant can be stored. This dimension is related to the frame problem from artificial intelligence. I claim that this representational aspect of the issue can be neutralized by abandoning language-like representational schemes and sticking to a plurality of structural ones. They allow representational states to play a clear and non-trivial explanatory role while enabling a kind of representational productivity that is not bothered by the frame problem. The second dimension is the inferential one. It encompasses our relevance-sensitive inferential productivity, i.e. our capacity to efficiently infer what’s relevant in indefinitely many contexts, including ones we’ve never faced before. I argue that such relevance sensitivity cannot be neither innate, for innate mechanisms result in the aforementioned circularity, nor learned through innate mechanisms, for learning processes themselves rely on relevance sensitivity. The suggested alternative is that relevance sensitivity is a cognitive gadget. A kind of mental mechanism that is culturally, rather than genetically, inherited. This possibility is rendered plausible within Cecilia Heyes’ “cognitive gadgets” framework. She shows how developmental tweaks and biases culturally established can participate in the constitution of cognitive mechanisms. Thus, cultural elements can participate in human cognition not only as information sources, but also in the form of tweaks that bias the processing of that information towards what’s culturally established as contextually relevant. (shrink)

Volume 33, Issue 8, November 2020, Page 1096-1120.

Is action-guiding vision cognitively penetrable? More specifically, is the visual processing that guides our goal-directed actions sensitive to semantic information from cognitive states? This paper critically examines a recent family of arguments whose aim is to challenge a widespread and influential view in philosophy and cognitive science: the view that action-guiding vision is cognitively impenetrable. I argue, in response, that while there may very well be top–down causal influences on action-guiding vision, they should not be taken to be an instance (...) of cognitive penetration. Assuming otherwise is to assign a computational role to the influencing states that they cannot perform. Although questions about cognitive penetrability are ultimately empirical, the issues addressed in this paper are largely philosophical. The discussion here highlights an important set of considerations that help better understand the relations between cognition, vision, and action. (shrink)

This article describes a representation-based framework of distributed cognition. This framework considers distributed cognition as a cognitive system whose structures and processes are distributed between internal and external representations, across a group of individuals, and across space and time. The major issue for distributed research, under this framework, are the distribution, transformation, and propagation of information across the components of the distributed cognitive system and how they affect the performance of the system as a whole. To demonstrate the value of (...) this representation-based approach, the framework was used to describe and explain an important, challenging, and controversial issue — the concept of affordance. (shrink)

In this commentary, I focus on the difference between processes and representations and how this distinction relates to the question of what is controlled. Despite some views that task switching is a prototypical control process, the analysis concludes that task switching depends on the task goal representation and that control processes are there to prevent goal representations from disintegrating. Over time, these processes become obsolete, leaving behind a representation that automatically controls task performance. The distinction between processes and representations relates (...) to practice effects and automaticity and sheds light on what is meant by the phrase “automatic control.”. (shrink)

De nombreuses études sur l'apprentissage des langues étrangères montrent que l'apprenant est incapable d'atteindre le niveau d'un locuteur natif. Des phénomènes de fossilisation et de nativisation demeurent, même à un niveau très avancé (Selinker & Lamendella, 1980 ; Selinker & Lakshamanan, 1992 ; Andersen, 1983 ; Narcy-Combes, 2005). Cet article met en évidence le rôle limitatif des représentations cognitives chez l'apprenant dans un contexte où il est possible d'isoler chronologiquement la construction des représentations cognitives et celle (...) d'une première langue. Ce contexte est celui de la surdité profonde prélinguistique, et plus spécifiquement celui de l'oralisme : l’acquisition de la langue y est différée de quelques mois ou années sans qu'il n'y ait de système linguistique préalable puisque les oralistes sont tenus à l'écart des langues signées. Les jeunes sourds développent des représentations cognitives dans leurs premières années de vie, préalablement à l'accès à une première langue. Or, des études expérimentales (ex. Vinter, 2000) montrent que les sujets sourds ont une approche inhabituelle du spatial et du temporel, approche pouvant être mise en rapport avec l'absence de perception auditive. Du fait des liens entre perception sensorielle et conception (ex. Talmy, 1988 & 2000), les représentations cognitives des sujets sourds relativement à l'espace et au temps seraient donc spécifiques. La langue orale produite par ces sujets à l'adolescence présente quant à elle des caractéristiques inhabituelles dans les domaines de la langue qui sont liés à l'espace et au temps, et ce, aussi bien chez les anglophones que chez les francophones dont les productions orales ont été analysées (ex. Vincent-Durroux, 2009 & 2008). Les représentations cognitives influent donc sur l'appropriation de la langue par les sourds. La discussion porte sur le fait que, dans le contexte de l'apprentissage de langues étrangères par des entendants, les représentations cognitives sous-jacentes à la langue maternelle pourraient avoir un rôle limitatif et ainsi expliquer la persistance des phénomènes de fossilisation et de nativisation. (shrink)

In their constructive reviews, Frances Egan, Randy Gallistel and Steven Gross have raised some important problems for the account of content advanced by Nicholas Shea in Representation in Cognitive Science. Here the author addresses their main challenges. Egan argues that the account includes an unrecognised pragmatic element; and that it makes contents explanatorily otiose. Gallistel raises questions about homomorphism and correlational information. Gross puts the account to work to resolve a dispute about probabilistic contents in perception, but argues that a (...) question remains about whether probabilities are found in the content or instead in the manner of representation. (shrink)

Research on brain or cognitive/affective processes, culture, social interaction, and structural analysis are overlapping but often independent ways humans have attempted to understand the origins of their evolution, historical, and contemporary development. Each level seeks to employ its own theoretical concepts and methods for depicting human nature and categorizing objects and events in the world, and often relies on different sources of evidence to support theoretical claims. Each level makes reference to different temporal bandwidths (milliseconds, seconds, minutes, hours, days, months, (...) years, decades, and centuries) and focuses on different spatio-temporal activities and controlled and non-controlled stimulus conditions. Biological mechanisms and environmental pressures for survival simultaneously created a gradual intersection and enhancement of cognitive/affective skills, cultural practices, and changes in collaborative social interaction and communicative skills. The evolution of a given level of analysis is assumed to have been incremental and overlapping. These innovative and independent ways humans have learned to characterize their brain or cognitive/affective and social/economic/political life often depend on unexamined, representational re-descriptions or cognitive/affective and socio-cultural devices and forms of communication that facilitate the depiction of practices and beliefs we attribute to respondents or subjects and research colleagues. (shrink)

This book shows how the different forms of sensory, discursive, and theoretical mediation that characterize human cognition are conducive to a realist epistemological framework that explains how the possibility of knowledge about a mind-independent reality is conceivable.

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

The representational nature of human cognition and thought in general has been a source of controversies. This is particularly so in the context of studies of unconscious cognition, in which representations tend to be ontologically and structurally segregated with regard to their conscious status. However, it appears evolutionarily and developmentally unwarranted to posit such segregations, as,otherwise, artifact structures and ontologies must be concocted to explain them from the viewpoint of the human cognitive architecture. Here, from a by-and-large Classical cognitivist viewpoint, (...) I show why this segregation is wrong, and elaborate on the need to postulate an ontological and structural continuity between unconscious and conscious representations. Specifically, I hypothesize that this continuity is to be found in the symbolic-based interplay between the syntax and the semantics of thought, and I propose a model of human information processing characterized by the integration of syntactic and semantic representations. (shrink)

How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.

This paper is a revised and extended version of a keynote contribution to a recent conference on Cognitive Informatics. It offers a brief summary of some of the core concerns of other contributions to the conference, highlighting the range of issues under discussion; and argues that many of the central concepts and preoccupations of cognitive informatics as understood by participants--and others in the general field of computation--rely on ill-founded realist assumptions, and what has been termed the functionalist view of representation. (...) Even if such ideas--albeit in a revised form -- can be defended, there must be a more extensive engagement with the literature and issues outside the confines of the computing and computational orthodoxy. (shrink)

Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. (...) I will assume that representational explanations should be construed as a form of mechanistic explanations and proceed by proposing a general sketch of a functional architecture of a representational cognitive mechanism. According to the view on offer here, representational mechanisms are mechanisms that meet four conditions: the structural resemblance condition, the action-guidance condition, the decouplability condition, and the error-detection condition. (shrink)

The representation of events, in primates at any rate, is a separate process from their emotional evaluation. The same holds for cognitive evaluation. Here too representation and evaluation are separate operations. Acknowledging the symmetry leads to the notion of free representation.

The distinction between body image and body schema has been incredibly influential in cognitive neuroscience. Recently, researchers have begun to speculate about the relationship between these representations (Gadsby, 2017, 2018; Pitron & de Vignemont, 2017; Pitron et al., 2018). Within this emerging literature, Pitron et al. (2018) proposed that the long-term body image and long-term body schema co-construct one another, through a process of reciprocal interaction. In proposing this model, they make two assumptions: that the long-term body image incorporates the (...) spatial characteristics of tools, and that it is distorted in the case of Alice in wonderland syndrome. Here, I challenge these assumptions, with a closer examination of what the term “long-term body image” refers to. In doing so, I draw out some important taxonomic principles for research into body representation. -/- . (shrink)

This chapter contains sections titled: The Systematicity of Inference The Systematicity of Cognitive Representations The Compositionality of Representations Another Systematicity Argument Can Functional Combinatorialism Explain the Systematic Relations in Thought? Conclusion.

The traditional model of human cognition (TMHC) postulates an ontological and/or structural gap between conscious and unconscious mental representations. By and large, it sees higher-level mental processes as commonly conceptual or symbolic in nature and therefore conscious, whereas unconscious, lower-level representations are conceived as non-conceptual or sub-symbolic. However, experimental evidence belies this model, suggesting that higher-level mental processes can be, and often are, carried out in a wholly unconscious way and/or without conceptual representations, and that these can be processed unconsciously. (...) This entails that the TMHC, as well as the theories on mental representation it motivates and that in turn support it, is wrong. (shrink)

One of the major divergences between dynamical systems theory and symbolism lies in their views on the role of representation in cognition. From the perspective of development, the cognitive development could be divided into three levels: sensorimotor, imagery representation and linguistic representation. It is claimed that representation is not a sufficient condition though it is necessary for cognition. However, it does not mean that the authors agree with the notion of strong coupling in dynamicism that completely rejects representation.

In various contexts and for various reasons, writers often define cognitive processes and architectures as those involving representational states and structures. Similarly, cognitive theories are also often delineated as those that invoke representations. In this paper, I present several reasons for rejecting this way of demarcating the cognitive. Some of the reasons against defining cognition in representational terms are that doing so needlessly restricts our theorizing, it undermines the empirical status of the representational theory of mind, and it encourages wildly (...) deflationary and explanatorily vacuous conceptions of representation. After criticizing this outlook, I sketch alternative ways we might try to capture what is distinctive about cognition and cognitive theorizing. (shrink)

Introduction: Something on the State of the Art 1 I. Functionalism and Realism 1. Operationalism and Ordinary Language 35 2. The Appeal to Tacit Knowledge in Psychological Explanations 63 3. What Psychological States are Not 79 4. Three Cheers for Propositional Attitudes 100 II. Reduction and Unity of Science 5. Special Sciences 127 6. Computation and Reduction 146 III. Intensionality and Mental Representation 7. Propositional Attitudes 177 8. Tom Swift and His Procedural Grandmother 204 9. Methodological Solipsism Considered as a (...) Research Strategy in Cognitive Psychology 225 IV. Nativism 10. The Present Status of the Innateness Controversy 257 Notes 317. (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)

The paper gives an explanation of some ontological and epistemological commitments of a cognitive research program dealing with the social representations, which has been coined by Dan Sperber, namely of the epidemiology of representations. Social representations are described as causal chains linking together mental representations and public productions. The importance of the psychological aspects and external, historical aspects is stressed as necessary in explaining social phenomena. The paper reviews critically Durkheim’s concept of social phenomena and an epistemologically more plausible alternative (...) is offered. (shrink)

Debate on the nature of representation in cognitive systems tends to oscillate between robustly realist views and various anti‐realist options. I defend an alternative view, deflationary realism, which sees cognitive representation as an offshoot of the extended application to cognitive systems of an explanatory model whose primary domain is public representation use. This extended application, justified by a common explanatory target, embodies idealisations, partial mismatches between model and reality. By seeing representation as part of an idealised model, deflationary realism avoids (...) the problems with robust realist views, while keeping allegiance to realism. (shrink)

A principled understanding of representations requires that they have objective, systematic content. It is claimed that there is an interesting form of nonconceptual, intentionality which is processed by non-systematic connectionist networks and has its correctness conditions provided by a modest biosemantics; but this type of content is not properly representational. Finally, I consider the consequences that such a verdict has on eliminativist views that look to connectionism as a means of radically reconceiving our understanding of cognition.

In this article we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasks—tasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations Is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose (...) the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. (shrink)

It is supposed to be common knowledge about the history of ideas that one of the few medieval philosophical contributions preserved in modern philosophical thought is the idea that mental phenomena are distinguished from physical phenomena by their intentionality, their directedness toward some object. As is usually the case with such commonplaces about the history of ideas, this claim is not quite true. Medieval philosophers routinely described ordinary physical phenomena, such as reflections in mirrors or sounds in the air, as (...) exhibiting intentionality, while they described what modern philosophers would take to be typically mental phenomena, such as sensation and imagination, as ordinary physical processes. Still, it is true that medieval philosophers would regard all acts of cognition as characterized by intentionality, on account of which all these acts are some sort of representations of their intended objects. This course is going to provide a broad survey of the conceptual relationships between intentionality, cognition and mental representation as conceived by some of the greatest medieval philosophers, including Aquinas, Scotus, Ockham and Buridan, and some of their lesser known contemporaries. The clarification of these conceptual connections sheds some light not only on the intriguing historical relationships between medieval and modern thought on these issues, but also on some fundamental questions in the philosophy of mind as it is conceived today. (shrink)

Most of this work is concerned with two theories that underlie cognitive science; theories which I call "the representational theory of intentionality" and "the computational theory of cognition" . While the representational theory of intentionality asserts that mental states are about the world in virtue of a representation relation between the world and the state, the computational theory of cognition asserts that humans and others perform cognitive tasks by computing functions on these representations. CTC draws upon a rich analogy between (...) the mind and digital computers, portraying cognizers as receiving input and generating outputs . ;Since researchers within cognitive science--especially within cognitive psychology, computer science, and philosophy of mind--share a common theoretic background in their adherence to the combination of RTI and CTC, what I call "computationalism" most of the work done in philosophy of mind has been aimed at either refuting or further articulating these two theories. This work is strongly within that tradition. ;RTI and CTC combine to create an explanatory framework for explanation in cognitive science. In this work, I seek to delineate the exact structure of this framework, to determine its components and their overall relationships. In my mind, this involves making implicit aspects of computational explanation explicit, providing an overall rationale for the various components and their relationships, and defending that rationale against criticism. ;In particular, the project of this work is to argue that computationalist explanations of cognition within cognitive science need to make appeals to both representation and knowledge; to distinguish the respective roles of knowledge and of representation in an overall picture of computational explanations, and to develop and argue for definitions of representation and knowledge that would allow those concepts to play their respective roles within computational explanations in cognitive science. ;The first half of this work , therefore, is devoted to theories of representation, especially to the need for, and role of representation in computational explanations. The first half of the work culminates in chapter three with a definition of representation and a picture of the structure of computational explanations. The second half of the project then becomes to generate a definition of knowledge that defines knowledge in non-epistemic language amenable to computationalism and that carries the epistemic burdens placed upon knowledge by its use in computational explanations, as well as to offer a limited defence of that definition within the framework of traditional epistemology. (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)

'Representation' is a concept which occurs both in cognitive science and philosophy. It has common features in both settings in that it concerns the explanation of behaviour in terms of the way the subject categorizes and systematizes responses to its environment. The prevailing model sees representations as causally structured entities correlated on the one hand with elements in a natural language and on the other with clearly identifiable items in the world. This leads to an analysis of representation and cognition (...) in terms of formal symbols and their relations. But human perception and cognition use multiple informational constraints and deal with unsystematic and messy input in a way best explained by Parallel Distributed Processing models. This undermines the claim that a formal representational theory of mind is 'the only game in town.' In particular it suggests a radically different model of brain function and its relation to epistemology from that found in current representational theories. (shrink)

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...) led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. (shrink)

The domain of numbers provides a paradigmatic case for investigating interactions of culture, language, and cognition: Numerical competencies are considered a core domain of knowledge, and yet the development of specifically human abilities presupposes cultural and linguistic input by way of counting sequences. These sequences constitute systems with distinct structural properties, the cross-linguistic variability of which has implications for number representation and processing. Such representational effects are scrutinized for two types of verbal numeration systems—general and object-specific ones—that were in parallel (...) use in several Oceanic languages. The analysis indicates that the object-specific systems outperform the general systems with respect to counting and mental arithmetic, largely due to their regular and more compact representation. What these findings reveal on cognitive diversity, how the conjectures involved speak to more general issues in cognitive science, and how the approach taken here might help to bridge the gap between anthropology and other cognitive sciences is discussed in the conclusion. (shrink)

The chapters in this section of the volume reveal the striking variety of human numerical cognition. The section comprises four chapters that focus on different aspects of the representation of numerical knowledge, as well as three chapters that examine the several cognitive processes involved in the manipulation of numbers during simple mental arithmetic. They show how chronometric analyses, in combination with clever experimental designs, can reveal the cognitive processes and representations underlying this impressive collection of cognitive skills. Our goal in (...) this overview chapter is to highlight common themes that connect these contributions. In particular, we suggest links between the present contributions, all of which are firmly grounded in the traditional information-processing approach to the human mind, and the more recently emerged embodied cognition perspective, according to which all knowledge representations remain associated with those sensory and motor features that were activated during acquisition of that knowledge. (shrink)

One of the central issues in cognitive science is the nature of human representations. We argue that symbolic representations are essential for capturing human cognitive capabilities. We start by examining some common misconceptions found in discussions of representations and models. Next we examine evidence that symbolic representations are essential for capturing human cognitive capabilities, drawing on the analogy literature. Then we examine fundamental limitations of feature vectors and other distributed representations that, despite their recent successes on various practical problems, suggest (...) that they are insufficient to capture many aspects of human cognition. After that, we describe the implications for cognitive architecture of our view that analogy is central, and we speculate on roles for hybrid approaches. We close with an analogy that might help bridge the gap. (shrink)

Annotation. Despite their importance, FOR have been generally underinvestigated. As the first book devoted to the theme, it suggests the exciting idea of a computer model that could change its FOR, and then evaluate its FOR relative to a particular task. The book looks at the influence on understanding, insight, expertise and the advance of knowledge of the forms of representation we use. This book will be welcomed by researchers in the fields of Cognitive Science and AI in particular, and (...) Psychology and Philosophy in general. (shrink)

In this paper, we defend a novel, multidimensional account of representational unification, which we distinguish from integration. The dimensions of unity are simplicity, generality and scope, non-monstrosity, and systematization. In our account, unification is a graded property. The account is used to investigate the issue of how research traditions contribute to representational unification, focusing on embodied cognition in cognitive science. Embodied cognition contributes to unification even if it fails to offer a grand unification of cognitive science. The study of this (...) failure shows that unification, contrary to what defenders of mechanistic explanation claim, is an important mechanistic virtue of research traditions. (shrink)

In this book, the author develops a new form of structural realism and deals with the problem of representation. The work combines two distinguished developments of the Semantic View of Theories, namely Structural Realism, a flourishing theory from contemporary philosophy of science, and Ronald Giere and colleagues’ Cognitive Models of Science approach. Readers will see how replacing the model-theoretic structures that are at issue in SR with connectionist networks and activations patterns helps us to deal with the problem of representation. (...) The author suggests that cognitive structures are not only the precise formal tools for regimenting the structure of scientific theories but also the tools that the biological brain uses to capture the essential features of its environment. Therefore, replacing model-theoretic structures with cognitive structures allows us to account for the theories-reality relationship on the basis of the most reliable theories of neurology. This is how a new form of SR, called Cognitive Structural Realism is introduced through this book, which articulates and defends CSR, and shows how two diverging branches of SVT can be reconciled. This ground-breaking work will particularly appeal to people who work in the philosophy of science, philosophy of mind and cognitive sciences. (shrink)

Contemporary literature distinguishes two ways to defend the claim that cognition is a matter of representations: one, cognition involves representation-hungry tasks; two, cognition involves a complex form of informational covariation between subcomponents of a system with an adaptive function. Each of these conceptions involves a different notion of representation, and promotes a particular view of the architecture of cognition. But despite the differences, each of them aims to support the claim that cognition is a matter of representations on architectural constraints. (...) The objective of this article is twofold: one, it is argued that architectural constraints do not entail either of those two ways to defend the claim that cognition is a matter of representations; two, it is claimed that both notions of representation share an objectionable common element – namely, the idea of a model that grounds the representational reading– that must be abandoned, in favor of a more economical explanation in terms of causal relations, in order to get a clear view of cognition. (shrink)

The present study evaluates the cognitive representation of a kicking movement performed by a human and a humanoid robot, and how they are represented in experts and novices of soccer and robotics, respectively. To learn about the expertise-dependent development of memory structures, we compared the representation structures of soccer experts and robot experts concerning a human and humanoid robot kicking movement. We found different cognitive representation structures for both expertise groups under two different motor performance conditions . In general, the (...) expertise relies on the perceptual-motor knowledge of the human motor system. Thus, the soccer experts’ cognitive representation of the humanoid robot movement is dominated by their representation of the corresponding human movement. Additionally, our results suggest that robot experts, in contrast to soccer experts, access functional features of the technical system of the humanoid robot in addition to their perceptual-motor knowledge about the human motor system. Thus, their perceptual-motor and neuro-functional machine representation are integrated into a cognitive representation of the humanoid robot movement. (shrink)

The relationship between cognition and culture is discussed in terms of technology and representation. The computational metaphor is discussed in relation to its providing an account of cognitive and technical development: the role of representation and self-modification through environmental manipulation and the development of open learning from stigmery. A rationalisation for the transformational effects of information and representation is sought in the physical and biological theories of Autokatakinetics and Autopoiesis. The conclusion drawn is that culture, rather than being an intrinsic (...) property of our human phenotype was learned and that cultural cognition is an information transforming system that is inadequately characterised by notions of parameterised deep-structure and that it is an open and potentially unbounded informational system. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety (...) of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (shrink)

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...) the model. While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling that combines dynamical systems theory mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science. (shrink)

_Abstract_: The significance of neuroscientific findings for the analysis of central problems in cognitive science has long been a matter of debate. Recent developments in cognitive neuroscience have reignited this discussion, especially with regard to the study of cognitive representations and cognitive processes. The present paper focuses on multivariate analyses, a class of neuroscientific methods that promises to shed new light on the neural bases of cognitive representations. Multivariate approaches are both powerful and increasingly used. Yet, we argue that their (...) successful application in neuroscience requires significant theoretical and methodological clarification. After providing a preliminary assessment of the pros and cons of multivariate methods, we claim that their successful application crucially depends on how we conceptualize the relationships between representations, cognitive processes, and neural data, in other words, on the cognitive ontology we use to describe the human mind. Our discussion also highlights some general strengths and weaknesses of neuroscientific contributions to the program of classical cognitive science. _Keywords_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology _Rappresentazioni e processi: quale ruolo per i metodi multivariati nelle neuroscienze cognitive?_ _Riassunto_: La rilevanza dei risultati neuroscientifici per quanto riguarda i problemi centrali delle scienze cognitive è motivo di discussione. Recenti sviluppi nelle neuroscienze cognitive hanno rianimato tale dibattito, in particolare rispetto allo studio delle rappresentazioni e dei processi cognitivi. Il presente articolo si focalizza sull’analisi multivariata, un insieme di metodi neuroscientifici che si promettono di studiare le basi neurali delle rappresentazioni cognitive. Nonostante le potenzialità e l’uso pervasivo degli approcci multivariati, in questo lavoro sosteniamo che prima di poter valutare il loro effettivo contributo nello studio delle rappresentazioni cognitive sia necessaria una chiarificazione teorica e metodologica. Dopo una discussione preliminare dei vantaggi e degli svantaggi dei metodi multivariati, evidenziamo come una loro efficace applicazione dipenda in maniera sostanziale da come viene intesa la relazione tra rappresentazioni, processi cognitivi e dati neurali o, in altre parole, dall’ontologia cognitiva che impieghiamo per descrivere la mente umana. Il presente lavoro affronta inoltre i generali punti di forza e gli elementi critici rispetto al contributo della ricerca neuroscientifica nel programma delle scienze cognitive classiche. _Parole chiave_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology. (shrink)

Carey has argued that there is a system of core numerical cognition – the analog magnitude system – in which cardinal numbers are explicitly represented in iconic format. While the existence of this system is beyond doubt, this paper aims to show that its representations cannot have the combination of features attributed to them by Carey. According to the argument from abstractness, the representation of the cardinal number of a collection of individuals as such requires the representation of individuals as (...) such, and this in turn requires non-iconic format, from which it is concluded that the explicit representation of the cardinal number of some individuals requires non-iconic representational format. In support of the first premise, an account is given of what approximate cardinal numbers might be, and in support of the second, a direct argument is articulated and defended. Finally, in response to an objection, a second argument for the central thesis is provided. While the discussion is couched in the terms of Carey’s work, the considerations it adduces are perfectly general, and the conclusion should therefore be taken into consideration by all those aiming to characterize the AM system. (shrink)

In 1969, John McCarthy and Pat Hayes uncovered a problem that has haunted the field of artificial intelligence ever since--the frame problem. The problem arises when logic is used to describe the effects of actions and events. Put simply, it is the problem of representing what remains unchanged as a result of an action or event. Many researchers in artificial intelligence believe that its solution is vital to the realization of the field's goals. Solving the Frame Problem presents the various (...) approaches to the frame problem that have been proposed over the years. The author presents the material chronologically--as an unfolding story rather than as a body of theory to be learned by rote. There are lessons to be learned even from the dead ends researchers have pursued, for they deepen our understanding of the issues surrounding the frame problem. In the book's concluding chapters, the author offers his own work on event calculus, which he claims comes very close to a complete solution to the frame problem. Artificial Intelligence series. (shrink)