About this topic

Does computation require representation? To what extent should representation figure within computational models? Can representational properties causally influence computation? How central an explanatory role should semantics occupy within computational psychology? Is the mind a “syntax-driven” machine? Can computational models help elucidate the nature of representation? Can they help us reduce the intentional to the non-intentional? What semantic frameworks are most useful for computer science and Artificial Intelligence? Can we build an artificial computing machine that thinks? How might the construction of such a machine illuminate the mind, including our capacity to represent? Is mental activity best modeled through “classical” computation, through “connectionist” computation, or through some other framework?

Key works The seminal article Turing 1936 introduces the Turing machine, thereby laying the foundation for all subsequent research on computation within computer science, recursion theory, Artificial Intelligence, cognitive psychology, and philosophy. Putnam 1967 introduced philosophers to the thesis that Turing-style computation provides illuminating models of mental activity. Fodor 1975 developed Putnam’s suggestion, combining it with the traditional picture of the mind as a representational organ. Fodor’s subsequent writings, including Fodor 1981 and many other articles and books, investigate the relation between mental computation and mental representation. Stich 1983 combines a computational approach to the mind with eliminativism regarding intentionality. Dennett 1987 advocates a broadly instrumentalist approach to intentionality. Searle 1980 is a widely discussed critique of the computational approach, centered on the relation between syntax and semantics. Putnam 1975 introduces the Twin Earth thought experiment, which crucially informs much of the subsequent literature on computation and representation. Burge 1982 applies the Twin Earth thought experiment to mental representation (whereas Putnam initially applied it only to linguistic representation).
Introductions The first three chapters of Rogers 1987 present the foundations of computation theory, with an emphasis on the Turing machine. Fodor 1981 offers a good (albeit opinionated) introduction to issues surrounding computation and mental representation. Horst 2005 and Pitt 2008 offer helpful surveys of the contemporary literature.
Related categories

666 found
1 — 50 / 666
Material to categorize
  1. On Knowledge Representation in Belief Networks.Bruce Abramson - 1991 - In B. Bouchon-Meunier, R. R. Yager & L. A. Zadeh (eds.), Uncertainty in Knowledge Bases. Springer. pp. 86--96.
  2. Rules Work on One Representation; Similarity Compares Two Representations.Todd M. Bailey - 2005 - Behavioral and Brain Sciences 28 (1):16-16.
    Rules and similarity refer to qualitatively different processes. The classification of a stimulus by rules involves abstract and usually domain-specific knowledge operating primarily on the target representation. In contrast, similarity is a relation between the target representation and another representation of the same type. It is also useful to distinguish associationist processes as a third type of cognitive process.
  3. Plausible Inference and Implicit Representation.Malcolm I. Bauer - 1993 - Behavioral and Brain Sciences 16 (3):452.
  4. Droit, Langues Et Représentation Des Connaissances.Hélène Bauer-Bernit - 1987 - Theoria 3 (1):123-136.
    The connection between law, language and knowledge representation is evoked in its theoretical framework, in the light of recent developments in linguistics, philosophy, theory of law and congnitivescience on wich artificial intelligence is based. The conditions and limitations of the modelisation of law are examined. Conclusions are draw concerning the feasibility; usefulness and limitations of “trans-frontier” expert-systems.
  5. Information and Representation in Autonomous Agents.Mark H. Bickhard - 2000 - Cognitive Systems Research 1 (2):65-75.
    Information and representation are thought to be intimately related. Representation, in fact, is commonly considered to be a special kind of information. It must be a _special_ kind, because otherwise all of the myriad instances of informational relationships in the universe would be representational -- some restrictions must be placed on informational relationships in order to refine the vast set into those that are truly representational. I will argue that information in this general sense is important to genuine agents, but (...)
  6. A Neural-Symbolic Perspective on Analogy.Rafael V. Borges, Artur S. D'Avila Garcez & Luis C. Lamb - 2008 - Behavioral and Brain Sciences 31 (4):379-380.
    The target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.
  7. Principles of Knowledge Representation.Gerhard Brewka - 1996 - Center for the Study of Language and Inf.
  8. On the Origin of Objects.Brian Cantwell Smith - 1996 - MIT Press.
  9. Tracking Latent Domain Structures: An Integration of Pathfinder and Latent Semantic Analysis. [REVIEW]Chaomei Chen - 1997 - AI and Society 11 (1-2):48-62.
    Standard psychological scaling methods have been widely used as knowledge elicitation tools to uncover structural characteristics of a given domain. However, these methods traditionally rely on relatedness ratings from human experts, which is often time-consuming and tedious. We describe an integrated approach to knowledge elicitation and representation using Latent Semantic Analysis and Pathfinder Network Scaling techniques. The semantic structure of a subject domain can be automatically characterised from a collection of published documents in the domain. The method is illustrated with (...)
  10. Models and Symbolic Nature of Knowledge.Malgorzata Czarnocka - 1995 - In HerfelWilliam (ed.), Theories and Models in Scientific Processes. Rodopi. pp. 44--27.
  11. Symbolic Nature of Cognition.Małgorzata Czarnocka - 2016 - Dialogue and Universalism 26 (1):121-136.
    I propose here an image of knowledge based on the concept of symbol: according to it, the relation of representation that constituting cognition is a symbolization. It is postulated that both the representing conceptual model, i.e. a pre-linguistic entity acquired in cognition, and the true sentence it generates are of symbolic and not of mirroring character. The symbolic nature of cognition carries dialectical tension. We have at our disposal conceptual models and true sentences which symbolically represent reality. However, it is (...)
  12. The Representation of Legal Contracts.Aspassia Daskalopulu & Marek Sergot - 1997 - AI and Society 11 (1-2):6-17.
    The paper outlines ongoing research on logic-based tools for the analysis and representation of legal contracts, of the kind frequently encountered in large-scale engineering projects and complex, long-term trading agreements. We consider both contract formation and contract performance, in each case identifying the representational issues and the prospects for providing automated support tools.
  13. Epistemological Approach to the Process of Practice.Richard Dazeley & Beyong Ho Kang - 2008 - Minds and Machines 18 (4):547-567.
    Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in (...)
  14. Some Learning Problems Concerning the Use of Symbolic Language in Physics.Silvia Ragout De Lozano & Marta Cardenas - 2002 - Science and Education 11 (6):589-599.
  15. Essai de représentation concrète du processus physiologique de l'intelligence.Louis Dessagne - 1938 - Revue Philosophique de la France Et de l'Etranger 126 (9/10):129 - 160.
  16. A General Representation for Internal Proportional Cornbinatorial Measurement Systems When the Operation Is Not Necessari!Y Closed.José A. Díez - 1999 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 14 (1):157-178.
    The aim of this paper is to give one kind of internal proportional systems with general representation and without closure and finiteness assumptions. First, we introduce the notions of internal proportional system and of general representation. Second, we briefly review the existing results which motivate our generalization. Third, we present the new systems, characterized by the fact that the linear order induced by the comparison weak order ≥ at the level of equivalence classes is also a weIl order. We prove (...)
  17. Developing Structured Representations.Leonidas A. A. Doumas & Lindsey E. Richland - 2008 - Behavioral and Brain Sciences 31 (4):384-385.
    Leech et al.'s model proposes representing relations as primed transformations rather than as structured representations (explicit representations of relations and their roles dynamically bound to fillers). However, this renders the model unable to explain several developmental trends (including relational integration and all changes not attributable to growth in relational knowledge). We suggest looking to an alternative computational model that learns structured representations from examples.
  18. Knowledge Representation, Reflexive Reasoning and Discourse Processing.Jesús Ezquerro & Mauricio Iza - 1996 - Theoria 11 (26):125-145.
  19. Conceptual Projection and Middle Spaces.Gilles Fauconnier & Mark Turner - unknown
    Conceptual projection from one mental space to another always involves projection to "middle" spaces-abstract "generic" middle spaces or richer "blended" middle spaces. Projection to a middle space is a general cognitive process, operating uniformly at different levels of abstraction and under superficially divergent contextual circumstances. Middle spaces are indispensable sites for central mental and linguistic work. The process of blending is in particular a fundamental and general cognitive process, running over many (conceivably all) cognitive phenomena, including categorization, the making of (...)
  20. Representational Systems.Tomer Fekete - 2010 - Minds and Machines 20 (1):69-101.
    The concept of representation has been a key element in the scientific study of mental processes, ever since such studies commenced. However, usage of the term has been all but too liberal—if one were to adhere to common use it remains unclear if there are examples of physical systems which cannot be construed in terms of representation. The problem is considered afresh, taking as the starting point the notion of activity spaces—spaces of spatiotemporal events produced by dynamical systems. It is (...)
  21. Learning New Features of Representation.R. L. Goldstone & P. Schyns - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 974--978.
  22. Cognitive Architectures Combine Formal and Heuristic Approaches.Cleotilde Gonzalez & Christian Lebiere - 2013 - Behavioral and Brain Sciences 36 (3):285 - 286.
    Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.
  23. A Defence of Connectionism Against the "Syntactic" Argument.Marcello Guarini - 2001 - Synthese 128 (3):287-317.
    In "Representations without Rules, Connectionism and the Syntactic Argument'', Kenneth Aizawa argues against the view that connectionist nets can be understood as processing representations without the use of representation-level rules, and he provides a positive characterization of how to interpret connectionist nets as following representation-level rules. He takes Terry Horgan and John Tienson to be the targets of his critique. The present paper marshals functional and methodological considerations, gleaned from the practice of cognitive modelling, to argue against Aizawa's characterization of (...)
  24. Trading Spaces: A Promissory Note to Solve Relational Mapping Problems.Karl Haberlandt - 1997 - Behavioral and Brain Sciences 20 (1):74-74.
    Clark & Thornton have demonstrated the paradox between the opacity of the transformations that underlie relational mappings and the ease with which people learn such mappings. However, C&T's trading-spaces proposal resolves the paradox only in the broadest outline. The general-purpose algorithm promised by C&T remains to be developed. The strategy of doing so is to analyze and formulate computational mechanisms for known cases of recoding.
  25. Learning and Representation: Tensions at the Interface.Steven José Hanson - 1990 - Behavioral and Brain Sciences 13 (3):511-518.
  26. Embodied Cognition and the Orwell’s Problem in Cognitive Science.V. Hari Narayanan - 2015 - AI and Society 30 (2):193-197.
    Embodied approach to cognition has taken roots in cognitive studies with developments in diverse fields such as robotics, artificial life and cognitive linguistics. Taking cue from the metaphor of a Watt governor, this approach stresses on the coupling between the organism and the environment and the continuous nature of the cognitive processes. This results in questioning the viability of computational–representational understanding of mind as a comprehensive theory of cognition. The paper, after giving an overview of embodied approach based on some (...)
  27. Grounding Symbols in Sensorimotor Categories with Neural Networks.Stevan Harnad - 1995 - Institute of Electrical Engineers Colloquium on "Grounding Representations.
    It is unlikely that the systematic, compositional properties of formal symbol systems -- i.e., of computation -- play no role at all in cognition. However, it is equally unlikely that cognition is just computation, because of the symbol grounding problem (Harnad 1990): The symbols in a symbol system are systematically interpretable, by external interpreters, as meaning something, and that is a remarkable and powerful property of symbol systems. Cognition (i.e., thinking), has this property too: Our thoughts are systematically interpretable by (...)
  28. Representation in Perception and Cognition: Connectionist Affordances.Gary Hatfield - 1991 - In William Ramsey, Stephen P. Stich & D. Rumelhart (eds.), Philosophy and Connectionist Theory. Lawrence Erlbaum. pp. 163--95.
    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 (...)
  29. Internal Representation, Cognition, and Belief: The Machine Within the Ghost.Richard Eugene Herder - 1981 - Dissertation, Case Western Reserve University
    Cognitive representations are mysterious entities which philosophers have tended to regard wth suspicion. The taint of the occult persists in large part because two important aspects of the notion of internal representation are often conflated, compounding the great difficulties involved in studying cognitive representations. I have taken pains to separate issues concerning the nature of the internal representations themselves, the vehicles of representation, from issues concerning the representation relation, which links internal representations to their objects. Some authors, "cognitive scientists" of (...)
  30. A Symbolic-Connectionist Theory of Relational Inference and Generalization.John E. Hummel & Keith J. Holyoak - 2003 - Psychological Review 110 (2):220-264.
  31. Ernaer on Die Einfuhlung Und Das Symbol. [REVIEW]Adam Leroy Jones - 1905 - Journal of Philosophy 2 (23):639.
  32. Liczba i symbol. Kilka uwag o renesansowym matematyzowaniu uniwersum.Lucyna Juśkiewicz - 2001 - Filozofia Nauki 3.
  33. Symbol Und Mythus Im Altfranzösischen Rolandslied. [REVIEW]Douglas Kelly - 1972 - Speculum 47 (1):142-144.
  34. Symbolic Parsing Via Subsymbolic Rules.Stan C. Kwasny & Kanaan A. Faisal - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 209--236.
  35. Robert C. Moore, Logic and Representation.G. Landini - 1997 - Minds and Machines 7:122-125.
  36. Principles of Knowledge Representation.Zdzis law Pawlak - 1983 - Bulletin of the Section of Logic 12 (4):194-199.
  37. Symbols as Constraints: The Structuring Role of Dynamics and Selforganization in Natural Langa.Joanna Raczaszek Leonardi - 2009 - Pragmatics and Cognition 17 (3):653-676.
  38. On the Analogy Between Cognitive Representation and Truth.Suárez Mauricio & Solé Albert - 2006 - Theoria: Revista de Teoría, Historia y Fundamentos de la Ciencia 21 (1):39-48.
    In this paper we claim that the notion of cognitive representation is irreducibly plural. By means of an analogy with the minimalist conception of truth, we show that this pluralism is compatible with a generally deflationary attitude towards representation. We then explore the extent and nature of representational pluralism by discussing the positive and negative analogies between the inferential conception of representation advocated by one of us and the minimalist conception of truth.
  39. Peter Gärdenfors: The Geometry of Meaning: Semantics Based on Conceptual Spaces.Ramesh Kumar Mishra - 2016 - Minds and Machines 26 (3):313-316.
  40. Why There Are No Mental Representations.M. Morris - 1991 - Minds and Machines 1 (1):1-30.
    I argue that there are no mental representations, in the sense of “representation” used in standard computational theories of the mind. I take Cummins' Meaning and Mental Representation as my stalking-horse, and argue that his view, once properly developed, is self-defeating. The argument implicitly undermines Fodor's view of the mind; I draw that conclusion out explicitly. The idea of mental representations can then only be saved by appeal to a Dennett-like instrumentalism; so I argue against that too. Finally, I argue (...)
  41. Symbol and Self: A Heuristic Journey Orbiting Symbols and Transformative Symbol Systems.Robert Shamms Mortier - 1988 - Dissertation, The Union for Experimenting Colleges and Universities
    This work investigates symbols and transformative symbol systems from a variety of angles and philosophical/religious viewpoints. Discourses on the idea and term of the symbol are defined and integrated with cultural, philosophical, and historical time-frames begin the inquiry. This is carried into an investigation of both the original and essential qualities involved, and an exploration of the purposes and intentionalities of symbolic perception. Throughout the work, a secondary theme is that of occultic and messianic connections and undertones, and speculations are (...)
  42. Towards a Winograd/Flores Semantics.Peter Mott - 1995 - Minds and Machines 5 (1):69-87.
    A basic theme of Winograd and Flores (1986) is that the principal function of language is to co-ordinate social activity. It is, they claim, from this function that meaning itself arises. They criticise approaches that try to understand meaning through the mechanisms of reference, the Rationalist Tradition as they call it. To seek to ground meaning in social practice is not new, but the approach is presently attractive because of difficulties encountered with the notion of reference. Without taking a view (...)
  43. New Developments in the Philosophy of AI.Vincent C. Müller - 2016 - In Fundamental Issues of Artificial Intelligence. Springer.
    The philosophy of AI has seen some changes, in particular: 1) AI moves away from cognitive science, and 2) the long term risks of AI now appear to be a worthy concern. In this context, the classical central concerns – such as the relation of cognition and computation, embodiment, intelligence & rationality, and information – will regain urgency.
  44. Fundamental Issues of Artificial Intelligence.Vincent C. Müller (ed.) - 2016 - Springer.
    PT-AI 2013: This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this volume investigates include the relation of AI and cognitive science, ethics of AI and robotics, brain (...)
  45. Pancomputationalism: Theory or Metaphor?Vincent C. Müller - 2014 - In Ruth Hagengruber & Uwe Riss (eds.), Philosophy, computing and information science. Pickering & Chattoo. pp. 213-221.
    The theory that all processes in the universe are computational is attractive in its promise to provide an understandable theory of everything. I want to suggest here that this pancomputationalism is not sufficiently clear on which problem it is trying to solve, and how. I propose two interpretations of pancomputationalism as a theory: I) the world is a computer and II) the world can be described as a computer. The first implies a thesis of supervenience of the physical over computation (...)
  46. Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011).Vincent C. Müller - 2011 - The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
  47. The Hard and Easy Grounding Problems (Comment on A. Cangelosi).Vincent C. Müller - 2011 - International Journal of Signs and Semiotic Systems 1 (1):70-70.
    I see four symbol grounding problems: 1) How can a purely computational mind acquire meaningful symbols? 2) How can we get a computational robot to show the right linguistic behavior? These two are misleading. I suggest an 'easy' and a 'hard' problem: 3) How can we explain and re-produce the behavioral ability and function of meaning in artificial computational agents?4) How does physics give rise to meaning?
  48. Philosophy and Theory of Artificial Intelligence.Vincent C. Müller (ed.) - 2011 - Springer.
    Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set (...)
  49. Essay.Noel Murphy - unknown
    The conventional approach to interpreting biological vision systems and experimenting with computer vision systems has been overwhelmingly dominated by a representational view of information. Even more recent connectionist approaches, though embodying a substantial change in viewpoint, have only involved a change of the type of representation, to one of a distributed nature. An alternative view is the notion of information as being constructed and codependent rather than instructional and referential. This is an interpretation based on the more embracing viewpoint of (...)
  50. The Immune Self: Practicing Meaning in Vivo.Yair Neuman - 2012 - Avant: Trends in Interdisciplinary Studies 3 (1):55-62.
    The immune self is our reified way to describe the processes through which the immune system maintains the differentiated identity of the organism and itself. This is an interpretative process, and to study it in a scientifically constructive way we should merge a long hermeneutical tradition asking questions about the nature of interpretation, together with modern understanding of the immune system, emerging sensing technologies and advanced computational tools for analyzing the sensors' data.
1 — 50 / 666