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About this topic
Summary The philosophy of neuroscience of representation addresses problems concerning the naturalization of representational content as well as questions concerning the format of representations implemented in brains and neural-inspired artificial systems (connectionist networks).
Key works A key work concerning the naturalization of intentional content as seen from a neural perspective is Akins 1996. See also,  Churchland 1993. Regarding whether connectionist networks utilize a distinct kind of representation, see the classic Haugeland 1998.
Introductions For an introductory overview of the neural basis of content, see the early parts of Mandik 2003. On the question of the format of neural representation, as well as related issues concerning neural computation, see Eliasmith 2003.
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  1. Brain Mechanisms for Offense, Defense, and Submission.David B. Adams - 1979 - Behavioral and Brain Sciences 2 (2):201-213.
  2. Motivational Systems, Motivational Mechanisms, and Aggression.David B. Adams - 1979 - Behavioral and Brain Sciences 2 (2):230-241.
  3. Of Sensory Systems and the "Aboutness" of Mental States.Kathleen Akins - 1996 - Journal of Philosophy 93 (7):337--372.
    La autora presenta una critica a la concepcion clasica de los sentidos asumida por la mayoria de autores naturalistas que pretenden explicar el contenido mental. Esta crítica se basa en datos neurobiologicos sobre los sentidos que apuntan a que estos no parecen describir caracteristicas objetivas del mundo, sino que actuan de forma ʼnarcisita', es decir, representan informacion en funcion de los intereses concretos del organismo.El articulo se encuentra también en: Bechtel, et al., Philosophy and the Neuroscience.
  4. Are Non-Abstract Brain Representations of Number Developmentally Plausible?Daniel Ansari - 2009 - Behavioral and Brain Sciences 32 (3-4):329-330.
    The theory put forward by Cohen Kadosh & Walsh (CK&W) proposing that semantic representations of numerical magnitude in the parietal cortex are format-specific, does not specify how these representations might be constructed over the course of learning and development. The developmental predictions of the non-abstract theory are discussed and the need for a developmental perspective on the abstract versus non-abstract question highlighted.
  5. Do Neurobiological Data Help Us to Understand Economic Decisions Better?Alessandro Antonietti - 2010 - Journal of Economic Methodology 17 (2):207-218.
    The contribution that neurobiological data provide us to comprehend the psychological aspects of economic decision-making is critically examined. First, different kinds of correspondences between neural events and mental activities are identified. On the basis of the distinctions made, some recent studies are selected, each of which focuses on a different stage of decision-making and employs a different set of neurobiological data. The thorough analysis of each study suggests that neuro-mental correspondences do not have an evidentiary function but rather a heuristic (...)
  6. "Mind" as Humanizing the Brain: Toward a Neurotheology of Meaning.James B. Ashbrook - 1997 - Zygon 32 (3):301-320.
  7. The Significance of Causally Coupled, Stable Neuronal Assemblies for the Psychological Time Arrow.Harald Atmanspacher - manuscript
    Stable neuronal assemblies are generally regarded as neural correlates of mental representations. Their temporal sequence corresponds to the experience of a direction of time, sometimes called the psychological time arrow. We show that the stability of particular, biophysically motivated models of neuronal assemblies, called coupled map lattices, is supported by causal interactions among neurons and obstructed by non-causal or anti-causal interactions among neurons. This surprising relation between causality and stability suggests that those neuronal assemblies that are stable due to causal (...)
  8. Interpreting Neurodynamics: Concepts and Facts.Harald Atmanspacher - unknown
    The dynamics of neuronal systems, briefly neurodynamics, has developed into an attractive and influential research branch within neuroscience. In this paper, we discuss a number of conceptual issues in neurodynamics that are important for an appropriate interpretation and evaluation of its results. We demonstrate their relevance for selected topics of theoretical and empirical work. In particular, we refer to the notions of determinacy and stochasticity in neurodynamics across levels of microscopic, mesoscopic and macroscopic descriptions. The issue of correlations between neural, (...)
  9. Neuronal Coding of Perceptual Systems.Werner Backhaus (ed.) - 2001 - World Scientific.
  10. Complex Non-Linear Biodynamics in Categories, Higher Dimensional Algebra and Łukasiewicz–Moisil Topos: Transformations of Neuronal, Genetic and Neoplastic Networks. [REVIEW]I. C. Baianu, R. Brown, G. Georgescu & J. F. Glazebrook - 2005 - Axiomathes 16 (1-2):65-122.
    A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to a Łukasiewicz–Moisil (...)
  11. A Neurocomputational Perspective: The Nature of Mind and the Structure of Science.LR Baker - 1992 - Philosophical Review 101 (4):906.
  12. The Roles Played by External Input and Synaptic Modulations in the Dynamics of Neuronal Systems.Arunava Banerjee - 2001 - Behavioral and Brain Sciences 24 (5):811-812.
    The framework within which Tsuda proposes his solution for transitory dynamics between attractor states is flawed from a neurological perspective. We present a more genuine framework and discuss the roles that external input and synaptic modulations play in the evolution of the dynamics of neuronal systems. Chaotic itinerancy, it is argued, is not necessary for transitory dynamics.
  13. Neurons Amongst the Symbols?C. Philip Beaman - 2000 - Behavioral and Brain Sciences 23 (4):468-470.
    Page's target article presents an argument for the use of localist, connectionist models in future psychological theorising. The “manifesto” marshalls a set of arguments in favour of localist connectionism and against distributed connectionism, but in doing so misses a larger argument concerning the level of psychological explanation that is appropriate to a given domain.
  14. Philosophy and the Neurosciences: A Reader.William P. Bechtel, Pete Mandik, Jennifer Mundale & Robert S. Stufflebeam (eds.) - 2001 - Blackwell.
    2. Daugman, J. G. Brain metaphor and brain theory 3. Mundale, J. Neuroanatomical Foundations of Cognition: Connecting the Neuronal Level with the Study of Higher Brain Areas.
  15. Representation is Space-Variant.Giorgio Bonmassar & Eric L. Schwartz - 1998 - Behavioral and Brain Sciences 21 (4):469-470.
    Under shift, caused for example by eye movement, or by relative movement of the subject or object of perception, the cortical representation undergoes very large changes in “size” and “shape.” Space-variance of cortical representation rules out models that fundamentally require linear interpolation between shifted patterns (e.g., Edelman's model) or rigid shift of an invariant retinal stimulus corresponding to shift at the cortex (e.g., the shifter theory of van Essen). Recently, a computational solution of “quasi-shift” invariance for space-variant mappings has been (...)
  16. The Puzzle of Chaotic Neurodynamics.Roman Borisyuk - 2001 - Behavioral and Brain Sciences 24 (5):812-813.
    Experimental evidence and mathematical/computational models show that in many cases chaotic, nonregular oscillations are adequate to describe the dynamical behaviour of neural systems. Further work is needed to understand the meaning of this dynamical regime for modelling information processing in the brain.
  17. Semantic Cognition or Data Mining?Denny Borsboom & Ingmar Visser - 2008 - Behavioral and Brain Sciences 31 (6):714-715.
    We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.
  18. Internal Representations--A Prelude for Neurosemantics.Olaf Breidbach - 1999 - Journal of Mind and Behavior 20 (4):403-419.
    Following the concept of internal representations, signal processing in a neuronal system has to be evaluated exclusively on the basis of internal system characteristics. Thus, this approach omits the external observer as a control function for sensory integration. Instead, the configuration of the system and its computational performance are the effects of endogeneous factors. Such self-referential operation is due to a strictly local computation in a network. Thereby, computations follow a set of rules that constitutes the emergent behaviour of the (...)
  19. Cognition and the Brain: The Philosophy and Neuroscience Movement.Andrew Brook & Kathleen Akins (eds.) - 2005 - Cambridge University Press.
    This volume provides an up to date and comprehensive overview of the philosophy and neuroscience movement, which applies the methods of neuroscience to traditional philosophical problems and uses philosophical methods to illuminate issues in neuroscience. At the heart of the movement is the conviction that basic questions about human cognition, many of which have been studied for millennia, can be answered only by a philosophically sophisticated grasp of neuroscience's insights into the processing of information by the human brain. Essays in (...)
  20. What is a Brain State?Richard Brown - 2006 - Philosophical Psychology 19 (6):729-742.
    Philosophers have been talking about brain states for almost 50 years and as of yet no one has articulated a theoretical account of what one is. In fact this issue has received almost no attention and cognitive scientists still use meaningless phrases like 'C-fiber firing' and 'neuronal activity' when theorizing about the relation of the mind to the brain. To date when theorists do discuss brain states they usually do so in the context of making some other argument with the (...)
  21. Components of Action Representations Evoked When Identifying Manipulable Objects.Daniel N. Bub, Michael E. J. Masson & Terry Lin - 2015 - Frontiers in Human Neuroscience 9.
  22. Neural Representations Used to Specify Action.Silvia A. Bunge & Michael J. Souza - 2008 - In Silvia A. Bunge & Jonathan D. Wallis (eds.), Neuroscience of Rule-Guided Behavior. Oxford University Press.
  23. Extended Mind and Representation.F. Thomas Burke - 2014 - In John R. Shook & Tibor Solymosi (eds.), Pragmatist Neurophilosophy: American Philosophy and the Brain. Bloomsbury Academic. pp. 177-202.
    Good old-fashioned cognitive science characterizes human thinking as symbol manipulation qua computation and therefore emphasizes the processing of symbolic representations as a necessary if not sufficient condition for “general intelligent action.” Recent alternative conceptions of human thinking tend to deemphasize if not altogether eschew the notion of representation. The present paper shows how classical American pragmatist conceptions of human thinking can successfully avoid either of these extremes, replacing old-fashioned conceptions of representation with one that characterizes both representatum and representans in (...)
  24. Information and the Function of Neurons.Marc Burock - manuscript
    Many of us consider it uncontroversial that information processing is a natural function of the brain. Since functions in biology are only won through empirical investigation, there should be a significant body of unambiguous evidence that supports this functional claim. Before we can interpret the evidence, however, we must ask what it means for a biological system to process information. Although a concept of information is generally accepted in the neurosciences without critique, in other biological sciences applications of information, despite (...)
  25. Evidence for Information Processing in the Brain.Marc Burock - manuscript
    Many cognitive scientists, neuroscientists, and philosophers of science consider it uncontroversial that the brain processes information. In this work we broadly consider the types of experimental evidence that would support this claim, and find that although physical features of specific brain areas selectively covary with external stimuli or abilities, there is no direct evidence supporting an information processing function of any particular brain area.
  26. Constructing the Space of Action: From Bio-Robotics to Mirror Neurons.Massimiliano Cappuccio - 2009 - World Futures 65 (2):126 – 132.
    This article distinguishes three archetypal ways of articulating spatial cognition: (1) via metric representation of objective geometry, (2) via somatosensory constitution of the peripersonal environment, and (3) via pragmatic comprehension of the finalistic sense of action. The last one is documented by neuroscientific studies concerning mirror neurons. Bio-robotic experiments implementing mirror functions confirm the constitutive role of goal-oriented actions in spatial processes.
  27. Representational Redescription and Cognitive Architectures.Antonella Carassa & Maurizio Tirassa - 1994 - Carassa, Antonella and Tirassa, Maurizio (1994) Representational Redescription and Cognitive Architectures. [Journal (Paginated)] 17 (4):711-712.
    We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.
  28. A Theory of the Epigenesis of Neuronal Networks by Selective Stabilization of Synapses.Jean Pierre Changeux, Philippe Courrège & Antoine Danchin - 1973 - Proceedings of the National Academy of Sciences Usa 70 (10):2974-8.
    A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the development of the (...)
  29. Abstraction of Mental Representations : Theoretical Considerations and Neuroscientific Evidence.Kalina Christoff & Kamyar Keramatian - 2008 - In Silvia A. Bunge & Jonathan D. Wallis (eds.), Neuroscience of Rule-Guided Behavior. Oxford University Press.
  30. Neural Representation and Neural Computation.Patricia S. Churchland & Terrence J. Sejnowski - 1989 - In L. Nadel (ed.), Philosophical Perspectives. MIT Press. pp. 343-382.
  31. Cognitive Neurobiology: A Computational Hypothesis for Laminar Cortex. [REVIEW]Paul M. Churchland - 1985 - Biology and Philosophy 1 (1):25-51.
    This paper outlines the functional capacities of a novel scheme for cognitive representation and computation, and it explores the possible implementation of this scheme in the massively parallel organization of the empirical brain. The suggestion is that the brain represents reality by means of positions in suitably constitutes phase spaces; and the brain performs computations on these representations by means of coordinate transformations from one phase space to another. This scheme may be implemented in the brain in two distinct forms: (...)
  32. Philosophical Issues in Brain Theory.Andy Clark - unknown
    The first question concerns a fundamental assumption of most researchers who theorize about the brain. Do neural systems exploit classical compositional and systematic representations, distributed representations, or no representations at all? The question is not easily answered. Connectionism, for example, has been criticised for both holding and challenging representational views. The second quesútion concerns the crucial methodological issue of how results emerging from the various brain sciences can help to constrain cognitive scientific models. Finally, the third question focuses attention on (...)
  33. A a A.Axel Cleeremans - unknown
    While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...)
  34. Consciousness: The Radical Plasticity Thesis.Axel Cleeremans - 2008 - In Rahul Banerjee & B. K. Chakrabarti (eds.), Models of Brain and Mind: Physical, Computational, and Psychological Approaches. Elsevier.
    In this chapter, I sketch a conceptual framework which takes it as a starting point that conscious and unconscious cognition are rooted in the same set of interacting learning mechanisms and representational systems. On this view, the extent to which a representation is conscious depends in a graded manner on properties such as its stability in time or its strength. Crucially, these properties are accrued as a result of learning, which is in turn viewed as a mandatory process that always (...)
  35. Computational Neuroethology: A Provisional Manifesto.D. Cliff - 1990 - In Jean-Arcady Meyer & Stewart W. Wilson (eds.), From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior (Complex Adaptive Systems). Cambridge University Press.
  36. The Nature and Implementation of Representation in Biological Systems.Mike Collins - 2009 - Dissertation, City University of New York
    I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms (...)
  37. Why Build a Virtual Brain? Large-Scale Neural Simulations as Test-Bed for Artificial Computing Systems.Matteo Colombo - 2015 - In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 429-434.
    Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...)
  38. How Authentic Intentionality Can Be Enabled: A Neurocomputational Hypothesis. [REVIEW]Matteo Colombo - 2010 - Minds and Machines 20 (2):183-202.
    According to John Haugeland, the capacity for “authentic intentionality” depends on a commitment to constitutive standards of objectivity. One of the consequences of Haugeland’s view is that a neurocomputational explanation cannot be adequate to understand “authentic intentionality”. This paper gives grounds to resist such a consequence. It provides the beginning of an account of authentic intentionality in terms of neurocomputational enabling conditions. It argues that the standards, which constitute the domain of objects that can be represented, reflect the statistical structure (...)
  39. The Informed Neuron: Issues in the Use of Information Theory in the Behavioral Sciences. [REVIEW]Jeff Coulter - 1995 - Minds and Machines 5 (4):583-96.
    The concept of “information” is virtually ubiquitous in contemporary cognitive science. It is claimed to be “processed” (in cognitivist theories of perception and comprehension), “stored” (in cognitivist theories of memory and recognition), and otherwise manipulated and transformed by the human central nervous system. Fred Dretske's extensive philosophical defense of a theory of informational content (“semantic” information) based upon the Shannon-Weaver formal theory of information is subjected to critical scrutiny. A major difficulty is identified in Dretske's equivocations in the use of (...)
  40. Language as an Emergent Function: Some Radical Neurological and Evolutionary Implications.Terrence W. Deacon - 2005 - Theoria 20 (3):269-286.
    Language is a spontaneously evolved emergent adaptation, not a formal computational system. Its structure does not derive from either innate or social instruction but rather self-organization and selection. Its quasi-universal features emerge from the interactions among semiotic constraints, neural processing limitations, and social transmission dynamics. The neurological processing of sentence structure is more analogous to embryonic differentiation than to algorithmic computation. The biological basis of this unprecedented adaptation is not located in some unique neurologieal structure nor the result of any (...)
  41. Efficiency, Information Theory, and Neural Representations.Joseph T. Devlin, Matt H. Davis, Stuart A. McLelland & Richard P. Russell - 2000 - Behavioral and Brain Sciences 23 (4):475-476.
    We contend that if efficiency and reliability are important factors in neural information processing then distributed, not localist, representations are “evolution's best bet.” We note that distributed codes are the most efficient method for representing information, and that this efficiency minimizes metabolic costs, providing adaptive advantage to an organism.
  42. Memory, Environment, and the Brain.César Schirmer Dos Santos - 2013 - Filosofia Unisinos 14 (3):204-214.
    In recent decades, investigation of brain injuries associated with amnesia allowed progress in the philosophy and science of memory, but it also paved the way for the hubris of assuming that memory is an exclusively neural phenomenon. Nonetheless, there are methodological and conceptual reasons preventing a reduction of the ecological and contextual phenomenon of memory to a neural phenomenon, since memory is the observed action of an individual before being the simple output of a brain (or, at least, so we (...)
  43. Refocusing the Question: Can There Be Skillful Coping Without Propositional Representations or Brain Representations? [REVIEW]Hubert L. Dreyfus - 2002 - Phenomenology and the Cognitive Sciences 1 (4):413-25.
  44. Constraining the Neural Representation of the Visual World.Shimon Edelman - 2002 - Trends in Cognitive Sciences 6 (3):125-131.
  45. Word Versus Task Representation in Neural Networks.Thomas Elbert, Christian Dobell, Alessandro Angrilli, Luciano Stegagno & Brigitte Rockstroh - 1999 - Behavioral and Brain Sciences 22 (2):286-287.
    The Hebbian view of word representation is challenged by findings of task (level of processing)-dependent, event-related potential patterns that do not support the notion of a fixed set of neurons representing a given word. With cross-language phonological reliability encoding more asymmetrical left hemisphere activity is evoked than with word comprehension. This suggests a dynamical view of the brain as a self-organizing, connectivity-adjusting system.
  46. What Sensory Signals Are About.C. L. Elder - 1998 - Analysis 58 (4):273-276.
    In ‘Of Sensory Systems and the “Aboutness” of Mental States’, Kathleen Akins (1996) argues against what she calls ‘the traditional view’ about sensory systems, according to which they are detectors of features in the environment outside the organism. As an antidote, she considers the case of thermoreception, a system whose sensors send signals about how things stand with themselves and their immediate dermal surround (a ‘narcissistic’ sensory system); and she closes by suggesting that the signals from many sensory systems may (...)
  47. A New Perspective on Representational Problems.Chris Eliasmith - 2005 - Journal of Cognitive Science 6:97-123.
  48. How Neurons Mean: A Neurocomputational Theory of Representational Content.Chris Eliasmith - 2000 - Dissertation, Washington University in St. Louis
    Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content.
  49. Decomposability and Mental Representation of French Verbs.Gustavo L. Estivalet & Fanny E. Meunier - 2015 - Frontiers in Human Neuroscience 9.
  50. Concrete Magnitudes: From Numbers to Time.Christine Falter, Valdas Noreika, Julian Kiverstein & Bruno Mölder - 2009 - Behavioral and Brain Sciences 32 (3-4):335-336.
    Cohen Kadosh & Walsh (CK&W) present convincing evidence indicating the existence of notation-specific numerical representations in parietal cortex. We suggest that the same conclusions can be drawn for a particular type of numerical representation: the representation of time. Notation-dependent representations need not be limited to number but may also be extended to other magnitude-related contents processed in parietal cortex (Walsh 2003).
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