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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. David B. Adams (1979). Brain Mechanisms for Offense, Defense, and Submission. Behavioral and Brain Sciences 2 (2):201-213.
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  2. David B. Adams (1979). Motivational Systems, Motivational Mechanisms, and Aggression. Behavioral and Brain Sciences 2 (2):230-241.
  3. Kathleen Akins (1996). Of Sensory Systems and the "Aboutness" of Mental States. 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.
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  4. Alessandro Antonietti (2010). Do Neurobiological Data Help Us to Understand Economic Decisions Better? 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 (...)
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  5. James B. Ashbrook (1997). "Mind" as Humanizing the Brain: Toward a Neurotheology of Meaning. Zygon 32 (3):301-320.
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  6. Harald Atmanspacher, The Significance of Causally Coupled, Stable Neuronal Assemblies for the Psychological Time Arrow.
    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 (...)
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  7. Harald Atmanspacher, Interpreting Neurodynamics: Concepts and Facts.
    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, (...)
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  8. Werner Backhaus (ed.) (2001). Neuronal Coding of Perceptual Systems. World Scientific.
  9. I. C. Baianu, R. Brown, G. Georgescu & J. F. Glazebrook (2006). Complex Non-Linear Biodynamics in Categories, Higher Dimensional Algebra and Łukasiewicz–Moisil Topos: Transformations of Neuronal, Genetic and Neoplastic Networks. [REVIEW] 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 (...)
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  10. Arunava Banerjee (2001). The Roles Played by External Input and Synaptic Modulations in the Dynamics of Neuronal Systems. 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.
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  11. C. Philip Beaman (2000). Neurons Amongst the Symbols? 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.
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  12. William P. Bechtel, Pete Mandik, Jennifer Mundale & Robert S. Stufflebeam (eds.) (2001). Philosophy and the Neurosciences: A Reader. 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.
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  13. Giorgio Bonmassar & Eric L. Schwartz (1998). Representation is Space-Variant. 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 (...)
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  14. Roman Borisyuk (2001). The Puzzle of Chaotic Neurodynamics. 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.
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  15. Denny Borsboom & Ingmar Visser (2008). Semantic Cognition or Data Mining? 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.
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  16. Olaf Breidbach (1999). Internal Representations--A Prelude for Neurosemantics. Journal of Mind and Behavior 20 (4):403-419.
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  17. Andrew Brook & Kathleen Akins (eds.) (2005). Cognition and the Brain: The Philosophy and Neuroscience Movement. 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 (...)
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  18. Richard Brown (2006). What is a Brain State? 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 (...)
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  19. Silvia A. Bunge & Michael J. Souza (2008). Neural Representations Used to Specify Action. In Silvia A. Bunge & Jonathan D. Wallis (eds.), Neuroscience of Rule-Guided Behavior. Oxford University Press.
  20. Massimiliano Cappuccio (2009). Constructing the Space of Action: From Bio-Robotics to Mirror Neurons. 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.
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  21. Antonella Carassa & Maurizio Tirassa (1994). Representational Redescription and Cognitive Architectures. 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.
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  22. Anthony Chemero & Michael T. Turvey (2011). Philosophy for the Rest of Cognitive Science. Topics in Cognitive Science 3 (2):425-437.
    Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel's (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically-oriented cognitive scientists. As an example of non-representational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp and Turvey 2009). We then propose a philosophy of science appropriate to non-representational, dynamical cognitive (...)
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  23. Kalina Christoff & Kamyar Keramatian (2008). Abstraction of Mental Representations : Theoretical Considerations and Neuroscientific Evidence. In Silvia A. Bunge & Jonathan D. Wallis (eds.), Neuroscience of Rule-Guided Behavior. Oxford University Press.
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  24. Patricia S. Churchland & Terrence J. Sejnowski (1989). Neural Representation and Neural Computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press. 343-382.
  25. Paul M. Churchland (1986). Cognitive Neurobiology: A Computational Hypothesis for Laminar Cortex. [REVIEW] 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: (...)
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  26. Andy Clark, Philosophical Issues in Brain Theory.
    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 (...)
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  27. Axel Cleeremans, A a A.
    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 (...)
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  28. Axel Cleeremans (2008). Consciousness: The Radical Plasticity Thesis. 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 (...)
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  29. D. Cliff (1990). Computational Neuroethology: A Provisional Manifesto. 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.
  30. Mike Collins (2009). The Nature and Implementation of Representation in Biological Systems. 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 (...)
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  31. Matteo Colombo (2010). How Authentic Intentionality Can Be Enabled: A Neurocomputational Hypothesis. [REVIEW] 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 (...)
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  32. Jeff Coulter (1995). The Informed Neuron: Issues in the Use of Information Theory in the Behavioral Sciences. [REVIEW] 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 (...)
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  33. Terrence W. Deacon (2005). Language as an Emergent Function: Some Radical Neurological and Evolutionary Implications. 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 (...)
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  34. Guy Dove (2011). On the Need for Embodied and Dis-Embodied Cognition. Frontiers in Psychology 1 (242):1-13.
    This essay proposes and defends a pluralistic theory of conceptual embodiment. Our concepts are represented in at least two ways: (i) through sensorimotor simulations of our interactions with objects and events and (ii) through sensorimotor simulations of natural language processing. Linguistic representations are “dis-embodied” in the sense that they are dynamic and multimodal but, in contrast to other forms of embodied cognition, do not inherit semantic content from this embodiment. The capacity to store information in the associations and inferential relationships (...)
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  35. Hubert L. Dreyfus (2002). Refocusing the Question: Can There Be Skillful Coping Without Propositional Representations or Brain Representations? [REVIEW] Phenomenology and the Cognitive Sciences 1 (4):413-25.
  36. Shimon Edelman (2002). Constraining the Neural Representation of the Visual World. Trends in Cognitive Sciences 6 (3):125-131.
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  37. Thomas Elbert, Christian Dobell, Alessandro Angrilli, Luciano Stegagno & Brigitte Rockstroh (1999). Word Versus Task Representation in Neural Networks. 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.
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  38. Crawford L. Elder (1998). What Sensory Signals Are About. 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 (...)
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  39. Chris Eliasmith (2005). A New Perspective on Representational Problems. Journal of Cognitive Science 6:97-123.
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  40. Chris Eliasmith (2000). How Neurons Mean: A Neurocomputational Theory of Representational Content. 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.
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  41. Christine Falter, Valdas Noreika, Julian Kiverstein & Bruno Mölder (2009). Concrete Magnitudes: From Numbers to Time. 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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  42. Oleg V. Favorov & Dan Ryder, Sinbad: A Neocortical Mechanism for Discovering Environmental Variables and Regularities Hidden in Sensory Input.
    We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one, since their existence is only indirectly (...)
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  43. Tomer Fekete (2010). Representational Systems. 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 (...)
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  44. Tomer Fekete & Shimon Edelman (2011). Towards a Computational Theory of Experience. Consciousness and Cognition 20 (3):807-827.
    A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must (...)
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  45. Walter J. Freeman (1997). Nonlinear Neurodynamics of Intentionality. Journal of Mind and Behavior 18 (2-3):291-304.
  46. Angela D. Friederici & D. Yves von Cramon (2000). Syntax in the Brain: Linguistic Versus Neuroanatomical Specificity. Behavioral and Brain Sciences 23 (1):32-33.
    We criticize the lack of neuroanatomical precision in the Grodzinsky target article. We propose a more precise neuroanatomical characterization of syntactic processing and suggest that syntactic procedures are supported by the left frontal operculum in addition to the anterior part of the superior temporal gyrus, which appears to be associated with syntactic knowledge representation.
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  47. Vittorio Gallese & Corrado Sinigaglia (2011). What is so Special About Embodied Simulation? Trends in Cognitive Sciences 15 (11):512-519.
    Simulation theories of social cognition abound in the literature, but it is often unclear what simulation means and how it works. The discovery of mirror neurons, responding both to action execution and observation, suggested an embodied approach to mental simulation. Over the last years this approach has been hotly debated and alternative accounts have been proposed. We discuss these accounts and argue that they fail to capture the uniqueness of embodied simulation (ES). ES theory provides a unitary account of basic (...)
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  48. Justin Garson (2003). The Introduction of Information Into Neurobiology. Philosophy of Science 70 (5):926-936.
    The first use of the term “information” to describe the content of nervous impulse occurs in Edgar Adrian's The Basis of Sensation (1928). What concept of information does Adrian appeal to, and how can it be situated in relation to contemporary philosophical accounts of the notion of information in biology? The answer requires an explication of Adrian's use and an evaluation of its situation in relation to contemporary accounts of semantic information. I suggest that Adrian's concept of information can be (...)
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  49. Justin Garson (2003). The Introduction of Information Into Neurobiology. Philosophy of Science 70 (5):926-936.
    The first use of the term "information" to describe the content of nervous impulse occurs 20 years prior to Shannon`s (1948) work, in Edgar Adrian`s The Basis of Sensation (1928). Although, at least throughout the 1920s and early 30s, the term "information" does not appear in Adrian`s scientific writings to describe the content of nervous impulse, the notion that the structure of nervous impulse constitutes a type of message subject to certain constraints plays an important role in all of his (...)
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  50. Daniel Gilman (1999). Network Stability and Consciousness? Behavioral and Brain Sciences 22 (1):155-156.
    A connectionist vehicle theory of consciousness needs to disambiguate its criteria for identifying the relevant vehicles. Moreover, a vehicle theory may appear entirely arbitrary in sorting between what are typically thought of as conscious and unconscious processes.
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