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  1. Mental Imagery.Nigel J. T. Thomas - 2001 - Stanford Encyclopedia of Philosophy.
    Mental imagery (varieties of which are sometimes colloquially refered to as “visualizing,” “seeing in the mind's eye,” “hearing in the head,” “imagining the feel of,” etc.) is quasi-perceptual experience; it resembles perceptual experience, but occurs in the absence of the appropriate external stimuli. It is also generally understood to bear intentionality (i.e., mental images are always images of something or other), and thereby to function as a form of mental representation. Traditionally, visual mental imagery, the most discussed variety, was thought (...)
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  • Subsymbolic Computation and the Chinese Room.David J. Chalmers - 1992 - In J. Dinsmore (ed.), The Symbolic and Connectionist Paradigms: Closing the Gap. Lawrence Erlbaum. pp. 25--48.
    More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that all important aspects of (...)
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  • Prototypes, Location, and Associative Networks (PLAN): Towards a Unified Theory of Cognitive Mapping.Eric Chown, Stephen Kaplan & David Kortenkamp - 1995 - Cognitive Science 19 (1):1-51.
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  • The Hebbian Paradigm Reintegrated: Local Reverberations as Internal Representations.Daniel J. Amit - 1995 - Behavioral and Brain Sciences 18 (4):617-626.
    The neurophysiological evidence from the Miyashita group's experiments on monkeys as well as cognitive experience common to us all suggests that local neuronal spike rate distributions might persist in the absence of their eliciting stimulus. In Hebb's cell-assembly theory, learning dynamics stabilize such self-maintaining reverberations. Quasi-quantitive modeling of the experimental data on internal representations in association-cortex modules identifies the reverberations as the internal code. This leads to cognitive and neurophysiological predictions, many following directly from the language used to describe the (...)
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  • Empirical and Theoretical Active Memory: The Proper Context.Daniel J. Amit - 1995 - Behavioral and Brain Sciences 18 (4):645-657.
    The context of the target article is delimited again, underlining the intended locationof the argument in the bottomup hierarchy of brain study. The central message is that collective delay activity distributions in cortical modules extend the role of a spike to an active memory of structured, learned information that can be carried across long time intervals. Moreover, the population code of the reverberations makes them readable down the cortical processing stream. Most of the critical comments are then interpreted and addressed (...)
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  • What Connectionist Models Learn: Learning and Representation in Connectionist Networks.Stephen José Hanson & David J. Burr - 1990 - Behavioral and Brain Sciences 13 (3):471-489.
    Connectionist models provide a promising alternative to the traditional computational approach that has for several decades dominated cognitive science and artificial intelligence, although the nature of connectionist models and their relation to symbol processing remains controversial. Connectionist models can be characterized by three general computational features: distinct layers of interconnected units, recursive rules for updating the strengths of the connections during learning, and “simple” homogeneous computing elements. Using just these three features one can construct surprisingly elegant and powerful models of (...)
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  • Damn the (Behavioral) Data, Full Steam Ahead.William Prinzmetal & Richard Ivry - 1989 - Behavioral and Brain Sciences 12 (3):413-414.
  • A Solution to the Tag-Assignment Problem for Neural Networks.Gary W. Strong & Bruce A. Whitehead - 1989 - Behavioral and Brain Sciences 12 (3):381-397.
    Purely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions have been demonstrated empirically. Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial (...)
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  • How Do Local Reverberations Achieve Global Integration?J. J. Wright - 1995 - Behavioral and Brain Sciences 18 (4):644-645.
    Amit's Hebbian model risks being overexplanatory, since it does not depend on specific physiological modelling of cortical ANNs, but concentrates on those phenomena which are modelled by a large class of ANNs. While offering a strong demonstration of the presence of Hebb's “cell assemblies,” it does not offer an equal account of Hebb's “phase sequence” concept.
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  • Association and Computation with Cell Assemblies.Frank der van Velde - 1995 - Behavioral and Brain Sciences 18 (4):643-644.
    The cell assembly is an important concept for cognitive psychology. Cognitive processing will to a large extent depend on the relations that can exist between different assemblies. A potential relation between assemblies can already be seen in the occurrence of conditioning. However, the resulting associations between assemblies only produce behavioristic processing or so-called regular computation. Higher-level cognitive abilities most likely result from nonregular computation. I discuss the possibility of this form of computation in terms of cell assemblies.
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  • Reverberations of Hebbian Thinking.Josef P. Rauschecker - 1995 - Behavioral and Brain Sciences 18 (4):642-643.
    Cortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.
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  • How to Decide Whether a Neural Representation is a Cognitive Concept?Maartje E. J. Raijmakers & Peter C. M. Molenaar - 1995 - Behavioral and Brain Sciences 18 (4):641-642.
    A distinction should be made between the formation of stimulus-driven associations and cognitive concepts. To test the learning mode of a neural network, we propose a simple and classic input-output test: the discrimination shift task. Feed-forward PDP models appear to form stimulus-driven associations. A Hopfield network should be extended to apply the test.
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  • The Problems of Cognitive Dynamical Models.Jean Petitot - 1995 - Behavioral and Brain Sciences 18 (4):640-640.
    Amit's “Attractor Neural Network” perspective on cognition raises difficult technical problems already met by prior dynamical models. This commentary sketches briefly some of them concerning the internal topological structure of attractors, the constituency problem, the possibility of activating simultaneously several attractors, and the different kinds of dynamical structures one can use to model brain activity: point attractors, strange attractors, synchronized arrays of oscillators, synfire chains, and so forth.
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  • Local or Transcortical Assemblies? Some Evidence From Cognitive Neuroscience.Friedemann Pulvermüller & Hubert Preissl - 1995 - Behavioral and Brain Sciences 18 (4):640-641.
    Amit defines cell assemblies aslocal cortical neuron populationswith strong internal connections. However, Hebb himself proposed that cell assemblies are distributed over different cortical areas. We review evidence from cognitive neuroscience and neuropsychology supporting the assumption that cell assemblies are transcortical.
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  • Another ANN Model for the Miyashita Experiments.Masahiko Morita - 1995 - Behavioral and Brain Sciences 18 (4):639-640.
    The Miyashita experiments are very interesting and the results should be examined from a viewpoint of attractor dynamics. Amit's target article shows a path toward realistic modeling by artificial neural networks, but it is not necessarily the only one. I introduce another model that can explain a substantial part of the empirical observations and makes an interesting prediction. This model consists of such units that have nonmonotonic input-output characteristics with local inhibition neurons.
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  • Attractors – Don't Get Sucked In.Peter M. Milner - 1995 - Behavioral and Brain Sciences 18 (4):638-639.
    Every immediate memory is unique; it is therefore unlikely to consist of an attractor or even a combination of attractors. In the present state of knowledge about the chemistry of synaptic transmission, there is no reason to look beyond neurons that directly receive sensory afferents for the afterdischarges that correspond to active memories.
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  • An Evolutionary Perspective on Hebb's Reverberatory Representations.David C. Krakauer & Alasdair I. Houston - 1995 - Behavioral and Brain Sciences 18 (4):636-637.
    Hebbian mechanisms are justified according to their functional utility in an evolutionary sense. The selective advantage of correlating content-contingent stimuli reflects the putative common cause of temporally or spatially contiguous inputs. The selective consequences of such correlations are discussed by using examples from the evolution of signal form in sexual selection and model-mimic coevolution. We suggest that evolutionary justification might be considered in addition to neurophysiology plansibility when constructing representational models.
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  • Distributed Cell Assemblies and Detailed Cell Models.Anders Lansner & Erik Fransén - 1995 - Behavioral and Brain Sciences 18 (4):637-638.
    Hebbian cell-assembly theory and attractor networks are good starting points for modeling cortical processing. Detailed cell models can be useful in understanding the dynamics of attractor networks. Cell assemblies are likely to be distributed, with the cortical column as the local processing unit. Synaptic memory may be dominant in all but the first couple of seconds.
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  • The Functional Meaning of Reverberations for Sensoric and Contextual Encoding.Wolfgang Klimesch - 1995 - Behavioral and Brain Sciences 18 (4):636-636.
    Amit argues that the local neuronal spike rate that persists in the absence of the eliciting stimulus represents the code of the eliciting stimulus. Based on the general argument that the inferred functional meaning of reverberation depends in part on the type of representational assumptions, reverberations may only be important for the encoding of contextual information.
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  • Hebb's Accomplishments Misunderstood.Michael Hucka, Mark Weaver & Stephen Kaplan - 1995 - Behavioral and Brain Sciences 18 (4):635-636.
    Amit's efforts to provide stronger theoretical and empirical support for Hebb's cell-assembly concept is admirable, but we have serious reservations about the perspective presented in the target article. For Hebb, the cell assembly was a building block; by contrast, the framework proposed here eschews the need to fit the assembly into a broader picture of its function.
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  • Additional Tests of Amit's Attractor Neural Networks.Ralph E. Hoffman - 1995 - Behavioral and Brain Sciences 18 (4):634-635.
    Further tests of Amit's model are indicated. One strategy is to use the apparent coding sparseness of the model to make predictions about coding sparseness in Miyashita's network. A second approach is to use memory overload to induce false positive responses in modules and biological systems. In closing, the importance of temporal coding and timing requirements in developing biologically plausible attractor networks is mentioned.
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  • Mathematics of Hebbian Attractors.Morris W. Hirsch - 1995 - Behavioral and Brain Sciences 18 (4):633-634.
    The concept of an attractor in a mathematical dynamical system is reviewed. Emphasis is placed on the distinction between a cell assembly, the corresponding attractor, and the attractor dynamics. The biological significance of these entities is discussed, especially the question of whether the representation of the stimulus requires the full attractor dynamics, or merely the cell assembly as a set of reverberating neurons. Comparison is made to Freeman's study of dynamic patterns in olfaction.
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  • Not the Module Does Memory Make – but the Network.Joaquin M. Fuster - 1995 - Behavioral and Brain Sciences 18 (4):631-633.
    This commentary questions the target articles inferences from a limited set of empirical data to support this model and conceptual scheme. Especially questionable is the attribution of internal representation properties to an assembly of cells in a discrete cortical module firing at a discrete attractor frequency. Alternative inferences are drawn from cortical cooling and cell-firing data that point to the internal representation as a broad and specific cortical network defined by cortico-cortical connectivity. Active memory, it is proposed, consists in the (...)
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  • The Hebbian Paradigm Reintegrated: Local Reverberations as Internal Representations.Walter J. Freeman - 1995 - Behavioral and Brain Sciences 18 (4):631-631.
    Recurrent excitation is experimentally well documented in cortical populations. It provides for intracortical excitatory biases that linearize negative feedback interactions and induce macroscopic state transitions during perception. The concept of the local neighborhood should be expanded to spatial patterns as the basis for perception, in which large areas of cortex are bound into cooperative behavior with near-silent columns as important as active columns revealed by unit recording.
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  • How Representation Works is More Important Than What Representations Are.Shimon Edelman - 1995 - Behavioral and Brain Sciences 18 (4):630-631.
    A theory of representation is incomplete if it states “representations areX” whereXcan be symbols, cell assemblies, functional states, or the flock of birds fromTheaetetus, without explaining the nature of the link between the universe ofXs and the world. Amit's thesis, equating representations with reverberations in Hebbian cell assemblies, will only be considered a solution to the problem of representation when it is complemented by a theory of how a reverberation in the brain can be a representation of anything.
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  • Reverberation Reconsidered: On the Path to Cognitive Theory.Eric Chown - 1995 - Behavioral and Brain Sciences 18 (4):628-629.
    Amit's work addresses a critical issue in cognitive science: the structure of neural representations. The use of Hebbian cell assemblies is a positive step, and we now need to consider its role in a larger cognitive theory. When considering the dynamics of a system built out of attractors, a more limited version of reverberation becomes necessary.
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  • What's in a Cell Assembly?G. J. Dalenoort & P. H. de Vries - 1995 - Behavioral and Brain Sciences 18 (4):629-630.
    The cell assembly as a simple attractor cannot explain many cognitive phenomena. It must be a highly structured network that can sustain highly structured excitation patterns. Moreover, a cell assembly must be more widely distributed in space than on a square millimeter.
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  • Where the Adventure Is.Elie Bienenstock & Stuart Geman - 1995 - Behavioral and Brain Sciences 18 (4):627-628.
    Interpreting the Miyashita et al. experiments in terms of a cellassembly representation does not adequately explain the performance of Miyashita's monkeys on novel stimuli. We will argue that the latter observations point to acompositionalrepresentation and suggest a dynamics involving rapid and reversible binding of distinct activity patterns.
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  • Are Single-Cell Data Sufficient for Testing Neural Network Models?Ehud Ahissar - 1995 - Behavioral and Brain Sciences 18 (4):626-627.
    Persistent activity can be the product of mechanisms other than attractor reverberations. The single-unit data presented by Amit cannot discriminate between the different mechanisms. In fact, single-unit data do not appear to be adequate for testing neural network models.
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  • Is Extension to Perception of Real-World Objects and Scenes Possible?J. Wagemans, K. Verfaillie, P. De Graef & K. Lamberts - 1989 - Behavioral and Brain Sciences 12 (3):415-417.
  • Perceptual Symbol Systems.Lawrence W. Barsalou - 1999 - Behavioral and Brain Sciences 22 (4):577-660.
    Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...)
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  • Codes and Their Vicissitudes.Bernhard Hommel, Jochen Müsseler, Gisa Aschersleben & Wolfgang Prinz - 2001 - Behavioral and Brain Sciences 24 (5):910-926.
    First, we discuss issues raised with respect to the Theory of Event Coding (TEC)'s scope, that is, its limitations and possible extensions. Then, we address the issue of specificity, that is, the widespread concern that TEC is too unspecified and, therefore, too vague in a number of important respects. Finally, we elaborate on our views about TEC's relations to other important frameworks and approaches in the field like stages models, ecological approaches, and the two-visual-pathways model. Footnotes1 We acknowledge the precedence (...)
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  • On Learnability, Empirical Foundations, and Naturalness.W. J. M. Levelt - 1990 - Behavioral and Brain Sciences 13 (3):501-501.
  • Learning and Representation: Tensions at the Interface.Steven José Hanson - 1990 - Behavioral and Brain Sciences 13 (3):511-518.
  • Connectionist Models Learn What?Timothy van Gelder - 1990 - Behavioral and Brain Sciences 13 (3):509-510.
  • Connectionist Learning and the Challenge of Real Environments.Mark Weaver & Stephen Kaplan - 1990 - Behavioral and Brain Sciences 13 (3):510-511.
  • Advances in Neural Network Theory.Gérard Toulouse - 1990 - Behavioral and Brain Sciences 13 (3):509-509.
  • Connectionist Models: Too Little Too Soon?William Timberlake - 1990 - Behavioral and Brain Sciences 13 (3):508-509.
  • There is More to Learning Then Meeth the Eye.Noel E. Sharkey - 1990 - Behavioral and Brain Sciences 13 (3):506-507.
  • Problems of Extension, Representation, and Computational Irreducibility.Patrick Suppes - 1990 - Behavioral and Brain Sciences 13 (3):507-508.
  • The Analysis of the Learning Needs to Be Deeper.John E. Rager - 1990 - Behavioral and Brain Sciences 13 (3):505-506.
  • Learning From Learned Networks.M. Pavel - 1990 - Behavioral and Brain Sciences 13 (3):503-504.
  • Realistic Neural Nets Need to Learn Iconic Representations.W. A. Phillips, P. J. B. Hancock & L. S. Smith - 1990 - Behavioral and Brain Sciences 13 (3):505-505.
  • Keeping Representations at Bay.Stanley Munsat - 1990 - Behavioral and Brain Sciences 13 (3):502-503.
  • Toward a Unification of Conditioning and Cognition in Animal Learning.William S. Maki - 1990 - Behavioral and Brain Sciences 13 (3):501-502.
  • Approaches to Learning and Representation.Pat Langley - 1990 - Behavioral and Brain Sciences 13 (3):500-501.
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  • What Can Psychologists Learn From Hidden-Unit Nets?K. Lamberts & G. D'Ydewalle - 1990 - Behavioral and Brain Sciences 13 (3):499-500.
  • How Connectionist Models Learn: The Course of Learning in Connectionist Networks.John K. Kruschke - 1990 - Behavioral and Brain Sciences 13 (3):498-499.
  • But What is the Substance of Connectionist Representation?James Hendler - 1990 - Behavioral and Brain Sciences 13 (3):496-497.
  • A Non-Empiricist Perspective on Learning in Layered Networks.Michael I. Jordan - 1990 - Behavioral and Brain Sciences 13 (3):497-498.