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  1. Kenneth Aizawa (1999). Jeffrey L. Elman, Elizabeth A. Bates, Mark H. Johnson, Annette Karmiloff-Smith, Domenico Parisi, and Kim Plunkett, (Eds.), Rethinking Innateness: A Connectionist Perspective on Development, Neural Network Modeling and Connectionism Series and Kim Plunkett and Jeffrey L. Elman, Exercises in Rethinking Innateness: A Handbook for Connectionist Simulations. [REVIEW] Minds and Machines 9 (3):447-456.
  2. John R. Anderson & Christian Lebiere (2003). Optimism for the Future of Unified Theories. Behavioral and Brain Sciences 26 (5):628-633.
    The commentaries on our article encourage us to believe that researchers are beginning to take seriously the goal of achieving the broad adequacy that Newell aspired to. The commentators offer useful elaborations to the criteria we suggested for the Newell Test. We agree with many of the commentators that classical connectionism is too restrictive to achieve this broad adequacy, and that other connectionist approaches are not so limited and can deal with the symbolic components of thought. All these approaches, including (...)
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  3. John R. Anderson, Christian Lebiere, Marsha Lovett & Lynne Reder (1998). ACT-R: A Higher-Level Account of Processing Capacity. Behavioral and Brain Sciences 21 (6):831-832.
    We present an account of processing capacity in the ACT-R theory. At the symbolic level, the number of chunks in the current goal provides a measure of relational complexity. At the subsymbolic level, limits on spreading activation, measured by the attentional parameter W, provide a theory of processing capacity, which has been applied to performance, learning, and individual differences data.
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  4. Daniel Andler (1992). From Paleo to Neo Connectionism. In G. van der Vijve (ed.), New Perspectives on Cybernetics.
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  5. Louise Antony (1991). A Pieced Quilt: A Critical Discussion of Stephen Schiffer'sRemnants of Meaning. Philosophical Psychology 4 (1):119-137.
    Abstract Stephen Schiffer, in his recent book, Remnants of Meaning, argues against the possibility of any compositional theory of meaning for natural language. Because the argument depends on the premise that there is no possible naturalistic reduction of the intentional to the physical, Schiffer's attack on theories of meaning is of central importance for theorists of mind. I respond to Schiffer's argument by showing that there is at least one reductive account of the mental that he has neglected to consider?the (...)
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  6. William Bechtel (2009). Looking Down, Around, and Up: Mechanistic Explanation in Psychology. Philosophical Psychology 22 (5):543-564.
    Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...)
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  7. William Bechtel (1988). Connectionism and the Philosophy of Mind: An Overview. Southern Journal of Philosophy 26 (S1):17-41.
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  8. Istvan S. N. Berkeley (2008). What the is a Symbol? Minds and Machines 18 (1).
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give (...)
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  9. Istvan S. N. Berkeley (2001). Peter Novak, Mental Symbols: A Defence of the Classical Theory of Mind. Studies in Cognitive Systems 19, Dordrecht, Netherlands: Kluwer Academic Publishers, 1997, XXII + 266 Pp., $114.00, ISBN 0-7923-4370-. [REVIEW] Minds and Machines 11 (1):148-150.
  10. David Bohm (1990). A New Theory of the Relationship of Mind and Matter. Philosophical Psychology 3 (2 & 3):271 – 286.
    The relationship of mind and matter is approached in a new way in this article. This approach is based on the causal interpretation of the quantum theory, in which an electron, for example, is regarded as an inseparable union of a particle and afield. This field has, however, some new properties that can be seen to be the main sources of the differences between the quantum theory and the classical (Newtonian) theory. These new properties suggest that the field may be (...)
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  11. Morten H. Christiansen, Christopher M. Conway & Michelle R. Ellefson (2002). Raising the Bar for Connectionist Modeling of Cognitive Developmental Disorders. Behavioral and Brain Sciences 25 (6):752-753.
    Cognitive developmental disorders cannot be properly understood without due attention to the developmental process, and we commend the authors’simulations in this regard. We note the contribution of these simulations to the nascent field of connectionist modeling of developmental disorders and outline a set of criteria for assessing individual models in the hope of furthering future modeling efforts.
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  12. Paul M. Churchland (1995). Machine Stereopsis: A Feedforward Network for Fast Stereo Vision with Movable Fusion Plane. In Android Epistemology. Cambridge: MIT Press.
  13. Axel Cleeremans, Applying Forward Models to Sequence Learning: A Connectionist Implementation.
    The ability to process events in their temporal and sequential context is a fundamental skill made mandatory by constant interaction with a dynamic environment. Sequence learning studies have demonstrated that subjects exhibit detailed — and often implicit — sensitivity to the sequential structure of streams of stimuli. Current connectionist models of performance in the so-called Serial Reaction Time Task (SRT), however, fail to capture the fact that sequence learning can be based not only on sensitivity to the sequential associations between (...)
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  14. Chris Code (1999). Re-Assembling the Brain: Are Cell Assemblies the Brain's Language for Recovery of Function? Behavioral and Brain Sciences 22 (2):284-284.
    Holistically ignited Hebbian models are fundamentally different from the serially organized connectionist implementations of language. This may be important for the recovery of language after injury, because connectionist models have provided useful insights into recovery of some cognitive functions. I ask whether cell assembly modelling can make an important contribution and whether the apparent incompatibility with successful connectionist modelling is a problem.
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  15. Roberto Cordeschi (2000). Early-Connectionism Machines. Artificial Intelligence and Society 14 (3-4):314-330.
    In this paper I put forward a reconstruction of the evolution of certain explanatory hypotheses on the neural basis of association and learning that are the premises of connectionism in the cybernetic age and of present-day connectionism. The main point of my reconstruction is based on two little-known case studies. The first is the project, published in 1913, of a hydraulic machine through which its author believed it was possible to simulate certain essential elements of the plasticity of nervous connections. (...)
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  16. Joseph Cruz, Connectionism.
    Although the scientific study of the mind as a distinct discipline has been around for only a short time, there are already rumblings of a fundamental change in view about what the mind is like. At the center of this controversy is a cluster of approaches that are together variously called connectionism, neural network modeling, parallel distributed processing (PDP), or dynamic systems theory. This course is an intensive introduction to the connectionist's proposals for thinking about the mind and understanding its (...)
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  17. Lindley Darden (2002). Strategies for Discovering Mechanisms: Schema Instantiation, Modular Subassembly, Forward/Backward Chaining. Proceedings of the Philosophy of Science Association 2002 (3):S354-S365.
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  18. M. R. W. Dawson, D. A. Medler, D. B. McCaughan, L. Willson & M. Carbonaro (2000). Using Extra Output Learning to Insert a Symbolic Theory Into a Connectionist Network. Minds and Machines 10 (2):171-201.
    This paper examines whether a classical model could be translated into a PDP network using a standard connectionist training technique called extra output learning. In Study 1, standard machine learning techniques were used to create a decision tree that could be used to classify 8124 different mushrooms as being edible or poisonous on the basis of 21 different Features (Schlimmer, 1987). In Study 2, extra output learning was used to insert this decision tree into a PDP network being trained on (...)
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  19. Michael R. W. Dawson & Corinne Zimmerman (2003). Interpreting the Internal Structure of a Connectionist Model of the Balance Scale Task. Brain and Mind 4 (2):129-149.
    One new tradition that has emerged from early research on autonomous robots is embodied cognitive science. This paper describes the relationship between embodied cognitive science and a related tradition, synthetic psychology. It is argued that while both are synthetic, embodied cognitive science is antirepresentational while synthetic psychology still appeals to representations. It is further argued that modern connectionism offers a medium for conducting synthetic psychology, provided that researchers analyze the internal representations that their networks develop. The paper then provides a (...)
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  20. David DeMoss (2003). Connectionist Agency. Philosophy in the Contemporary World 10 (2):9-15.
    Any mind-brain theory eventually will have to deal with agency. I do not claim that no other theory could do this successfully. I do claim that connectionism is able to handle some key features of agency. First, I will offer a brief account of connectionism and the advantages of using it to account for human agency, comparing and contrasting connectionism with two other mind-brain accounts in cognitive science, symbolicism and dynamicism. Then, since a connectionist account of agency depends on a (...)
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  21. Donelson E. Dulany (1999). Consciousness, Connectionism, and Intentionality. Behavioral and Brain Sciences 22 (1):154-155.
    Connectionism can provide useful theories in which consciousness is the exclusive vehicle of explicit representation. The theories may not, however, handle some phenomena adequately: sense of agency, modes and contents of awareness, propositional and deliberative thought, metacognitive awareness and consciousness of self. They should, however, be useful in describing automatic, activational relations among nonpropositional conscious contents.
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  22. Kevan Edwards (2011). Higher-Level Concepts and Their Heterogeneous Implementations: A Polemical Review of Edouard Machery's Doing Without Concepts. Philosophical Psychology 24 (1):119-133.
    Doing Without Concepts Edouard Machery New York: Oxford University Press, 2009 296 pages, ISBN: 0195306880 (hbk); $65.00 This paper offers a critical review of Edouard Machery's Doing Without Concepts, with a particular emphasis on an approach to concept individuation that is consistent with many of Machery's arguments but has the potential to avoid his eliminativist conclusion. The approach agrees with Machery's claims to the effect that prototypes, exemplars, theories (and so on) form a heterogeneous class, but construes these theoretical entities (...)
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  23. J. Richard Eiser (1998). The Dynamical Hypothesis in Social Cognition. Behavioral and Brain Sciences 21 (5):638-638.
    Research in attitudes and social cognition exemplifies van Gelder's distinction between the computational and dynamical approaches. The former emphasizes linear measurement and rational decision-making. The latter considers processes of associative memory and self-organization in attitude formation and social influence. The study of dynamical processes in social cognition has been facilitated by connectionist approaches to computation.
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  24. Malcolm R. Forster (1999). How Do Simple Rules `Fit to Reality' in a Complex World? Minds and Machines 9 (4):543-564.
    The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that they `fit to reality.' (...)
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  25. Malcolm Forster & Eric Saidel (1994). Connectionism and the Fate of Folk Psychology: A Reply to Ramsey, Stich and Garon. Philosophical Psychology 7 (4):437 – 452.
    Ramsey, Stick and Garon (1991) argue that if the correct theory of mind is some parallel distributed processing theory, then folk psychology must be false. Their idea is that if the nodes and connections that encode one representation are causally active then all representations encoded by the same set of nodes and connections are also causally active. We present a clear, and concrete, counterexample to RSG's argument. In conclusion, we suggest that folk psychology and connectionism are best understood as complementary (...)
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  26. James Franklin & S. W. K. Chan (1998). Symbolic Connectionism in Natural Language Disambiguation. IEEE Transactions on Neural Networks 9:739-755.
    Uses connectionism (neural networks) to extract the "gist" of a story in order to represent a context going forward for the disambiguation of incoming words as a text is processed.
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  27. Stan Franklin & Max Garzon (1992). On Stability and Solvability (or, When Does a Neural Network Solve a Problem?). Minds and Machines 2 (1):71-83.
    The importance of the Stability Problem in neurocomputing is discussed, as well as the need for the study of infinite networks. Stability must be the key ingredient in the solution of a problem by a neural network without external intervention. Infinite discrete networks seem to be the proper objects of study for a theory of neural computability which aims at characterizing problems solvable, in principle, by a neural network. Precise definitions of such problems and their solutions are given. Some consequences (...)
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  28. Robert M. French & Elizabeth Thomas (2000). Why Localist Connectionist Models Are Inadequate for Categorization. Behavioral and Brain Sciences 23 (4):477-477.
    Two categorization arguments pose particular problems for localist connectionist models. The internal representations of localist networks do not reflect the variability within categories in the environment, whereas networks with distributed internal representations do reflect this essential feature of categories. We provide a real biological example of perceptual categorization in the monkey that seems to require population coding (i.e., distributed internal representations).
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  29. James Garson, Connectionism. Stanford Encyclopedia of Philosophy.
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  30. Francisco Calvo Garzóan (2003). Connectionist Semantics and the Collateral Information Challenge. Mind and Language 18 (1):77–94.
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  31. Francisco Calvo Garzón (2003). Nonclassical Connectionism Should Enter the Decathlon. Behavioral and Brain Sciences 26 (5):603-604.
    In this commentary I explore nonclassical connectionism (NCC) as a coherent framework for evaluation in the spirit of the Newell Test. Focusing on knowledge integration, development, real-time performance, and flexible behavior, I argue that NCC's “within-theory rank ordering” would place subsymbolic modeling in a better position. Failure to adopt a symbolic level of thought cannot be interpreted as a weakness.
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  32. Ross W. Gayler (2006). Vector Symbolic Architectures Are a Viable Alternative for Jackendoff's Challenges. Behavioral and Brain Sciences 29 (1):78-79.
    The authors, on the basis of brief arguments, have dismissed tensor networks as a viable response to Jackendoff's challenges. However, there are reasons to believe that connectionist approaches descended from tensor networks are actually very well suited to answering Jackendoff's challenges. I rebut their arguments for dismissing tensor networks and briefly compare the approaches.
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  33. Petros A. M. Gelepithis (2003). Criteria and Evaluation of Cognitive Theories. Behavioral and Brain Sciences 26 (5):607-609.
    I have three types of interrelated comments. First, on the choice of the proposed criteria, I argue against any list and for a system of criteria. Second, on grading, I suggest modifications with respect to consciousness and development. Finally, on the choice of “theories” for evaluation, I argue for Edelman's theory of neuronal group selection instead of connectionism (classical or not).
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  34. Christopher D. Green (2001). Scientific Models, Connectionist Networks, and Cognitive Science. .
    The employment of a particular class of computer programs known as "connectionist networks" to model mental processes is a widespread approach to research in cognitive science these days. Little has been written, however, on the precise connection that is thought to hold between such programs and actual in vivo cognitive processes such that the former can be said to "model" the latter in a scientific sense. What is more, this relation can be shown to be problematic. In this paper I (...)
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  35. Christopher D. Green, Are Connectionist Models Theories of Cognition?
    This paper explores the question of whether connectionist models of cognition should be considered to be scientific theories of the cognitive domain. It is argued that in traditional scientific theories, there is a fairly close connection between the theoretical (unobservable) entities postulated and the empirical observations accounted for. In connectionist models, however, hundreds of theoretical terms are postulated -- viz., nodes and connections -- that are far removed from the observable phenomena. As a result, many of the features of any (...)
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  36. Christopher D. Green & John Vervaeke, What Kind of Explanation, If Any, is a Connectionist Net?
    Connectionist models of cognition are all the rage these days. They are said to provide better explanations than traditional symbolic computational models in a wide array of cognitive areas, from perception to memory to language to reasoning to motor action. But what does it actually mean to say that they "explain" cognition at all? In what sense do the dozens of nodes and hundreds of connections in a typical connectionist network explain anything? It is the purpose of this paper to (...)
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  37. Stephen Grossberg (2003). Bring ART Into the ACT. Behavioral and Brain Sciences 26 (5):610-611.
    ACT is compared with a particular type of connectionist model that cannot handle symbols and use nonbiological operations which do not learn in real time. This focus continues an unfortunate trend of straw man debates in cognitive science. Adaptive Resonance Theory, or ART-neural models of cognition can handle both symbols and subsymbolic representations, and meet the Newell criteria at least as well as connectionist models.
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  38. Rick Grush, Blending in Language, Conceptual Structure, and the Cerebral Cortex.
    0. Introduction The past decade has seen Cognitive Linguistics (CL) emerge as an important, exciting and promising theoretical alternative to Chomskyan approaches to the study of language. Even so, sheer numbers and institutional inertia make it the case that most current neurolinguistic research either assumes that the Chomskyan formalist story is more or less correct (and thus that the task of neurolinguistics is to determine how the brain implements GB, for instance), or that the there are two possibilities, Chomskyanism or (...)
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  39. Stevan Harnad, Grounding Symbols in the Analog World with Neural Nets a Hybrid Model.
    1.1 The predominant approach to cognitive modeling is still what has come to be called "computationalism" (Dietrich 1990, Harnad 1990b), the hypothesis that cognition is computation. The more recent rival approach is "connectionism" (Hanson & Burr 1990, McClelland & Rumelhart 1986), the hypothesis that cognition is a dynamic pattern of connections and activations in a "neural net." Are computationalism and connectionism really deeply different from one another, and if so, should they compete for cognitive hegemony, or should they collaborate? These (...)
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  40. Michael Harré & Allan Snyder (2012). Intuitive Expertise and Perceptual Templates. Minds and Machines 22 (3):167-182.
    We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both (...)
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  41. Margaret Harris (1998). Can Connectionism Model Developmental Change? Mind and Language 13 (4):576–581.
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  42. Terence Horgan & John Tienson (1998). Resisting the Tyranny of Terminology: The General Dynamical Hypothesis in Cognitive Science. Behavioral and Brain Sciences 21 (5):643-643.
    What van Gelder calls the dynamical hypothesis is only a special case of what we here dub the general dynamical hypothesis. His terminology makes it easy to overlook important alternative dynamical approaches in cognitive science. Connectionist models typically conform to the general dynamical hypothesis, but not to van Gelder's.
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  43. Lynn Huestegge, Jonathan Grainger & Ralph Radach (2003). Visual Word Recognition and Oculomotor Control in Reading. Behavioral and Brain Sciences 26 (4):487-488.
    A central component in the E-Z Reader model is a two-stage word processing mechanism made responsible for both the triggering of eye movements and sequential shifts of attention. We point to problems with both the verbal description of this mechanism and its computational implementation in the simulation. As an alternative, we consider the use of a connectionist processing module in combination with a more indirect form of cognitive eye-movement control.
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  44. John E. Hummel (2010). Symbolic Versus Associative Learning. Cognitive Science 34 (6):958-965.
    Ramscar and colleagues (2010, this volume) describe the “feature-label-order” (FLO) effect on category learning and characterize it as a constraint on symbolic learning. I argue that FLO is neither a constraint on symbolic learning in the sense of “learning elements of a symbol system” (instead, it is an effect on nonsymbolic, association learning) nor is it, more than any other constraint on category learning, a constraint on symbolic learning in the sense of “solving the symbol grounding problem.”.
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  45. J.Ü, Rgen SchrÖ & der (1999). What has Consciousness to Do with Explicit Representations and Stable Activation Vectors? Behavioral and Brain Sciences 22 (1):166-167.
    To assess O'Brien & Opie's connectionist vehicle theory of consciousness, (1) it is not enough to point to the methodological weakness of certain experiments (dichotic listening, etc.). Successful cognitive theories postulating explicit unconscious representations have to be taken into account as well. (2) The distinction between vehicle and process theories cannot be drawn in the way envisaged by the authors because a representation's explicitness depends not only on its structural but also on its processing properties. (3) The stability of an (...)
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  46. Anuenue Kukona & Whitney Tabor (2011). Impulse Processing: A Dynamical Systems Model of Incremental Eye Movements in the Visual World Paradigm. Cognitive Science 35 (6):1009-1051.
    The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a (...)
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  47. Aarre Laakso & Paco Calvo (2011). How Many Mechanisms Are Needed to Analyze Speech? A Connectionist Simulation of Structural Rule Learning in Artificial Language Acquisition. Cognitive Science 35 (7):1243-1281.
    Some empirical evidence in the artificial language acquisition literature has been taken to suggest that statistical learning mechanisms are insufficient for extracting structural information from an artificial language. According to the more than one mechanism (MOM) hypothesis, at least two mechanisms are required in order to acquire language from speech: (a) a statistical mechanism for speech segmentation; and (b) an additional rule-following mechanism in order to induce grammatical regularities. In this article, we present a set of neural network studies demonstrating (...)
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  48. R. C. Lacher (1993). Expert Networks: Paradigmatic Conflict, Technological Rapproachement. [REVIEW] Minds and Machines 3 (1):53-71.
    A rule-based expert system is demonstrated to have both a symbolic computational network representation and a sub-symbolic connectionist representation. These alternate views enhance the usefulness of the original system by facilitating introduction of connectionist learning methods into the symbolic domain. The connectionist representation learns and stores metaknowledge in highly connected subnetworks and domain knowledge in a sparsely connected expert network superstructure. The total connectivity of the neural network representation approximates that of real neural systems and hence avoids scaling and memory (...)
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  49. András Lörincz, Barnabás Póczos, Gábor Szirtes & Bálint Takács (2002). Ockham's Razor at Work: Modeling of the ``Homunculus''. [REVIEW] Brain and Mind 3 (2):187-220.
    There is a broad consensus about the fundamental role of thehippocampal system (hippocampus and its adjacent areas) in theencoding and retrieval of episodic memories. This paper presents afunctional model of this system. Although memory is not asingle-unit cognitive function, we took the view that the wholesystem of the smooth, interrelated memory processes may have acommon basis. That is why we follow the Ockham's razor principleand minimize the size or complexity of our model assumption set.The fundamental assumption is the requirement of (...)
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  50. Albert Low (2005). What is Consciousness and has It Evolved? World Futures 61 (3):199 – 227.
    Research into consciousness has now become respectable, and much has been written about it. Is consciousness the exclusive property of human beings, or can it be found also in animals? Can machines become conscious? Is consciousness an illusion, and are all mental states ultimately reducible to the movement of molecules? If consciousness is other than matter, what connection does it have with matter? These and others like them are now serious scientific questions in the West. This article discusses consciousness within (...)
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