Search results for 'Neural' (try it on Scholar)

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  1. David J. Chalmers (2000). What is a Neural Correlate of Consciousness? In Thomas Metzinger (ed.), Neural Correlates of Consciousness. MIT Press. 17--39.score: 21.0
    The search for neural correlates of consciousness (or NCCs) is arguably the cornerstone in the recent resurgence of the science of consciousness. The search poses many difficult empirical problems, but it seems to be tractable in principle, and some ingenious studies in recent years have led to considerable progress. A number of proposals have been put forward concerning the nature and location of neural correlates of consciousness. A few of these include.
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  2. Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. Neural Networks 21 (2-3):311-321.score: 21.0
    I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend
    the following theses. (1) Many neural networks compute—they perform computations. (2) Some neural networks compute in a classical way.
    Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other neural networks
    compute in a non-classical way. (4) Yet other neural networks do not perform computations. Brains may well (...)
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  3. Alva Noë & Evan Thompson (2004). Are There Neural Correlates of Consciousness? Journal of Consciousness Studies 11 (1):3-28.score: 18.0
    In the past decade, the notion of a neural correlate of consciousness (or NCC) has become a focal point for scientific research on consciousness (Metzinger, 2000a). A growing number of investigators believe that the first step toward a science of consciousness is to discover the neural correlates of consciousness. Indeed, Francis Crick has gone so far as to proclaim that ‘we … need to discover the neural correlates of consciousness.… For this task the primate visual system seems (...)
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  4. Bernard Molyneux (2010). Why the Neural Correlates of Consciousness Cannot Be Found. Journal of Consciousness Studies 17 (9-10):168-188.score: 18.0
    From the assumption that the presence of consciousness is detectable, in the first instance, only from behavioral indicators, I offer a proof to the effect that, with respect to any theory T that states that some particular state or process is the neural correlate of consciousness, there are always rival neural correlates that, from T’s perspective, can never be empirically ruled out. That's because, with respect to these states, the means of detecting consciousness is disrupted along with the (...)
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  5. Susan L. Hurley & No (2003). Neural Plasticity and Consciousness. Biology and Philosophy 18 (1):131-168.score: 18.0
    and apply it to various examples of neural plasticity in which input is rerouted intermodally or intramodally to nonstandard cortical targets. In some cases but not others, cortical activity ‘defers’ to the nonstandard sources of input. We ask why, consider some possible explanations, and propose a dynamic sensorimotor hypothesis. We believe that this distinction is important and worthy of further study, both philosophical and empirical, whether or not our hypothesis turns out to be correct. In particular, the question of (...)
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  6. Ned Block (2001). How Not to Find the Neural Correlate of Consciousness. In João Branquinho (ed.), The Foundations of Cognitive Science. Oxford: Clarendon Press. 1.score: 18.0
    There are two concepts of consciousness that are easy to confuse with one another, access-consciousness and phenomenal consciousness. However, just as the concepts of water and H2O are different concepts of the same thing, so the two concepts of consciousness may come to the same thing in the brain. The focus of this paper is on the problems that arise when these two concepts of consciousness are conflated. I will argue that John Searle’s reasoning about the function of consciousness goes (...)
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  7. Pete Mandik (2003). Varieties of Representation in Evolved and Embodied Neural Networks. Biology and Philosophy 18 (1):95-130.score: 18.0
    In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind (...)
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  8. Carolyn Parkinson, Walter Sinnott-Armstrong, Philipp E. Koralus, Angela Mendelovici, Victoria McGeer & Thalia Wheatley (2011). Is Morality Unified? Evidence That Distinct Neural Systems Underlie Moral Judgments of Harm, Dishonesty, and Disgust. Journal of Cognitive Neuroscience 23 (10):3162-3180.score: 18.0
    Much recent research has sought to uncover the neural basis of moral judgment. However, it has remained unclear whether "moral judgments" are sufficiently homogenous to be studied scientifically as a unified category. We tested this assumption by using fMRI to examine the neural correlates of moral judgments within three moral areas: (physical) harm, dishonesty, and (sexual) disgust. We found that the judgment ofmoral wrongness was subserved by distinct neural systems for each of the different moral areas and (...)
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  9. Gerald M. Edelman & Giulio Srinivasan Tononi (2000). Reentry and the Dynamic Core: Neural Correlates of Conscious Experience. In Thomas Metzinger (ed.), Neural Correlates of Consciousness. MIT Press.score: 18.0
  10. Anil K. Seth & Bernard J. Baars (2005). Neural Darwinism and Consciousness. Consciousness and Cognition 14 (1):140-168.score: 18.0
    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the ‘dynamic core’). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely (...)
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  11. Greg Janzen (2008). Bennett and Hacker on Neural Materialism. Acta Analytica 23 (3):273-286.score: 18.0
    In their recent book Philosophical Foundations of Neuroscience, Max Bennett and Peter Hacker attack neural materialism (NM), the view, roughly, that mental states (events, processes, etc.) are identical with neural states or material properties of neural states (events, processes, etc.). Specifically, in the penultimate chapter entitled “Reductionism,” they argue that NM is unintelligible, that “there is no sense to literally identifying neural states and configurations with psychological attributes.” This is a provocative claim indeed. If Bennett and (...)
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  12. John Bickle (2001). Understanding Neural Complexity: A Role for Reduction. [REVIEW] Minds and Machines 11 (4):467-481.score: 18.0
    Psychoneural reduction is under attack again, only this time from a former ally: cognitive neuroscience. It has become popular to think of the brain as a complex system whose theoretically important properties emerge from dynamic, non-linear interactions between its component parts. ``Emergence'' is supposed to replace reduction: the latter is thought to be incapable of explaining the brain qua complex system. Rather than engage this issue at the level of theories of reduction versus theories of emergence, I here emphasize a (...)
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  13. Thomas Metzinger (2000). Neural Correlates of Consciousness: Empirical and Conceptual Questions. MIT Press.score: 18.0
  14. Andreas K. Engel & Wolf Singer (2001). Temporal Binding and the Neural Correlates of Sensory Awareness. Trends in Cognitive Sciences 5 (1):16-25.score: 18.0
    Theories of binding have recently come into the focus of the consciousness debate. In this review, we discuss the potential relevance of temporal binding mechanisms for sensory awareness. Specifically, we suggest that neural synchrony with a precision in the millisecond range may be crucial for conscious processing, and may be involved in arousal, perceptual integration, attentional selection and working memory. Recent evidence from both animal and human studies demonstrates that specific changes in neuronal synchrony occur during all of these (...)
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  15. Uriah Kriegel (2007). A Cross-Order Integration Hypothesis for the Neural Correlate of Consciousness. Consciousness & Cognition 16 (4):897-912.score: 18.0
    b>. One major problem many hypotheses regarding the neural correlate of consciousness (NCC) face is what we might call “the why question”: _why _would this particular neural feature, rather than another, correlate with consciousness? The purpose of the present paper is to develop an NCC hypothesis that answers this question. The proposed hypothesis is inspired by the Cross-Order Integration (COI) theory of consciousness, according to which consciousness arises from the functional integration of a first-order representation of an external (...)
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  16. Ilya B. Farber (2005). How a Neural Correlate Can Function as an Explanation of Consciousness: Evidence From the History of Science Regarding the Likely Explanatory Value of the NCC Approach. Journal of Consciousness Studies 12 (4-5):77-95.score: 18.0
    A frequent criticism of the neuroscientific approach to consciousness is that its theories describe only 'correlates' or 'analogues' of consciousness, and so fail to address the nature of consciousness itself. Despite its apparent logical simplicity, this criticism in fact relies on some substantive assumptions about the nature and evolution of scientific explanations. In particular, it is usually assumed that, in expressing correlations, neural correlate of consciousness (NCC) theories must fail to capture the causal structure relating brain and mind. Drawing (...)
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  17. Donald Levy (2003). Neural Holism and Free Will. Philosophical Psychology 16 (2):205-229.score: 18.0
    Both libertarian and compatibilist approaches have been unsuccessful in providing an acceptable account of free will. Recent developments in cognitive neuroscience, including the connectionist theory of mind and empirical findings regarding modularity and integration of brain functions, provide the basis for a new approach: neural holism. This approach locates free will in fully integrated behavior in which all of a person's beliefs and desires, implicitly represented in the brain, automatically contribute to an act. Deliberation, the experience of volition, and (...)
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  18. Paul Thagard & Terrence C. Stewart (2011). The AHA! Experience: Creativity Through Emergent Binding in Neural Networks. Cognitive Science 35 (1):1-33.score: 18.0
    Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity (...)
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  19. Aarre Laakso & Garrison W. Cottrell (2000). Content and Cluster Analysis: Assessing Representational Similarity in Neural Systems. Philosophical Psychology 13 (1):47-76.score: 18.0
    If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method (...)
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  20. Steven R. Quartz & Terrence J. Sejnowski (1997). The Neural Basis of Cognitive Development: A Constructivist Manifesto. Behavioral and Brain Sciences 20 (4):537-556.score: 18.0
    How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with (...)
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  21. Marc Slors (2010). Neural Resonance: Between Implicit Simulation and Social Perception. [REVIEW] Phenomenology and the Cognitive Sciences 9 (3):437-458.score: 18.0
    Shaun Gallagher and Dan Zahavi have recently argued against a simulationist interpretation of neural resonance. Recognizing intentions and emotions in the facial expressions and gestures of others may be subserved by e.g. mirror neuron activity, but this does not mean that we first experience an intention or emotion and then project it onto the other. Mirror neurons subserve social cognition, according to Gallagher and Zahavi, by being integral parts of processes of enactive social perception. I argue that the notion (...)
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  22. Naoyuki Osaka (ed.) (2003). Neural Basis of Consciousness. John Benjamins.score: 18.0
    CHAPTER Issues in neural basis of consciousness An introduction Naoyuki Osaka Kyoto University, Japan Consciousness is a most important issue for human ...
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  23. Michael John Healy & Thomas Preston Caudell (2006). Ontologies and Worlds in Category Theory: Implications for Neural Systems. [REVIEW] Axiomathes 16 (1-2):165-214.score: 18.0
    We propose category theory, the mathematical theory of structure, as a vehicle for defining ontologies in an unambiguous language with analytical and constructive features. Specifically, we apply categorical logic and model theory, based upon viewing an ontology as a sub-category of a category of theories expressed in a formal logic. In addition to providing mathematical rigor, this approach has several advantages. It allows the incremental analysis of ontologies by basing them in an interconnected hierarchy of theories, with an operation on (...)
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  24. Ariel S. Cecchi (2014). Cognitive Penetration, Perceptual Learning and Neural Plasticity. Dialectica 68 (1):63-95.score: 18.0
    Cognitive penetration of perception, broadly understood, is the influence that the cognitive system has on a perceptual system (e.g., visual, auditory, haptic). The paper shows a form of cognitive penetration in the visual system (defined as early vision) which I call ‘architectural’. Architectural cognitive penetration is the process whereby the behaviour or the structure of the perceptual system is influenced by the cognitive system, which consequently may have an impact on the content of the perceptual experience. I scrutinize a study (...)
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  25. Robert W. Kentridge (1995). Symbols, Neurons, Soap-Bubbles and the Neural Computation Underlying Cognition. Minds and Machines 4 (4):439-449.score: 18.0
    A wide range of systems appear to perform computation: what common features do they share? I consider three examples, a digital computer, a neural network and an analogue route finding system based on soap-bubbles. The common feature of these systems is that they have autonomous dynamics — their states will change over time without additional external influence. We can take advantage of these dynamics if we understand them well enough to map a problem we want to solve onto them. (...)
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  26. Gualtiero Piccinini & Sonya Bahar (2013). Neural Computation and the Computational Theory of Cognition. Cognitive Science 37 (3):453-488.score: 18.0
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog (...)
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  27. Daniel D. Hutto & Erik Myin (2013). Neural Representations Not Needed - No More Pleas, Please. Phenomenology and the Cognitive Sciences:1-16.score: 18.0
    Colombo (Phenomenology and the Cognitive Sciences, 2012) argues that we have compelling reasons to posit neural representations because doing so yields unique explanatory purchase in central cases of social norm compliance. We aim to show that there is no positive substance to Colombo’s plea—nothing that ought to move us to endorse representationalism in this domain, on any level. We point out that exposing the vices of the phenomenological arguments against representationalism does not, on its own, advance the case for (...)
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  28. Lothar Philipps & Giovanni Sartor (1999). Introduction: From Legal Theories to Neural Networks and Fuzzy Reasoning. [REVIEW] Artificial Intelligence and Law 7 (2-3):115-128.score: 18.0
    Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.
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  29. Bence Nanay (2002). Evolutionary Psychology and the Selectionist Model of Neural Development: A Combined Approach. Evolution and Cognition.score: 18.0
    Evolutionary psychology and the selectionist theories of neural development are usually regarded as two unrelated theories addressing two logically distinct questions. The focus of evolutionary psychology is the phylogeny of the human mind, whereas the selectionist theories of neural development analyse the ontogeny of the mind. This paper will endeavour to combine these two approaches in the explanation of the human mind. Doing so might help in overcoming some of the criticisms of both theories. The first part of (...)
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  30. James R. Hurford (2003). The Neural Basis of Predicate-Argument Structure. Behavioral and Brain Sciences 26 (3):261-283.score: 18.0
    Neural correlates exist for a basic component of logical formulae, PREDICATE(x). Vision and audition research in primates and humans shows two independent neural pathways; one locates objects in body-centered space, the other attributes properties, such as colour, to objects. In vision these are the dorsal and ventral pathways. In audition, similarly separable “where” and “what” pathways exist. PREDICATE(x) is a schematic representation of the brain's integration of the two processes of delivery by the senses of the location of (...)
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  31. Michael A. Arbib & Péter Érdi (2000). Précis of Neural Organization: Structure, Function, and Dynamics. Behavioral and Brain Sciences 23 (4):513-533.score: 18.0
    Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) (...)
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  32. Daisuke Okamoto (2009). Social Relationship of a Firm and the Csp–Cfp Relationship in Japan: Using Artificial Neural Networks. [REVIEW] Journal of Business Ethics 87 (1):117 - 132.score: 18.0
    As a criterion of a good firm, a lucrative and growing business has been said to be important. Recently, however, high profitability and high growth potential are insufficient for the criteria, because social influences exerted by recent firms have been extremely significant. In this paper, high social relationship is added to the list of the criteria. Empirical corporate social performance versus corporate financial performance (CSP–CFP) relationship studies that consider social relationship are very limited in Japan, and there are no definite (...)
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  33. Matteo Colombo (forthcoming). Explaining Social Norm Compliance. A Plea for Neural Representations. Phenomenology and the Cognitive Sciences:1-22.score: 18.0
    How should we understand the claim that people comply with social norms because they possess the right kinds of beliefs and preferences? I answer this question by considering two approaches to what it is to believe (and prefer), namely: representationalism and dispositionalism. I argue for a variety of representationalism, viz. neural representationalism. Neural representationalism is the conjunction of two claims. First, what it is essential to have beliefs and preferences is to have certain neural representations. Second, (...) representations are often necessary to adequately explain behaviour. After having canvassed one promising way to understand what neural representations could be, I argue that the appeal to beliefs and preferences in explanations of paradigmatic cases of norm compliance should be understood as an appeal to neural representations. (shrink)
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  34. Steve Donaldson (2008). A Neural Network for Creative Serial Order Cognitive Behavior. Minds and Machines 18 (1):53-91.score: 18.0
    If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and intersystem integration that are absent in most existing models. This model begins to address these issues by integrating predictive learning, sequence interleaving, and sequence creation components to simulate a spectrum of higher-order cognitive behaviors which have eluded the grasp of simpler systems. (...)
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  35. Eliano Pessa & Graziano Terenzi (2007). Semiosis in Cognitive Systems: A Neural Approach to the Problem of Meaning. [REVIEW] Mind and Society 6 (2):189-209.score: 18.0
    This paper deals with the problem of understanding semiosis and meaning in cognitive systems. To this aim we argue for a unified two-factor account according to which both external and internal information are non-independent aspects of meaning, thus contributing as a whole in determining its nature. To overcome the difficulties stemming from this approach we put forward a theoretical scheme based on the definition of a suitable representation space endowed with a set of transformations, and we show how it can (...)
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  36. 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.score: 18.0
    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 (...)
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  37. Ulrich J. Frey & Hannes Rusch (2013). Using Artificial Neural Networks for the Analysis of Social-Ecological Systems. Ecology and Society 18 (2).score: 18.0
    The literature on common pool resource (CPR) governance lists numerous factors that influence whether a given CPR system achieves ecological long-term sustainability. Up to now there is no comprehensive model to integrate these factors or to explain success within or across cases and sectors. Difficulties include the absence of large-N-studies (Poteete 2008), the incomparability of single case studies, and the interdependence of factors (Agrawal and Chhatre 2006). We propose (1) a synthesis of 24 success factors based on the current SES (...)
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  38. Stephen Jarosek (2013). Pragmatism, Neural Plasticity and Mind-Body Unity. Biosemiotics 6 (2):205-230.score: 18.0
    Recent developments in cognitive science provide compelling leads that need to be interpreted and synthesized within the context of semiotic and biosemiotic principles. To this end, we examine the impact of the mind-body unity on the sorts of choices that an organism is predisposed to making from its Umwelt. In multicellular organisms with brains, the relationship that an organism has with its Umwelt impacts on neural plasticity, the functional specialisations that develop within the brain, and its behaviour. Clinical observations, (...)
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  39. John D. Pettigrew (2001). Searching for the Switch: Neural Bases for Perceptual Rivalry Alternations. [REVIEW] Brain and Mind 2 (1):85-118.score: 18.0
    A midbrain neural basis for the perceptualoscillations of binocular rivalry is suggestedon the basis of fMRI studies of rivalry andinferences from the properties of rivalry thatcannot be explained from the known propertiesof primary visual cortical (V1) neurons. Therivalry switch is proposed to activatehomologous areas of each cerebral hemispherealternately, by means of a bistable oscillatorcircuit that straddles the midline of theventral tegmentum. This bistable oscillatoroperates at the same slow rate that ischaracteristic of perceptual rivalryalternations. Whilst attempting to divert thepresent preoccupation (...)
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  40. Thomas R. Shultz & Alan C. Bale (2006). Neural Networks Discover a Near-Identity Relation to Distinguish Simple Syntactic Forms. Minds and Machines 16 (2):107-139.score: 18.0
    Computer simulations show that an unstructured neural-network model [Shultz, T. R., & Bale, A. C. (2001). Infancy, 2, 501–536] covers the essential features␣of infant learning of simple grammars in an artificial language [Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999). Science, 283, 77–80], and generalizes to examples both outside and inside of the range of training sentences. Knowledge-representation analyses confirm that these networks discover that duplicate words in the sentences are nearly identical and that (...)
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  41. Matteo Colombo (forthcoming). Neural Representationalism, the Hard Problem of Content and Vitiated Verdicts. A Reply to Hutto & Myin (2013). Phenomenology and the Cognitive Sciences:1-18.score: 18.0
    Colombo’s (Phenomenology and the Cognitive Sciences, 2013) plea for neural representationalism is the focus of a recent contribution to Phenomenology and Cognitive Science by Daniel D. Hutto and Erik Myin. In that paper, Hutto and Myin have tried to show that my arguments fail badly. Here, I want to respond to their critique clarifying the type of neural representationalism put forward in my (Phenomenology and the Cognitive Sciences, 2013) piece, and to take the opportunity to make a few (...)
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  42. Artur S. D’Avila Garcez, Dov M. Gabbay, Oliver Ray & John Woods (2007). Abductive Reasoning in Neural-Symbolic Systems. Topoi 26 (1):37-49.score: 18.0
    Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments (...)
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  43. Dan Hunter (1999). Out of Their Minds: Legal Theory in Neural Networks. [REVIEW] Artificial Intelligence and Law 7 (2-3):129-151.score: 18.0
    This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks (...)
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  44. James L. McClelland (2013). Integrating Probabilistic Models of Perception and Interactive Neural Networks: A Historical and Tutorial Review. Frontiers in Psychology 4.score: 18.0
    This article seeks to establish a rapprochement between explicitly Bayesian models of contextual effects in perception and neural network models of such effects, particularly the connectionist interactive activation model of perception. The article is in part an historical review and in part a tutorial, reviewing the probabilistic Bayesian approach to understanding perception and how it may be shaped by context, and also reviewing ideas about how such probabilistic computations may be carried out in neural networks, focusing on the (...)
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  45. Tobias Schröder, Terrence C. Stewart & Paul Thagard (2013). Intention, Emotion, and Action: A Neural Theory Based on Semantic Pointers. Cognitive Science 38 (2).score: 18.0
    We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms (...)
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  46. A. D. Patel (2010). Why Would Musical Training Benefit the Neural Encoding of Speech? The OPERA Hypothesis. Frontiers in Psychology 2:142-142.score: 18.0
    Mounting evidence suggests that musical training benefits the neural encoding of speech. This paper offers a hypothesis specifying why such benefits occur. The “OPERA” hypothesis proposes that such benefits are driven by adaptive plasticity in speech-processing networks, and that this plasticity occurs when five conditions are met. These are: 1) Overlap: there is anatomical overlap in the brain networks that process an acoustic feature used in both music and speech (e.g., waveform periodicity, amplitude envelope), 2) Precision: music places higher (...)
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  47. Sammy Perone & John P. Spencer (2013). Autonomy in Action: Linking the Act of Looking to Memory Formation in Infancy Via Dynamic Neural Fields. Cognitive Science 37 (1):1-60.score: 18.0
    Looking is a fundamental exploratory behavior by which infants acquire knowledge about the world. In theories of infant habituation, however, looking as an exploratory behavior has been deemphasized relative to the reliable nature with which looking indexes active cognitive processing. We present a new theory that connects looking to the dynamics of memory formation and formally implement this theory in a Dynamic Neural Field model that learns autonomously as it actively looks and looks away from a stimulus. We situate (...)
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  48. F. Dolcos A. D. Iordan, S. Dolcos (2013). Neural Signatures of the Response to Emotional Distraction: A Review of Evidence From Brain Imaging Investigations. [REVIEW] Frontiers in Human Neuroscience 7.score: 18.0
    Prompt responses to emotional, potentially threatening, stimuli are supported by neural mechanisms that allow for privileged access of emotional information to processing resources. The existence of these mechanisms can also make emotional stimuli potent distracters, particularly when task-irrelevant. The ability to deploy cognitive control in order to cope with emotional distraction is essential for adaptive behavior, while reduced control may lead to enhanced emotional distractibility, which is often a hallmark of affective disorders. Evidence suggests that increased susceptibility to emotional (...)
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  49. Nadia Bolognini & Angelo Maravita (2012). Interactions Between Senses: Updating on Neural Mechanisms and Behavioral Evidence. Frontiers in Psychology 3.score: 18.0
    Interactions between Senses: Updating on Neural Mechanisms and Behavioral Evidence.
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  50. Holk Cruse & Malte Schilling (2013). How and to What End May Consciousness Contribute to Action? Attributing Properties of Consciousness to an Embodied, Minimally Cognitive Artificial Neural Network. Frontiers in Psychology 4.score: 18.0
    An artificial neural network called reaCog is described which is based on a decentralized, reactive and embodied architecture to control non-trivial hexapod walking in unpredictable environment (Walknet) as well as insect-like navigation (Navinet). In reaCog, these basic networks are extended in such a way that the complete system, reaCog, adopts the capability of inventing new behaviors and - via internal simulation - of planning ahead. This cognitive expansion enables the reactive system to be enriched with additional procedures. Here, we (...)
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