Search results for 'computational neuroscience' (try it on Scholar)

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  1. David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.score: 90.0
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions (...)
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  2. Tyler D. Bancroft (2013). Ethical Aspects of Computational Neuroscience. Neuroethics 6 (2):415-418.score: 90.0
    Recent research in computational neuroscience has demonstrated that we now possess the ability to simulate neural systems in significant detail and on a large scale. Simulations on the scale of a human brain have recently been reported. The ability to simulate entire brains (or significant portions thereof) would be a revolutionary scientific advance, with substantial benefits for brain science. However, the prospect of whole-brain simulation comes with a set of new and unique ethical questions. In the present paper, (...)
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  3. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.score: 78.0
    According to some philosophers, computational explanation is proprietary
    to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational (...)
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  4. Rick Grush (2001). The Semantic Challenge to Computational Neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press. 155--172.score: 75.0
    I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that (...)
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  5. M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.score: 63.0
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper (...)
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  6. Chris Eliasmith (forthcoming). Computational Neuroscience. In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier.score: 60.0
    Keywords: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.
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  7. Edmund T. Rolls (2007). Memory, Attention, and Decision-Making: A Unifying Computational Neuroscience Approach. OUP Oxford.score: 60.0
    Memory, attention, and decision-making are three major areas of psychology. They are frequently studied in isolation, and using a range of models to understand them. This book brings a unified approach to understanding these three processes. It shows how these fundamental functions for cognitive neuroscience can be understood in a common and unifying computational neuroscience framework. This framework links empirical research on brain function from neurophysiology, functional neuroimaging, and the effects of brain damage, to a description of (...)
     
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  8. Roman Borisyuk (2000). Encyclopedia of Computational Neuroscience: The End of the Second Millennium. Behavioral and Brain Sciences 23 (4):534-535.score: 57.0
    Arbib et al. describe mathematical and computational models in neuroscience as well as neuroanatomy and neurophysiology of several important brain structures. This is a useful guide to mathematical and computational modelling of the structure and function of nervous system. The book highlights the need to develop a theory of brain functioning, and it offers some useful approaches and concepts.
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  9. Rick Grush (2006). How to, and How Not to, Bridge Computational Cognitive Neuroscience and Husserlian Phenomenology of Time Consciousness. Synthese 153 (3):417-450.score: 51.0
    A number of recent attempts to bridge Husserlian phenomenology of time consciousness and contemporary tools and results from cognitive science or computational neuroscience are described and critiqued. An alternate proposal is outlined that lacks the weaknesses of existing accounts.
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  10. Axel Cleeremans, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain.score: 48.0
    The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprised of networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons and (...)
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  11. Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.score: 48.0
    Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The (...)
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  12. Juan Felipe Martinez Florez (2012). Dietmar Heinke and Eirini Mavritsaki (Eds): Computational Modelling in Behavioural Neuroscience. [REVIEW] Minds and Machines 22 (1):57-60.score: 48.0
    Dietmar Heinke and Eirini Mavritsaki (eds): Computational Modelling in Behavioural Neuroscience Content Type Journal Article Category Book Review Pages 57-60 DOI 10.1007/s11023-011-9265-8 Authors Juan Felipe Martinez Florez, Institute of Psychology, Universidad del Valle, Campus Universitario Melndez, Ed. 388, Of. 4017, Cali, Colombia Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.
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  13. Axel Cleeremans, Harder, Better, Faster, Stronger: A Review of “Computational Explorations in Cognitive Neuroscience”. [REVIEW]score: 48.0
    Just like the sequel to a successful movie, O’Reilly and Munakata’s “Computational Explorations in Cognitive Neuroscience” aims to follow up and expand on the original 1986 “Parallel Distributed Processing” volumes edited by James McClelland, David Rumelhart and the PDP research group. This kinship, which is explicitly recognized by the authors as the book is prefaced by Jay McClelland, is perceptible throughout Computational Explorations: Not only does this volume visit many of the problems and paradigms that the original (...)
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  14. Tony Vladusich (2008). Towards a Computational Neuroscience of Autism-Psychosis Spectrum Disorders. Behavioral and Brain Sciences 31 (3):282-283.score: 48.0
    Crespi & Badcock (C&B) hypothesize that psychosis and autism represent opposite poles of human social cognition. I briefly outline how computational models of cognitive brain function may be used as a resource to further develop and experimentally test hypotheses concerning 1.
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  15. Bartlomiej Swiatczak (2011). Conscious Representations: An Intractable Problem for the Computational Theory of Mind. [REVIEW] Minds and Machines 21 (1):19-32.score: 45.0
    Advocates of the computational theory of mind claim that the mind is a computer whose operations can be implemented by various computational systems. According to these philosophers, the mind is multiply realisable because—as they claim—thinking involves the manipulation of syntactically structured mental representations. Since syntactically structured representations can be made of different kinds of material while performing the same calculation, mental processes can also be implemented by different kinds of material. From this perspective, consciousness plays a minor role (...)
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  16. Gualtiero Piccinini (2004). The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts' Logical Calculus of Ideas Immanent in Nervous Activity. Synthese 141 (2):175-215.score: 45.0
    Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism (...)
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  17. Michael A. Arbib & Péter Érdi (2000). Précis of Neural Organization: Structure, Function, and Dynamics. Behavioral and Brain Sciences 23 (4):513-533.score: 45.0
    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) Function: Control (...)
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  18. Enrique Frias-Martinez & Fernand Gobet (2007). Automatic Generation of Cognitive Theories Using Genetic Programming. Minds and Machines 17 (3):287-309.score: 45.0
    Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental (...)
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  19. K. Nicholas Leibovic (1997). Patricia S. Churchland and Terrence J. Sejnowski, the Computational Brain, Computational Neuroscience Series, Cambridge, MA: MIT Press, 1992. [REVIEW] Minds and Machines 7 (4):581-585.score: 45.0
  20. Helen Barbas Yohan J. John, Daniel Bullock, Basilis Zikopoulos (2013). Anatomy and Computational Modeling of Networks Underlying Cognitive-Emotional Interaction. Frontiers in Human Neuroscience 7.score: 45.0
    The classical dichotomy between cognition and emotion equated the first with rationality or logic and the second with irrational behaviors. The idea that cognition and emotion are separable, antagonistic forces competing for dominance of mind has been hard to displace despite abundant evidence to the contrary. For instance, it is now known that a pathological absence of emotion leads to profound impairment of decision making. Behavioral observations of this kind are corroborated at the mechanistic level: neuroanatomical studies reveal that brain (...)
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  21. Tyler D. Bancroft, William E. Hockley & Philip Servos (2011). Vibrotactile Working Memory as a Model Paradigm for Psychology, Neuroscience, and Computational Modeling. Frontiers in Human Neuroscience 5.score: 45.0
  22. Jean-Marc Fellous, Jorge L. Armony & Joseph E. LeDoux (2002). Emotional Circuits and Computational Neuroscience. In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. Mit Press. 2.score: 45.0
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  23. Shane Lee & Stephanie R. Jones (2013). Distinguishing Mechanisms of Gamma Frequency Oscillations in Human Current Source Signals Using a Computational Model of a Laminar Neocortical Network. Frontiers in Human Neuroscience 7:869.score: 45.0
    Gamma frequency rhythms have been implicated in numerous studies for their role in healthy and abnormal brain function. The frequency band has been described to encompass as broad a range as 30–150 Hz. Crucial to understanding the role of gamma in brain function is an identification of the underlying neural mechanisms, which is particularly difficult in the absence of invasive recordings in macroscopic human signals such as those from magnetoencephalography (MEG) and electroencephalography (EEG). Here, we studied features of current dipole (...)
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  24. Randall C. O'Reilly & Yuko Munakata (2003). Computational Neuroscience: From Biology to Cognition. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.score: 45.0
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  25. Terrence J. Sejnowski (1986). Computational Neuroscience. Behavioral and Brain Sciences 9 (1):104.score: 45.0
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  26. M. J. Farah (2000). Computational Modeling in Cognitive Neuroscience. In Martha J. Farah & Todd E. Feinberg (eds.), Patient-Based Approaches to Cognitive Neuroscience. Mit Press. 53--62.score: 39.0
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  27. Rosemarie Velik (2010). Why Machines Cannot Feel. Minds and Machines 20 (1):1-18.score: 36.0
    For a long time, emotions have been ignored in the attempt to model intelligent behavior. However, within the last years, evidence has come from neuroscience that emotions are an important facet of intelligent behavior being involved into cognitive problem solving, decision making, the establishment of social behavior, and even conscious experience. Also in research communities like software agents and robotics, an increasing number of researchers start to believe that computational models of emotions will be needed to design intelligent (...)
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  28. Frances Egan (forthcoming). Function-Theoretic Explanation and Neural Mechanisms. In David M. Kaplan (ed.), Integrating Mind and Brain Science: Mechanistic Perspectives and Beyond.score: 36.0
    A common kind of explanation in cognitive neuroscience might be called function-theoretic: with some target cognitive capacity in view, the theorist hypothesizes that the system computes a well-defined function (in the mathematical sense) and explains how computing this function constitutes (in the system’s normal environment) the exercise of the cognitive capacity. Recently, proponents of the so-called ‘new mechanist’ approach in philosophy of science have argued that a model of a cognitive capacity is explanatory only to the extent that it (...)
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  29. Peter Dayan & L. Abbott (2002). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Philosophical Psychology 15 (4):563-577.score: 36.0
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  30. John Donohoe (2010). Man as Machine: A Review of Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience, by CR Gallistel and AP King. [REVIEW] Behavior and Philosophy 38:83-101.score: 36.0
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  31. Gualtiero Piccinini & Andrea Scarantino (2011). Information Processing, Computation, and Cognition. Journal of Biological Physics 37 (1):1-38.score: 34.0
    Computation and information processing are among the most fundamental notions in cognitive science. They are also among the most imprecisely discussed. Many cognitive scientists take it for granted that cognition involves computation, information processing, or both – although others disagree vehemently. Yet different cognitive scientists use ‘computation’ and ‘information processing’ to mean different things, sometimes without realizing that they do. In addition, computation and information processing are surrounded by several myths; first and foremost, that they are the same thing. In (...)
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  32. Jérémie Mattout (2012). Brain-Computer Interfaces: A Neuroscience Paradigm of Social Interaction? A Matter of Perspective. Frontiers in Human Neuroscience 6.score: 33.0
    Brain-Computer Interfaces: A Neuroscience Paradigm of Social Interaction? A Matter of Perspective.
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  33. Garrett Neske (2010). The Notion of Computation is Fundamental to an Autonomous Neuroscience. Complexity 16 (1):10-19.score: 32.0
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  34. John Bickle, Pete Mandik & Anthony Landreth, The Philosophy of Neuroscience. Stanford Encyclopedia of Philosophy.score: 30.0
    Over the past three decades, philosophy of science has grown increasingly “local.” Concerns have switched from general features of scientific practice to concepts, issues, and puzzles specific to particular disciplines. Philosophy of neuroscience is a natural result. This emerging area was also spurred by remarkable recent growth in the neurosciences. Cognitive and computational neuroscience continues to encroach upon issues traditionally addressed within the humanities, including the nature of consciousness, action, knowledge, and normativity. Empirical discoveries about brain structure (...)
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  35. Gabriel Vacariu & Vacariu (2013). The Mind-Brain Problem in Cognitive Neuroscience (Only Content).score: 30.0
    (June 2013) “The mind-body problem in cognitive neuroscience”, Philosophia Scientiae 17/2, Gabriel Vacariu and Mihai Vacariu (eds.): 1. William Bechtel (Philosophy, Center for Chronobiology, and Interdisciplinary Program in Cognitive Science University of California, San Diego) “The endogenously active brain: the need for an alternative cognitive architecture” 2. Rolls T. Edmund (Oxford Centre for Computational Neuroscience, Oxford, UK) “On the relation between the mind and the brain: a neuroscience perspective” 3. Cees van Leeuwen (University of Leuven, Belgium; (...)
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  36. Tomer Fekete (2010). Representational Systems. Minds and Machines 20 (1):69-101.score: 30.0
    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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  37. Matteo Colombo (2014). Explaining Social Norm Compliance. A Plea for Neural Representations. Phenomenology and the Cognitive Sciences 13 (2):217-238.score: 30.0
    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, neural representations are often (...)
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  38. Jonathan Bentwich (2006). The Duality Principle: Irreducibility of Sub-Threshold Psychophysical Computation to Neuronal Brain Activation. Synthese 153 (3):451-455.score: 27.0
    A key working hypothesis in neuroscience is ‘materialistic reductionism’, i.e., the assumption whereby all physiological, behavioral or cognitive phenomena is produced by localized neurochemical brain activation (but not vice versa). However, analysis of sub-threshold Weber’s psychophysical stimulation indicates its computational irreducibility to the direct interaction between psychophysical stimulation and any neuron/s. This is because the materialistic-reductionistic working hypothesis assumes that the determination of the existence or non-existence of any psychophysical stimulation [s] may only be determined through its direct (...)
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  39. Brian L. Keeley (2000). Neuroethology and the Philosophy of Cognitive Science. Philosophy of Science 60 (3):404-418.score: 27.0
    Neuroethology is a branch of biology that studies the neural basis of naturally occurring animal behavior. This science, particularly a recent program called computational neuroethology, has a similar structure to the interdisciplinary endeavor of cognitive science. I argue that it would be fruitful to conceive of cognitive science as the computational neuroethology of humans. However, there are important differences between the two sciences, including the fact that neuroethology is much more comparative in its perspective. Neuroethology is a biological (...)
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  40. Roberto Cordeschi (2002). The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Kluwer.score: 27.0
    The book provides a valuable text for undergraduate and graduate courses on the historical and theoretical issues of Cognitive Science, Artificial Intelligence, Psychology, Neuroscience, and the Philosophy of Mind. The book should also be of interest for researchers in these fields, who will find in it analyses of certain crucial issues in both the earlier and more recent history of their disciplines, as well as interesting overall insights into the current debate on the nature of mind.
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  41. Elliot C. Brown & Martin Brüne (2012). The Role of Prediction in Social Neuroscience. Frontiers in Human Neuroscience 6 (147):147-147.score: 24.0
    Research has shown that the brain is constantly making predictions about future events. Theories of prediction in perception, action and learning suggest that the brain serves to reduce the discrepancies between expectation and actual experience, i.e. by reducing the prediction error. Forward models of action and perception propose the generation of a predictive internal representation of the expected sensory outcome, which is matched to the actual sensory feedback. Shared neural representations have been found when experiencing one’s own and observing others’ (...)
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  42. Steven R. Quartz (2008). From Cognitive Science to Cognitive Neuroscience to Neuroeconomics. Economics and Philosophy 24 (3):459-471.score: 24.0
    As an emerging discipline, neuroeconomics faces considerable methodological and practical challenges. In this paper, I suggest that these challenges can be understood by exploring the similarities and dissimilarities between the emergence of neuroeconomics and the emergence of cognitive and computational neuroscience two decades ago. From these parallels, I suggest the major challenge facing theory formation in the neural and behavioural sciences is that of being under-constrained by data, making a detailed understanding of physical implementation necessary for theory construction (...)
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  43. Iris van Rooij, Johan Kwisthout, Mark Blokpoel, Jakub Szymanik, Todd Wareham & Ivan Toni (2011). Intentional Communication: Computationally Easy or Difficult? Frontiers in Human Neuroscience 5.score: 24.0
    Human intentional communication is marked by its flexibility and context sensitivity. Hypothesized brain mechanisms can provide convincing and complete explanations of the human capacity for intentional communication only insofar as they can match the computational power required for displaying that capacity. It is thus of importance for cognitive neuroscience to know how computationally complex intentional communication actually is. Though the subject of considerable debate, the computational complexity of communication remains so far unknown. In this paper we defend (...)
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  44. J. Bruce Morton, Fredrick Ezekiel & Heather A. Wilk (2011). Cognitive Control: Easy to Identify But Hard to Define. Topics in Cognitive Science 3 (2):212-216.score: 24.0
    Cognitive control is easy to identify in its effects, but difficult to grasp conceptually. This creates somewhat of a puzzle: Is cognitive control a bona fide process or an epiphenomenon that merely exists in the mind of the observer? The topiCS special edition on cognitive control presents a broad set of perspectives on this issue and helps to clarify central conceptual and empirical challenges confronting the field. Our commentary provides a summary of and critical response to each of the papers.
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  45. Iris van Rooij Mark Blokpoel, Johan Kwisthout (2012). When Can Predictive Brains Be Truly Bayesian? Frontiers in Psychology 3.score: 24.0
    When Can Predictive Brains be Truly Bayesian?
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  46. Jack Vromen (2010). Where Economics and Neuroscience Might Meet. Journal of Economic Methodology 17 (2):171-183.score: 24.0
    Contrary to what is claimed by Gul and Pesendorfer (2008), in this paper I argue that neuroscience and economics can meet in ways that speak to the interests of economists. As Bernheim (2009) argues, economists seem to be primarily interested in novel models that link ?traditional? environmental variables (such as prices and taxes) to choice behavior in a more accurate way than existing models. Neuroscience might be helpful here, since especially computational neuroscience is also in the (...)
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  47. Marcel A. J. Van Gerven Ole Jensen, Ali Bahramisharif, Robert Oostenveld, Stefan Klanke, Avgis Hadjipapas, Yuka O. Okazaki (2011). Using Brain–Computer Interfaces and Brain-State Dependent Stimulation as Tools in Cognitive Neuroscience. Frontiers in Psychology 2.score: 24.0
    Large efforts are currently being made to develop and improve online analysis of brain activity which can be used e.g. for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures (...)
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  48. Walter Schneider & Jason M. Chein (2003). Controlled & Automatic Processing: Behavior, Theory, and Biological Mechanisms. Cognitive Science 27 (3):525-559.score: 24.0
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  49. Diane Pecher Anna M. Borghi (2011). Introduction to the Special Topic Embodied and Grounded Cognition. Frontiers in Psychology 2.score: 24.0
    Introduction to the Special Topic Embodied and Grounded Cognition.
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  50. Robert A. Jacobs (2010). Integrated Approaches to Perceptual Learning. Topics in Cognitive Science 2 (2):182-188.score: 24.0
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