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

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  1.  94
    David Michael Kaplan (2011). Explanation and Description in Computational Neuroscience. Synthese 183 (3):339-373.
    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.  18
    Tyler D. Bancroft (2013). Ethical Aspects of Computational Neuroscience. Neuroethics 6 (2):415-418.
    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.  6
    Daniel C. Burnston (forthcoming). Computational Neuroscience and Localized Neural Function. Synthese:1-22.
    In this paper I criticize a view of functional localization in neuroscience, which I call “computational absolutism”. “Absolutism” in general is the view that each part of the brain should be given a single, univocal function ascription. Traditional varieties of absolutism posit that each part of the brain processes a particular type of information and/or performs a specific task. These function attributions are currently beset by physiological evidence which seems to suggest that brain areas are multifunctional—that they process (...)
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  4. Gualtiero Piccinini (2006). Computational Explanation in Neuroscience. Synthese 153 (3):343-353.
    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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  5. 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.
    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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  6.  4
    Marcin Miłkowski (2016). A Mechanistic Account of Computational Explanation in Cognitive Science and Computational Neuroscience. In Vincent C. Müller (ed.), Computing and Philosophy. Springer 191-205.
    Explanations in cognitive science and computational neuroscience rely predominantly on computational modeling. Although the scientific practice is systematic, and there is little doubt about the empirical value of numerous models, the methodological account of computational explanation is not up-to-date. The current chapter offers a systematic account of computational explanation in cognitive science and computational neuroscience within a mechanistic framework. The account is illustrated with a short case study of modeling of the mirror neuron (...)
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  7. Chris Eliasmith (forthcoming). Computational Neuroscience. In Paul R. Thagard (ed.), Philosophy of Psychology and Cognitive Science. Elsevier
    Keywords: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience.
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  8. Thomas P. Trappenberg (2002). Fundamentals of Computational Neuroscience. Oxford University Press Uk.
    Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Fundamentals of Computational Neuroscience is the first introductory book to this topic. It introduces the theoretical foundations of neuroscience with a focus on understanding information processing in the brain. The book is aimed at those within the brain and cognitive sciences, from graduate level and upwards.
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  9. Edmund T. Rolls (2007). Memory, Attention, and Decision-Making: A Unifying Computational Neuroscience. Oxford University Press Uk.
    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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  10.  34
    Roman Borisyuk (2000). Encyclopedia of Computational Neuroscience: The End of the Second Millennium. Behavioral and Brain Sciences 23 (4):534-535.
    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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  11.  50
    M. Chirimuuta (2014). Minimal Models and Canonical Neural Computations: The Distinctness of Computational Explanation in Neuroscience. Synthese 191 (2):127-153.
    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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  12.  5
    Tony Vladusich (2008). Towards a Computational Neuroscience of Autism-Psychosis Spectrum Disorders. Behavioral and Brain Sciences 31 (3):282-283.
    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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  13. Edmund T. Rolls (2007). Memory, Attention, and Decision-Making: A Unifying Computational Neuroscience Approach. OUP Oxford.
    Memory, attention, and decision-making are three major areas of cognitive neuroscience. They are however frequently studied in isolation, using a range of models to understand them. This book brings a unified approach to understanding these three processes, showing how these fundamental functions can be understood in a common and unifying framework.
     
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  14. Rick Grush (2006). How to, and How Not to, Bridge Computational Cognitive Neuroscience and Husserlian Phenomenology of Time Consciousness. Synthese 153 (3):417-450.
    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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  15.  4
    Terrence J. Sejnowski (1986). Computational Neuroscience. Behavioral and Brain Sciences 9 (1):104.
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  16.  16
    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.
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  17.  12
    Randall C. O'Reilly & Yuko Munakata (2003). Computational Neuroscience: From Biology to Cognition. In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group
  18.  12
    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.
  19. Axel Cleeremans, Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain.
    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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  20.  23
    Juan Felipe Martinez Florez (2012). Dietmar Heinke and Eirini Mavritsaki (Eds): Computational Modelling in Behavioural Neuroscience. [REVIEW] Minds and Machines 22 (1):57-60.
    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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  21.  21
    Axel Cleeremans, Harder, Better, Faster, Stronger: A Review of “Computational Explorations in Cognitive Neuroscience”. [REVIEW]
    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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  22. 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.
     
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  23.  7
    Peter Dayan & L. Abbott (2002). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Philosophical Psychology 15 (4):563-577.
  24.  12
    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.
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  25. 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.
    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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  26.  19
    Thor Grünbaum (forthcoming). The Perception-Action Model: Counting Computational Mechanisms. Mind and Language.
    Milner and Goodale’s Two Visual Systems Hypothesis (TVSH) is regarded as common ground in recent discussions of visual consciousness. A central part of TVSH is a functional model of vision and action (a functional perception-action model, PAM for short). In this paper, I provide a brief overview of these current discussions and argue that PAM is ambiguous between a strong and a weak version. I argue that, given a standard way of individuating computational mechanisms, the available evidence cannot be (...)
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  27. Bartlomiej Swiatczak (2011). Conscious Representations: An Intractable Problem for the Computational Theory of Mind. [REVIEW] Minds and Machines 21 (1):19-32.
    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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  28.  59
    Nir Fresco (2012). The Explanatory Role of Computation in Cognitive Science. Minds and Machines 22 (4):353-380.
    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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  29.  15
    Valerie G. Hardcastle & Kiah Hardcastle (2015). Marr's Levels Revisited: Understanding How Brains Break. Topics in Cognitive Science 7 (2):259-273.
    While the research programs in early cognitive science and artificial intelligence aimed to articulate what cognition was in ideal terms, much research in contemporary computational neuroscience looks at how and why brains fail to function as they should ideally. This focus on impairment affects how we understand David Marr's hypothesized three levels of understanding. In this essay, we suggest some refinements to Marr's distinctions using a population activity model of cortico-striatal circuitry exploring impulsivity and behavioral inhibition as a (...)
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  30.  21
    Enrique Frias-Martinez & Fernand Gobet (2007). Automatic Generation of Cognitive Theories Using Genetic Programming. Minds and Machines 17 (3):287-309.
    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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  31.  38
    Michael A. Arbib & Péter Érdi (2000). Précis of Neural Organization: Structure, Function, and Dynamics. Behavioral and Brain Sciences 23 (4):513-533.
    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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  32.  4
    Anna-Mari Rusanen & Otto Lappi (forthcoming). On Computational Explanations. Synthese:1-19.
    Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve (...)
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  33. John Bickle, Pete Mandik & Anthony Landreth, The Philosophy of Neuroscience. Stanford Encyclopedia of Philosophy.
    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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  34. Worth Boone & Gualtiero Piccinini (2016). The Cognitive Neuroscience Revolution. Synthese 193 (5):1509-1534.
    We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in (...)
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  35.  24
    Maria Serban (2015). The Scope and Limits of a Mechanistic View of Computational Explanation. Synthese 192 (10):3371-3396.
    An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive (...) as a subspecies of mechanistic explanations. This paper identifies several challenges facing their mechanistic account and sketches an alternative way of thinking about the epistemic roles of computational approaches used in the study of brain and cognition. Drawing on examples from both low-level and systems-level computational neuroscience, I argue that at least some computational explanations of neural and cognitive processes are partially independent from mechanistic constraints. (shrink)
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  36.  35
    Rosemarie Velik (2010). Why Machines Cannot Feel. Minds and Machines 20 (1):1-18.
    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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  37. Gabriel Vacariu & Vacariu (2013). The Mind-Brain Problem in Cognitive Neuroscience (Only Content).
    (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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  38.  19
    Giosuè Baggio, Michiel Lambalgen & Peter Hagoort (2015). Logic as Marr's Computational Level: Four Case Studies. Topics in Cognitive Science 7 (2):287-298.
    We sketch four applications of Marr's levels-of-analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on (...)
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  39. Gualtiero Piccinini & Andrea Scarantino (2011). Information Processing, Computation, and Cognition. Journal of Biological Physics 37 (1):1-38.
    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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  40.  26
    Matteo Colombo & Cory D. Wright (forthcoming). Explanatory Pluralism: An Unrewarding Prediction Error for Free Energy Theorists. Brain and Cognition.
    Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, (...)
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  41.  23
    Steven R. Quartz (2008). From Cognitive Science to Cognitive Neuroscience to Neuroeconomics. Economics and Philosophy 24 (3):459-471.
    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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  42.  5
    Jack Vromen (2010). Where Economics and Neuroscience Might Meet. Journal of Economic Methodology 17 (2):171-183.
    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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  43.  21
    Matteo Colombo (2014). Explaining Social Norm Compliance. A Plea for Neural Representations. Phenomenology and the Cognitive Sciences 13 (2):217-238.
    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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  44.  63
    Tomer Fekete (2010). Representational Systems. Minds and Machines 20 (1):69-101.
    The concept of representation has been a key element in the scientific study of mental processes, ever since such studies commenced. However, usage of the term has been all but too liberal—if one were to adhere to common use it remains unclear if there are examples of physical systems which cannot be construed in terms of representation. The problem is considered afresh, taking as the starting point the notion of activity spaces—spaces of spatiotemporal events produced by dynamical systems. It is (...)
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  45.  4
    Edmund T. Rolls (2013). On the Relation Between the Mind and the Brain: A Neuroscience Perspective. Philosophia Scientiae 17 (2):31-70.
    Dans cet article, je montre que les neurosciences computationnelles fournissent une nouvelle approche pertinente à des problèmes traditionnels en philosophie tels que la relation entre les états mentaux et cérébraux , le déterminisme et le libre arbitre, et peut nous aider à traiter le problème « difficile » des aspects phénoménaux de la conscience. Un des thèmes de cet article et de mon livre Neuroculture: on the Implications of Brain Science est qu’en comprenant les calculs effectués par les neurones et (...)
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  46.  6
    Elizabeth Irvine (2016). Model-Based Theorizing in Cognitive Neuroscience. British Journal for the Philosophy of Science 67 (1):143-168.
    Weisberg and Godfrey-Smith distinguish between two forms of theorizing: data-driven ‘abstract direct representation’ and modelling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena and so directly represent them, while models may not be intended to represent anything and so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorizing that involves direct representational relationships. This is based (...)
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  47. Roberto Cordeschi (2002). The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Kluwer.
    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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  48.  22
    Elizabeth Irvine (2014). Model-Based Theorizing in Cognitive Neuroscience. British Journal for the Philosophy of Science 67 (1):axu034.
    Weisberg and Godfrey-Smith distinguish between two forms of theorizing: data-driven ‘abstract direct representation’ and modelling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena and so directly represent them, while models may not be intended to represent anything and so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorizing that involves direct representational relationships. This is based (...)
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  49.  43
    Brian L. Keeley (2000). Neuroethology and the Philosophy of Cognitive Science. Philosophy of Science 60 (3):404-418.
    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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  50.  8
    Olaf Sporns (2000). Synthetic Approaches to Cognitive Neuroscience. Behavioral and Brain Sciences 23 (4):548-549.
    Cognition and behavior are the result of neural processes occurring at multiple levels of organization. Synthetic computational approaches are capable of bridging the gaps between multiple organizational levels and contribute to our understanding of how neural structures give rise to specific dynamical states. Such approaches are indispensable for formulating the theoretical foundations of cognitive neuroscience.
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