Search results for 'Cognition Mathematical models' (try it on Scholar)

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  1.  23
    Matt Jones & Bradley C. Love (2011). Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition. Behavioral and Brain Sciences 34 (4):169-188.
    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify (...)
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  2.  16
    Friedrich T. Sommer & Pentti Kanerva (2006). Can Neural Models of Cognition Benefit From the Advantages of Connectionism? Behavioral and Brain Sciences 29 (1):86-87.
    Cognitive function certainly poses the biggest challenge for computational neuroscience. As we argue, past efforts to build neural models of cognition (the target article included) had too narrow a focus on implementing rule-based language processing. The problem with these models is that they sacrifice the advantages of connectionism rather than building on them. Recent and more promising approaches for modeling cognition build on the mathematical properties of distributed neural representations. These approaches truly exploit the key (...)
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  3.  88
    Zheng Wang, Jerome R. Busemeyer, Harald Atmanspacher & Emmanuel M. Pothos (2013). The Potential of Using Quantum Theory to Build Models of Cognition. Topics in Cognitive Science 5 (4):672-688.
    Quantum cognition research applies abstract, mathematical principles of quantum theory to inquiries in cognitive science. It differs fundamentally from alternative speculations about quantum brain processes. This topic presents new developments within this research program. In the introduction to this topic, we try to answer three questions: Why apply quantum concepts to human cognition? How is quantum cognitive modeling different from traditional cognitive modeling? What cognitive processes have been modeled using a quantum account? In addition, a brief introduction (...)
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  4.  68
    Helen De Cruz (2016). Numerical Cognition and Mathematical Realism. Philosophers' Imprint 16 (16).
    Humans and other animals have an evolved ability to detect discrete magnitudes in their environment. Does this observation support evolutionary debunking arguments against mathematical realism, as has been recently argued by Clarke-Doane, or does it bolster mathematical realism, as authors such as Joyce and Sinnott-Armstrong have assumed? To find out, we need to pay closer attention to the features of evolved numerical cognition. I provide a detailed examination of the functional properties of evolved numerical cognition, and (...)
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  5. A. I͡U Khrennikov (2002). Classical and Quantum Mental Models and Freud's Theory of Unconscious/Conscious Mind. Växjö University Press.
  6.  72
    Adam Morton (1993). Mathematical Models: Questions of Trustworthiness. British Journal for the Philosophy of Science 44 (4):659-674.
    I argue that the contrast between models and theories is important for public policy issues. I focus especially on the way a mathematical model explains just one aspect of the data.
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  7. David Michael Kaplan & Carl F. Craver (2011). The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective. Philosophy of Science 78 (4):601-627.
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual (...)
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  8. Joseph Goguen (2006). Mathematical Models of Cognitive Space and Time. In D. Andler, M. Okada & I. Watanabe (eds.), Reasoning and Cognition. 125--128.
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  9.  5
    Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.
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  10.  62
    Alison Pease, Markus Guhe & Alan Smaill (2013). Developments in Research on Mathematical Practice and Cognition. Topics in Cognitive Science 5 (2):224-230.
    We describe recent developments in research on mathematical practice and cognition and outline the nine contributions in this special issue of topiCS. We divide these contributions into those that address (a) mathematical reasoning: patterns, levels, and evaluation; (b) mathematical concepts: evolution and meaning; and (c) the number concept: representation and processing.
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  11.  2
    D. Wade Hands (2016). Derivational Robustness, Credible Substitute Systems and Mathematical Economic Models: The Case of Stability Analysis in Walrasian General Equilibrium Theory. European Journal for Philosophy of Science 6 (1):31-53.
    This paper supports the literature which argues that derivational robustness can have epistemic import in highly idealized economic models. The defense is based on a particular example from mathematical economic theory, the dynamic Walrasian general equilibrium model. It is argued that derivational robustness first increased and later decreased the credibility of the Walrasian model. The example demonstrates that derivational robustness correctly describes the practices of a particular group of influential economic theorists and provides support for the arguments of (...)
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  12.  32
    August Stern (2000). Quantum Theoretic Machines: What is Thought From the Point of View of Physics. Elsevier.
    Making Sense of Inner Sense 'Terra cognita' is terra incognita. It is difficult to find someone not taken abackand fascinated by the incomprehensible but indisputable fact: there are material systems which are aware of themselves. Consciousness is self-cognizing code. During homo sapiens's relentness and often frustrated search for self-understanding various theories of consciousness have been and continue to be proposed. However, it remains unclear whether and at what level the problems of consciousness and intelligent thought can be resolved. Science's greatest (...)
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  13. Maria Nowakowska (1986). Cognitive Sciences: Basic Problems, New Perspectives and Implications for Artificial Intelligence. Academic Press.
  14.  3
    Nicholas L. Cassimatis, Paul Bello & Pat Langley (2008). Ability, Breadth, and Parsimony in Computational Models of Higher‐Order Cognition. Cognitive Science 32 (8):1304-1322.
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  15.  43
    Michael R. Dietrich, Robert A. Skipper Jr & Roberta L. Millstein (2009). (Mis)Interpreting Mathematical Models: Drift as a Physical Process. Philosophy & Theory in Biology 1 (20130604):e002.
    Recently, a number of philosophers of biology have endorsed views about random drift that, we will argue, rest on an implicit assumption that the meaning of concepts such as drift can be understood through an examination of the mathematical models in which drift appears. They also seem to implicitly assume that ontological questions about the causality of terms appearing in the models can be gleaned from the models alone. We will question these general assumptions by showing (...)
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  16.  18
    Philippe Tracqui (1995). From Passive Diffusion to Active Cellular Migration in Mathematical Models of Tumour Invasion. Acta Biotheoretica 43 (4):443-464.
    Mathematical models of tumour invasion appear as interesting tools for connecting the information extracted from medical imaging techniques and the large amount of data collected at the cellular and molecular levels. Most of the recent studies have used stochastic models of cell translocation for the comparison of computer simulations with histological solid tumour sections in order to discriminate and characterise expansive growth and active cell movements during host tissue invasion. This paper describes how a deterministic approach based (...)
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  17.  67
    Steffen Ducheyne (2005). Mathematical Models in Newton's Principia: A New View of the 'Newtonian Style'. International Studies in the Philosophy of Science 19 (1):1 – 19.
    In this essay I argue against I. Bernard Cohen's influential account of Newton's methodology in the Principia: the 'Newtonian Style'. The crux of Cohen's account is the successive adaptation of 'mental constructs' through comparisons with nature. In Cohen's view there is a direct dynamic between the mental constructs and physical systems. I argue that his account is essentially hypothetical-deductive, which is at odds with Newton's rejection of the hypothetical-deductive method. An adequate account of Newton's methodology needs to show how Newton's (...)
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  18.  31
    Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.
    There are presently two leading foreign policy decision-making paradigms in vogue. The first is based on the classical or rational model originally posited by von Neumann and Morgenstern to explain microeconomic decisions. The second is based on the cybernetic perspective whose groundwork was laid by Herbert Simon in his early research on bounded rationality. In this paper we introduce a third perspective — thepoliheuristic theory of decision-making — as an alternative to the rational actor and cybernetic paradigms in international relations. (...)
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  19.  9
    Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.
    There are presently two leading foreign policy decision-making paradigms in vogue. The first is based on the classical or rational model originally posited by von Neumann and Morgenstern to explain microeconomic decisions. The second is based on the cybernetic perspective whose groundwork was laid by Herbert Simon in his early research on bounded rationality. In this paper we introduce a third perspective -- the poliheuristic theory of decision-making -- as an alternative to the rational actor and cybernetic paradigms in international (...)
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  20.  84
    Marcin Miłkowski (2015). Evaluating Artificial Models of Cognition. Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I (...)
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  21.  11
    R. Paul Thompson (2010). Causality, Mathematical Models and Statistical Association: Dismantling Evidence‐Based Medicine. Journal of Evaluation in Clinical Practice 16 (2):267-275.
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  22. Carlos Montemayor (2015). Models for Cognition and Emotion: Evolutionary and Linguistic Considerations. Behavioral and Brain Sciences 38.
    A central claim in Luiz Pessoa’s (2013) book is that the terms “emotion” and “cognition” can be useful in characterizing behaviors but will not be cleanly mapped into brain regions. In order to be verified, this claim requires models for the integration and interfacing of emotion and cognition; yet, such models remain problematic.
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  23.  62
    Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.
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  24.  34
    Carlos Montemayor & Fuat Balci (2007). Compositionality in Language and Arithmetic. Journal of Theoretical and Philosophical Psychology 27 (1):53-72.
    The lack of conceptual analysis within cognitive science results in multiple models of the same phenomena. However, these models incorporate assumptions that contradict basic structural features of the domain they are describing. This is particularly true about the domain of mathematical cognition. In this paper we argue that foundational theoretic aspects of psychological models for language and arithmetic should be clarified before postulating such models. We propose a means to clarify these foundational concepts by (...)
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  25.  1
    Jonathan A. Waskan (2006). Models and Cognition. A Bradford Book.
    Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when new information becomes (...)
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  26.  29
    Andrea Loettgers (2007). Model Organisms and Mathematical and Synthetic Models to Explore Gene Regulation Mechanisms. Biological Theory 2 (2):134-142.
    Gene regulatory networks are intensively studied in biology. One of the main aims of these studies is to gain an understanding of how the structure of genetic networks relates to specific functions such as chemotaxis and the circadian clock. Scientists have examined this question by using model organisms such as Drosophila and mathematical models. In the last years, synthetic models—engineered genetic networks—have become more and more important in the exploration of gene regulation. What is the potential of (...)
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  27.  36
    Vladimir G. Red'ko (2000). Evolution of Cognition: Towards the Theory of Origin of Human Logic. [REVIEW] Foundations of Science 5 (3):323-338.
    The main problem discussed in this paper is: Why and how did animal cognition abilities arise? It is argued that investigations of the evolution of animal cognition abilities are very important from an epistemological point of view. A new direction for interdisciplinary researches – the creation and development of the theory of human logic origin – is proposed. The approaches to the origination of such a theory (mathematical models of ``intelligent invention'' of biological evolution, the cybernetic (...)
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  28.  79
    Scott Hotton & Jeff Yoshimi (2011). Extending Dynamical Systems Theory to Model Embodied Cognition. Cognitive Science 35 (3):444-479.
    We define a mathematical formalism based on the concept of an ‘‘open dynamical system” and show how it can be used to model embodied cognition. This formalism extends classical dynamical systems theory by distinguishing a ‘‘total system’’ (which models an agent in an environment) and an ‘‘agent system’’ (which models an agent by itself), and it includes tools for analyzing the collections of overlapping paths that occur in an embedded agent's state space. To illustrate the way (...)
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  29.  35
    Jerome R. Busemeyer & Zheng Wang (2014). Quantum Cognition: Key Issues and Discussion. Topics in Cognitive Science 6 (1):43-46.
    Quantum cognition is an emerging field that uses mathematical principles of quantum theory to help formalize and understand cognitive systems and processes. The topic on the potential of using quantum theory to build models of cognition (Volume 5, issue 4) introduces and synthesizes its new development through an introduction and six core articles. The current issue presents 14 commentaries on the core articles. Five key issues surface, some of which are interestingly controversial and debatable as expected (...)
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  30. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons. 2--493.
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  31.  2
    Paolo Palladino (1991). Defining Ecology: Ecological Theories, Mathematical Models, and Applied Biology in the 1960s and 1970s. Journal of the History of Biology 24 (2):223 - 243.
    Ever since the early decades of this century, there have emerged a number of competing schools of ecology that have attempted to weave the concepts underlying natural resource management and natural-historical traditions into a formal theoretical framework. It was widely believed that the discovery of the fundamental mechanisms underlying ecological phenomena would allow ecologists to articulate mathematically rigorous statements whose validity was not predicated on contingent factors. The formulation of such statements would elevate ecology to the standing of a rigorous (...)
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  32.  12
    Rineke Verbrugge (2009). Logic and Social Cognition the Facts Matter, and so Do Computational Models. Journal of Philosophical Logic 38 (6):649-680.
    This article takes off from Johan van Benthem’s ruminations on the interface between logic and cognitive science in his position paper “Logic and reasoning: Do the facts matter?”. When trying to answer Van Benthem’s question whether logic can be fruitfully combined with psychological experiments, this article focuses on a specific domain of reasoning, namely higher-order social cognition, including attributions such as “Bob knows that Alice knows that he wrote a novel under pseudonym”. For intelligent interaction, it is important that (...)
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  33.  38
    Thomas L. Griffiths, Nick Chater, Charles Kemp, Amy Perfors & Joshua B. Tenenbaum (2010). Probabilistic Models of Cognition: Exploring Representations and Inductive Biases. Trends in Cognitive Sciences 14 (8):357-364.
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  34. Manuel de Vega, Margaret Jean Intons-Peterson, Philip N. Johnson-Laird, Michel Denis & Marc Marschark (1996). Models of Visuospatial Cognition. Oxford University Press Usa.
    This second volume in the Counterpoints Series focuses on alternative models of visual-spatial processing in human cognition. The editors provide a historical and theoretical introduction and offer ideas about directions and new research designs.
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  35.  78
    George Lakoff (2008). The Role of the Brain in the Metaphorical Mathematical Cognition. Behavioral and Brain Sciences 31 (6):658-659.
    Rips et al. appear to discuss, and then dismiss with counterexamples, the brain-based theory of mathematical cognition given in Lakoff and Nez (2000). Instead, they present another theory of their own that they correctly dismiss. Our theory is based on neural learning. Rips et al. misrepresent our theory as being directly about real-world experience and mappings directly from that experience.
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  36.  11
    Thomas L. Griffiths (2009). The Strengths of – and Some of the Challenges for – Bayesian Models of Cognition. Behavioral and Brain Sciences 32 (1):89-90.
    Bayesian Rationality (Oaksford & Chater 2007) illustrates the strengths of Bayesian models of cognition: the systematicity of rational explanations, transparent assumptions about human learners, and combining structured symbolic representation with statistics. However, the book also highlights some of the challenges this approach faces: of providing psychological mechanisms, explaining the origins of the knowledge that guides human learning, and accounting for how people make genuinely new discoveries.
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  37.  9
    Ruth Condray & Stuart R. Steinhauer (2002). The Residual Normality Assumption and Models of Cognition in Schizophrenia. Behavioral and Brain Sciences 25 (6):753-754.
    Thomas & Karmiloff- Smith ’ argument that the Residual Normality assumption is not valid for developmental disorders has implications for models of cognition in schizophrenia, a disorder that may involve a neurodevelopmental pathogenesis. A limiting factor for such theories is the lack of understanding about the nature of the cognitive system. Moreover, it is unclear how the proposal that modularization emerges from developmental processes would change that fundamental question.
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  38.  10
    Christopher D. Green, Are Connectionist Models Theories of Cognition?
    This paper explores the question of whether connectionist models of cognition should be considered to be scientific theories of the cognitive domain. It is argued that in traditional scientific theories, there is a fairly close connection between the theoretical (unobservable) entities postulated and the empirical observations accounted for. In connectionist models, however, hundreds of theoretical terms are postulated -- viz., nodes and connections -- that are far removed from the observable phenomena. As a result, many of the (...)
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  39. Jonathan A. Waskan (2012). Models and Cognition. A Bradford Book.
    In this groundbreaking book, Jonathan Waskan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. This logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem--the lack of a principled way to determine which features of a representation must be updated (...)
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  40. Linda B. Greaver, G. Wei, Stephen M. Marson, Cynthia H. Herndon & James Rogers (2006). United States Low Birth Weight Since 1950: Distributions, Impacts, Causes, Costs, Patterns, Mathematical Models, Prediction and Prevention (I). Inquiry 7 (2):131-144.
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  41.  34
    Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.
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  42.  61
    C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.
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  43.  5
    Andrea Loettgers (2007). Getting Abstract Mathematical Models in Touch with Nature. Science in Context 20 (1):97.
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  44.  38
    Timothy J. O'Donnell, Marc D. Hauser & W. Tecumseh Fitch (2005). Using Mathematical Models of Language Experimentally. Trends in Cognitive Sciences 9 (6):284-289.
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  45.  25
    John V. Gillespie & Dina A. Zinnes (1975). Progressions in Mathematical Models of International Conflict. Synthese 31 (2):289 - 321.
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  46.  6
    Daniel Solow & Joesph Szmerekovsky (2004). Mathematical Models for Explaining the Emergence of Specialization in Performing Tasks. Complexity 10 (1):37-48.
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  47.  4
    Daniel Breslau & Yuval Yonay (1999). Beyond Metaphor: Mathematical Models in Economics as Empirical Research. Science in Context 12 (2).
  48.  47
    David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  49. Kazunori Fujimoto, Mitsunobu Shimazu & Yutaka Yamamoto (2003). Decision Support for Internet Users On Research Progress and Challenge Toward Building Mathematical Models. Transactions of the Japanese Society for Artificial Intelligence 18:36-44.
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  50.  1
    Salahaddin Khalilov (2014). The Alternative Mathematical Models of the World. Philosophy Study 4 (5).
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