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

1000+ found
Sort by:
  1. Joseph Goguen (2006). Mathematical Models of Cognitive Space and Time. In D. Andler, M. Okada & I. Watanabe (eds.), Reasoning and Cognition. 125--128.score: 294.0
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  2. 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.score: 267.0
    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 (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  3. 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.score: 261.0
    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 (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  4. 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.score: 240.0
    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 (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  5. 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.score: 225.0
    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 (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  6. A. I͡U Khrennikov (2002). Classical and Quantum Mental Models and Freud's Theory of Unconscious/Conscious Mind. Växjö University Press.score: 225.0
  7. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 206.3
    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. (...)
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  8. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 206.3
    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 (...)
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  9. August Stern (2000). Quantum Theoretic Machines: What is Thought From the Point of View of Physics. Elsevier.score: 198.0
    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 (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  10. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.score: 196.0
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  11. Maria Nowakowska (1986). Cognitive Sciences: Basic Problems, New Perspectives and Implications for Artificial Intelligence. Academic Press.score: 195.0
  12. Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.score: 194.7
    To explore the relation between mathematical models and reality, four different domains of reality are distinguished: observer-independent reality (to which there is no direct access), personal reality, social reality and mathematical/formal reality. The concepts of personal and social reality are strongly inspired by constructivist ideas. Mathematical reality is social as well, but constructed as an autonomous system in order to make absolute agreement possible. The essential problem of mathematical modelling is that within mathematics there is (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  13. Alison Pease, Markus Guhe & Alan Smaill (2013). Developments in Research on Mathematical Practice and Cognition. Topics in Cognitive Science 5 (2):224-230.score: 194.0
    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.
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  14. Scott Hotton & Jeff Yoshimi (2011). Extending Dynamical Systems Theory to Model Embodied Cognition. Cognitive Science 35 (3):444-479.score: 192.0
    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 (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  15. 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.score: 175.3
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  16. Nick Chater & Alan Yuille (2006). Probabilistic Models of Cognition: Conceptual Foundations. Trends in Cognitive Sciences 10 (7):287-291.score: 174.0
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, (...)
    No categories
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  17. Ramy A. Fathy, Ahmed A. Abdel-Hafez & Abd El-Halim A. Zekry (2013). A Formal Mathematical Model of Cognitive Radio. Cognition 2 (4).score: 172.0
    Direct download  
     
    My bibliography  
     
    Export citation  
  18. Matt Jones & Bradley C. Love (2011). Pinning Down the Theoretical Commitments of Bayesian Cognitive Models. Behavioral and Brain Sciences 34 (4):215-231.score: 171.0
    Mathematical developments in probabilistic inference have led to optimism over the prospects for Bayesian models of cognition. Our target article calls for better differentiation of these technical developments from theoretical contributions. It distinguishes between Bayesian Fundamentalism, which is theoretically limited because of its neglect of psychological mechanism, and Bayesian Enlightenment, which integrates rational and mechanistic considerations and is thus better positioned to advance psychological theory. The commentaries almost uniformly agree that mechanistic grounding is critical to the success (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  19. Roberta L. Millstein, Robert A. Skipper Jr & Michael R. Dietrich (unknown). Interpreting Mathematical Models: Drift as a Physical Process. Philosophy and Theory in Biology 1 (20130604):e002.score: 168.0
    Recently, a number of philosophers of biology (e.g., Matthen and Ariew 2002; Walsh, Lewens, and Ariew 2002; Pigliucci and Kaplan 2006; Walsh 2007) 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 (or lack thereof) of terms appearing in (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  20. Philippe Tracqui (1995). From Passive Diffusion to Active Cellular Migration in Mathematical Models of Tumour Invasion. Acta Biotheoretica 43 (4).score: 168.0
    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 (...)
    Direct download  
     
    My bibliography  
     
    Export citation  
  21. Elizabeth Irvine (forthcoming). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese:1-17.score: 167.0
    This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. (...)
    No categories
    Direct download (2 more)  
     
    My bibliography  
     
    Export citation  
  22. R. Paul Thompson (2010). Causality, Mathematical Models and Statistical Association: Dismantling Evidence‐Based Medicine. Journal of Evaluation in Clinical Practice 16 (2):267-275.score: 166.7
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  23. 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.score: 166.3
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  24. Jerome R. Busemeyer & Zheng Wang (2014). Quantum Cognition: Key Issues and Discussion. Topics in Cognitive Science 6 (1):43-46.score: 165.0
    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 (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  25. 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.score: 164.0
    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 (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  26. Joseph A. King, Christopher Donkin, Franziska M. Korb & Tobias Egner (2012). Model-Based Analysis of Context-Specific Cognitive Control. Frontiers in Psychology 3.score: 153.0
    Interference resolution is improved for stimuli presented in contexts (e.g. locations) associated with frequent conflict. This phenomenon, the “context-specific proportion congruent” (CSPC) effect, has challenged the traditional juxtaposition of “automatic” and “controlled” processing because it suggests that contextual cues can prime top-down control settings in a bottom-up manner. We recently obtained support for this “priming of control” hypothesis with fMRI by showing that CSPC effects are mediated by contextually-cued adjustments in processing selectivity. However, an equally plausible explanation is that CSPC (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  27. Paolo Palladino (1991). Defining Ecology: Ecological Theories, Mathematical Models, and Applied Biology in the 1960s and 1970s. [REVIEW] Journal of the History of Biology 24 (2):223 - 243.score: 152.0
    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 (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  28. Vladimir G. Red'ko (2000). Evolution of Cognition: Towards the Theory of Origin of Human Logic. [REVIEW] Foundations of Science 5 (3):323-338.score: 150.0
    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 (...)
    Direct download (7 more)  
     
    My bibliography  
     
    Export citation  
  29. Andrew Aberdein (2013). Mathematical Wit and Mathematical Cognition. Topics in Cognitive Science 5 (2):231-250.score: 146.0
    The published works of scientists often conceal the cognitive processes that led to their results. Scholars of mathematical practice must therefore seek out less obvious sources. This article analyzes a widely circulated mathematical joke, comprising a list of spurious proof types. An account is proposed in terms of argumentation schemes: stereotypical patterns of reasoning, which may be accompanied by critical questions itemizing possible lines of defeat. It is argued that humor is associated with risky forms of inference, which (...)
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  30. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons.. 2--493.score: 146.0
    No categories
     
    My bibliography  
     
    Export citation  
  31. Christopher D. Green, Are Connectionist Models Theories of Cognition?score: 145.3
    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 (...)
     
    My bibliography  
     
    Export citation  
  32. George Lakoff (2008). The Role of the Brain in the Metaphorical Mathematical Cognition. Behavioral and Brain Sciences 31 (6):658-659.score: 144.0
    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.
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  33. Ruth Condray & Stuart R. Steinhauer (2002). The Residual Normality Assumption and Models of Cognition in Schizophrenia. Behavioral and Brain Sciences 25 (6):753-754.score: 144.0
    Thomas & Karmiloff-Smith’ (T&K-S’) 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 (modular components versus global processes). Moreover, it is unclear how the proposal that modularization emerges from developmental processes would change that fundamental question.
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  34. Rineke Verbrugge (2009). Logic and Social Cognition the Facts Matter, and so Do Computational Models. Journal of Philosophical Logic 38 (6):649-680.score: 144.0
    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 (...)
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  35. 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.score: 144.0
    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.
    Direct download (6 more)  
     
    My bibliography  
     
    Export citation  
  36. [deleted]Michael Harre (2013). The Neural Circuitry of Expertise: Perceptual Learning and Social Cognition. Frontiers in Human Neuroscience 7:852.score: 144.0
    Amongst the most significant questions we are confronted with today include the integration of the brain's micro-circuitry, our ability to build the complex social networks that underpin society and how our society impacts on our ecological environment. In trying to unravel these issues one place to begin is at the level of the individual: to consider how we accumulate information about our environment, how this information leads to decisions and how our individual decisions in turn create our social environment. While (...)
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  37. Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.score: 143.3
    In this commentary to Napoletani et al. (Found Sci 16:1–20, 2011), we argue that the approach the authors adopt suggests that neural nets are mathematical techniques rather than models of cognitive processing, that the general approach dates as far back as Ptolemy, and that applied mathematics is more than simply applying results from pure mathematics.
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  38. John Seely Brown & Richard R. Burton (1978). Diagnostic Models for Procedural Bugs in Basic Mathematical Skills. Cognitive Science 2 (2):155-192.score: 143.0
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  39. Jacques Ricard & Käty Ricard (1997). Mathematical Models in Biology. In Evandro Agazzi & György Darvas (eds.), Philosophy of Mathematics Today. Kluwer. 299--304.score: 142.0
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  40. Giuseppe Longo & Arnaud Viarouge (2010). Mathematical Intuition and the Cognitive Roots of Mathematical Concepts. Topoi 29 (1):15-27.score: 140.0
    The foundation of Mathematics is both a logico-formal issue and an epistemological one. By the first, we mean the explicitation and analysis of formal proof principles, which, largely a posteriori, ground proof on general deduction rules and schemata. By the second, we mean the investigation of the constitutive genesis of concepts and structures, the aim of this paper. This “genealogy of concepts”, so dear to Riemann, Poincaré and Enriques among others, is necessary both in order to enrich the foundational analysis (...)
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  41. C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.score: 140.0
    No categories
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  42. Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.score: 140.0
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  43. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.score: 140.0
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  44. Adam Morton (1993). Mathematical Models: Questions of Trustworthiness. British Journal for the Philosophy of Science 44 (4):659-674.score: 140.0
    Direct download (8 more)  
     
    My bibliography  
     
    Export citation  
  45. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.score: 140.0
    Direct download (5 more)  
     
    My bibliography  
     
    Export citation  
  46. Mehmet Elgin (2010). Mathematical Models, Explanation, Laws, and Evolutionary Biology. History and Philosophy of the Life Sciences 32 (4).score: 140.0
  47. Frank M. Doan (1960). On the Organizational Base of Language with Special Reference to Mathematical Models. Philosophy and Phenomenological Research 21 (2):239-247.score: 140.0
    No categories
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
  48. M. Thieullen (2009). Self Organization and Evolution in Mathematical Models. In Maryvonne Gérin & Marie-Christine Maurel (eds.), Origins of Life: Self-Organization and/or Biological Evolution? Edp Sciences. 37--46.score: 140.0
    No categories
    Direct download  
     
    My bibliography  
     
    Export citation  
  49. Richard M. Warren (1989). The Use of Mathematical Models in Perceptual Theory. Behavioral and Brain Sciences 12 (4):776.score: 140.0
    Direct download (3 more)  
     
    My bibliography  
     
    Export citation  
  50. Dominik Wodarz & Martin A. Nowak (2002). Mathematical Models of HIV Pathogenesis and Treatment. Bioessays 24 (12):1178-1187.score: 140.0
    Direct download (4 more)  
     
    My bibliography  
     
    Export citation  
1 — 50 / 1000