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

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  1. A. I͡U Khrennikov (2002). Classical and Quantum Mental Models and Freud's Theory of Unconscious/Conscious Mind. Växjö University Press.score: 207.0
  2. 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 (...)
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  3. 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: 195.0
    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 (...)
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  4. 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: 189.0
    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 (...)
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  5. Maria Nowakowska (1986). Cognitive Sciences: Basic Problems, New Perspectives and Implications for Artificial Intelligence. Academic Press.score: 189.0
  6. Joseph Goguen (2006). Mathematical Models of Cognitive Space and Time. In. In D. Andler, M. Okada & I. Watanabe (eds.), Reasoning and Cognition. 125--128.score: 180.0
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  7. 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: 168.0
    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 (...)
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  8. 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: 159.7
    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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  9. Scott Hotton & Jeff Yoshimi (2011). Extending Dynamical Systems Theory to Model Embodied Cognition. Cognitive Science 35 (3):444-479.score: 156.0
    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 (...)
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  10. Jerome R. Busemeyer & Zheng Wang (2014). Quantum Cognition: Key Issues and Discussion. Topics in Cognitive Science 6 (1):43-46.score: 147.0
    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 (...)
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  11. Alison Pease, Markus Guhe & Alan Smaill (2013). Developments in Research on Mathematical Practice and Cognition. Topics in Cognitive Science 5 (2):224-230.score: 146.0
    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.
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  12. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 141.0
    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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  13. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 141.0
    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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  14. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.score: 140.0
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  15. Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.score: 138.7
    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 (...)
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  16. Matt Jones & Bradley C. Love (2011). Pinning Down the Theoretical Commitments of Bayesian Cognitive Models. Behavioral and Brain Sciences 34 (4):215-231.score: 135.0
    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 (...)
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  17. Elizabeth Irvine (forthcoming). Models, Robustness, and Non-Causal Explanation: A Foray Into Cognitive Science and Biology. Synthese:1-17.score: 135.0
    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. (...)
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  18. 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: 132.0
    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 (...)
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  19. Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.score: 129.3
    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.
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  20. Joseph A. King, Christopher Donkin, Franziska M. Korb & Tobias Egner (2012). Model-Based Analysis of Context-Specific Cognitive Control. Frontiers in Psychology 3.score: 129.0
    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 (...)
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  21. 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: 127.3
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  22. Michael Harre (2013). The Neural Circuitry of Expertise: Perceptual Learning and Social Cognition. Frontiers in Human Neuroscience 7:852.score: 126.0
    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 (...)
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  23. Rein Vihalemm (2009). Teaduslik teooria kui teadusfilosoofia kategooria. Studia Philosophica Estonica 2 (1):32-46.score: 123.0
    Artiklis arendatakse alternatiivset kontseptsiooni niihästi traditsioonilisele füüsikakesksele teadusliku teooria käsitlusele kui ka seisukohale, et füüsikateooriat ei saa teadusfilosoofias mõista teadusliku teooria mudelina, sest erinevates teadustes on teooriad oma loomult erinevad. Ollakse seisukohal, et teaduslik teooria on ikkagi teadusfilosoofia kategooriana teadusliku distsipliini eripärast sõltumatu. Käsitletakse põhiliselt kahte punkti: (1) miks on teadusfilosoofias põhjust kritiseerida traditsioonilist, füüsika põhjal saadud ettekujutust teaduslikust teooriast? (2) miks ei ole põhjendatud seisukoht, et nt keemias on teaduslik teooria (nt klassikaline keemilise struktuuri teooria) oma loomult füüsikateooriast (nt (...)
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  24. Nick Chater & Alan Yuille (2006). Probabilistic Models of Cognition: Conceptual Foundations. Trends in Cognitive Sciences 10 (7):287-291.score: 118.0
    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, (...)
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  25. Taiki Takahashi (2014). Toward a Physical Theory of Quantum Cognition. Topics in Cognitive Science 6 (1):104-107.score: 117.0
    Recently, mathematical models based on quantum formalism have been developed in cognitive science. The target articles in this special issue of Topics in Cognitive Science clearly illustrate how quantum theoretical formalism can account for various aspects of human judgment and decision making in a quantitatively and mathematically rigorous manner. In this commentary, we show how future studies in quantum cognition and decision making should be developed to establish theoretical foundations based on physical theory, by introducing Taketani's three-stage (...)
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  26. Giuseppe Longo & Arnaud Viarouge (2010). Mathematical Intuition and the Cognitive Roots of Mathematical Concepts. Topoi 29 (1):15-27.score: 116.0
    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 (...)
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  27. Axel Gelfert (2011). Mathematical Formalisms in Scientific Practice: From Denotation to Model-Based Representation. Studies in History and Philosophy of Science 42 (2):272-286.score: 112.0
    The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that (...)
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  28. Roberta L. Millstein, Robert A. Skipper Jr & Michael R. Dietrich (2009). (Mis)Interpreting Mathematical Models: Drift as a Physical Process. Philosophy and Theory in Biology 1 (20130604):e002.score: 112.0
    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 (...)
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  29. Philippe Tracqui (1995). From Passive Diffusion to Active Cellular Migration in Mathematical Models of Tumour Invasion. Acta Biotheoretica 43 (4).score: 112.0
    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 (...)
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  30. Nick Chater & Mike Oaksford (2013). Programs as Causal Models: Speculations on Mental Programs and Mental Representation. Cognitive Science 37 (6):1171-1191.score: 111.0
    Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of mental “programs” and mental representation. We argue that programs (consisting of algorithms and data structures) have a causal (counterfactual-supporting) structure; these counterfactuals can reveal the nature of mental representations. Programs can (...)
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  31. 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: 110.7
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  32. Richard M. Shiffrin (2010). Perspectives on Modeling in Cognitive Science. Topics in Cognitive Science 2 (4):736-750.score: 110.3
    This commentary gives a personal perspective on modeling and modeling developments in cognitive science, starting in the 1950s, but focusing on the author’s personal views of modeling since training in the late 1960s, and particularly focusing on advances since the official founding of the Cognitive Science Society. The range and variety of modeling approaches in use today are remarkable, and for many, bewildering. Yet to come to anything approaching adequate insights into the infinitely complex fields of mind, brain, and intelligent (...)
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  33. Prof Ignazio Licata (2008). Logical Openness in Cognitive Models. Epistemologia:177-192.score: 108.0
    It is here proposed an analysis of symbolic and sub-symbolic models for studying cognitive processes, centered on emergence and logical openness notions. The Theory of logical openness connects the Physics of system/environment relationships to the system informational structure. In this theory, cognitive models can be ordered according to a hierarchy of complexity depending on their logical openness degree, and their descriptive limits are correlated to Gödel-Turing Theorems on formal systems. The symbolic models with low logical openness describe (...)
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  34. 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: 108.0
    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 (...)
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  35. Steven Sloman (2005). Causal Models: How People Think About the World and Its Alternatives. OUP USA.score: 108.0
    Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, that is, between action and outcome. -/- In cognitive terms, the question becomes one of how people construct and reason with the causal models we use to represent our world. A revolution is occuring in how statisticians, philosophers, and computer scientists answer this question. These fields have ushered in new insights about causal (...) by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called 'causal Bayesian networks'. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention: How does intervening on one thing affect other things? This question is not merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention, so cognition is thereby intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. -/- In this book, Steven Sloman offers a conceptual introduction to the key mathematical ideas in the framework, presenting them in a non-technical way, by focusing on the intuitions rather than the theorems. He tries to show why the ideas are important to understanding how people explain things, and why it is so central to human action to think not only about the world as it is, but also about the world as it could be. Sloman also reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgement, categorization, inductive inference, language, and learning. In short, this book offers a discussion about how people think, talk, learn, and explain things in causal terms - in terms of action and manipulation. (shrink)
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  36. Maria Isabel Aldinhas Ferreira & Miguel Gama Caldas (2013). Modelling Artificial Cognition in Biosemiotic Terms. Biosemiotics 6 (2):245-252.score: 105.0
    Stemming from Uexkull’s fundamental concepts of Umwelt and Innenwelt as developed in the biosemiotic approach of Ferreira 2010, 2011, the present work models mathematically the semiosis of cognition and proposes an artificial cognitive architecture to be deployed in a robotic structure.
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  37. Mauro Dorato (2012). Mathematical Biology and the Existence of Biological Laws. In DieksD (ed.), Probabilities, Laws and Structure. Springer.score: 102.0
    An influential position in the philosophy of biology claims that there are no biological laws, since any apparently biological generalization is either too accidental, fact-like or contingent to be named a law, or is simply reducible to physical laws that regulate electrical and chemical interactions taking place between merely physical systems. In the following I will stress a neglected aspect of the debate that emerges directly from the growing importance of mathematical models of biological phenomena. My main aim (...)
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  38. Ingo Brigandt (2013). Systems Biology and the Integration of Mechanistic Explanation and Mathematical Explanation. Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):477-492.score: 102.0
    The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and (...)
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  39. 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: 101.0
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  40. Glenn Gunzelmann (2011). Introduction to the Topic on Modeling Spatial Cognition. Topics in Cognitive Science 3 (4):628-631.score: 99.0
    Our ability to process spatial information is fundamental for understanding and interacting with the environment, and it pervades other components of cognitive functioning from language to mathematics. Moreover, technological advances have produced new capabilities that have created research opportunities and astonishing applications. In this Topic on Modeling Spatial Cognition, research crossing a variety of disciplines and methodologies is described, all focused on developing models to represent the capacities and limitations of human spatial cognition.
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  41. Andrew Aberdein (2013). Mathematical Wit and Mathematical Cognition. Topics in Cognitive Science 5 (2):231-250.score: 98.0
    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 (...)
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  42. Christopher D. Green, Are Connectionist Models Theories of Cognition?score: 97.3
    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 (...)
     
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  43. George Lakoff (2008). The Role of the Brain in the Metaphorical Mathematical Cognition. Behavioral and Brain Sciences 31 (6):658-659.score: 96.0
    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.
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  44. 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: 96.0
    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.
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  45. Rineke Verbrugge (2009). Logic and Social Cognition the Facts Matter, and so Do Computational Models. Journal of Philosophical Logic 38 (6):649-680.score: 96.0
    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 (...)
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  46. 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: 96.0
    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.
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  47. 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: 96.0
    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 (...)
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  48. Li-kung Shaw (1972). A Mathematical Model of Life and Living. Buenos Aires,Libreria Inglesa.score: 96.0
    [v. 1. Basic theories]--v. 2. Applications.--v. 3. Theory of plants and other essays.
     
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  49. Li-kung[from old catalog] Shaw (1959). A Mathematical Model of Human Life. Rosario, Argentina.score: 96.0
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  50. Robert McDowell Thrall (1966). Foundations [of Mathematics Oriented Toward the Concept of Mathematical Model]. Ann Arbor.score: 96.0
     
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