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

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  1.  1
    Joseph S. Ullian (1968). Chomsky Noam and Miller George A.. Introduction to the Formal Analysis of Natural Languages. Handbook of Mathematical Psychology, Volume II, Edited by Duncan Luce R., Bush Robert R., and Galanter Eugene, John Wiley and Sons, Inc., New York and London 1963, Pp. 269–321.Chomsky Noam. Formal Properties of Grammars. Handbook of Mathematical Psychology, Volume II, Edited by Duncan Luce R., Bush Robert R., and Galanter Eugene, John Wiley and Sons, Inc., New York and London 1963, Pp. 323–418.Miller George A. And Chomsky Noam. Finitary Models of Language Users. Handbook of Mathematical Psychology, Volume II, Edited by Duncan Luce R., Bush Robert R., and Galanter Eugene, John Wiley and Sons, Inc., New York and London 1963, Pp. 419–491. [REVIEW] Journal of Symbolic Logic 33 (2):299-300.
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  2. J. F. Staal (1966). Reviews. Noam Chomsky. Syntactic Structures. Janua Linguarum, Studia Memoriae Nicolai van Wijk Dedicata, Series Minor No. 4. Mouton & Co., ‘s-Gravenhage 1957, 116 Pp. Noam Chomsky. Three Models for the Description of Language. A Reprint of XXIII 71. Readings in Mathematical Psychology, Volume II, Edited by R. Duncan Luce, Robert R. Bush, and Eugene Galanter, John Wiley and Sons, Inc., New York, London, and Sydney, 1965, Pp. 105–124. Noam Chomsky. Logical Structures in Language. American Documentation, Vol. 8 , Pp. 284–291. Noam Chomsky and George A. Miller. Finite State Languages. Information and Control, Vol. 1 , Pp. 91–112. Reprinted in Readings in Mathematical Psychology, Volume II, Edited by R. Duncan Luce, Robert R. Bush, and Eugene Galanter, John Wiley and Sons, Inc., New York, London, and Sydney, 1965, Pp. 156–171. Noam Chomsky. On Certain Formal Properties of Grammars. Information and Control, Vol. 2 , Pp. 137–167. Reprinted in Readings in Mathematical Psychology, Volume II, Ed. [REVIEW] Journal of Symbolic Logic 31 (2):245-251.
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  3.  32
    Evert Willem Beth (1966). Mathematical Epistemology and Psychology. New York, Gordon and Breach.
  4.  5
    Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.
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  5.  6
    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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  6.  16
    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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  7. 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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  8. John Dagsvik (1983). Discrete Dynamic Choice: An Extension of the Choice Models of Thurstone and Luce. I Kommisjon Hos H. Aschehoug Og Universitetsforlaget.
     
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  9.  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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  10. 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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  11.  15
    Andrei G. Khromov (2001). Logical Self-Reference as a Model for Conscious Experience. Journal of Mathematical Psychology 45 (5):720-731.
  12.  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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  13.  11
    Geert de Soete, Hubert Feger & Karl C. Klauer (eds.) (1989). New Developments in Psychological Choice Modeling. Distributors for the United States and Canada, Elsevier Science Pub..
    A selection of 15 papers on choice modeling are presented in this volume.
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  14.  42
    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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  15.  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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  16.  66
    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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  17.  8
    Helmut K. Reich (2008). Extending the Psychology of Religion: A Call for Exploration of Psychological Universals, More Inclusive Approaches, and Comprehensive Models. Archive for the Psychology of Religion 30 (1):115-134.
    Extensions of ongoing research identified in the introduction to this special issue are discussed here with farther reaching objectives: researching more intensely psychological universals thought to underlie religion, taking a more inclusive approach to psychology of religion, and constructing more comprehensive models. All three involve conscious experience, to which some observations are devoted. Remarks about the relationships between these research areas conclude the article.
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  18.  82
    Eric Hochstein (2012). Minds, Models and Mechanisms: A New Perspective on Intentional Psychology. Journal of Experimental & Theoretical Artificial Intelligence 24 (4):547-557.
    In this article, I argue that intentional psychology (i.e. the interpretation of human behaviour in terms of intentional states and propositional attitudes) plays an essential role in the sciences of the mind. However, this role is not one of identifying scientifically respectable states of the world. Rather, I argue that intentional psychology acts as a type of phenomenological model, as opposed to a mechanistic one. I demonstrate that, like other phenomenological models in science, intentional psychology is (...)
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  19.  79
    Carsten Held, Markus Knauff & Gottfried Vosgerau (eds.) (2006). Mental Models and the Mind: Current Developments in Cognitive Psychology, Neuroscience, and Philosophy of Mind. Elsevier.
    "Cognitive psychology," "cognitive neuroscience," and "philosophy of mind" are names for three very different scientific fields, but they label aspects of the same scientific goal: to understand the nature of mental phenomena. Today, the three disciplines strongly overlap under the roof of the cognitive sciences. The book's purpose is to present views from the different disciplines on one of the central theories in cognitive science: the theory of mental models. Cognitive psychologists report their research on the representation and (...)
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  20.  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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  21.  33
    Jacques Hadamard (1945). The Mathematician's Mind: The Psychology of Invention in the Mathematical Field. Princeton University Press.
    Fifty years ago when Jacques Hadamard set out to explore how mathematicians invent new ideas, he considered the creative experiences of some of the greatest thinkers of his generation, such as George Polya, Claude Le;vi-Strauss, and Albert Einstein. It appeared that inspiration could strike anytime, particularly after an individual had worked hard on a problem for days and then turned attention to another activity. In exploring this phenomenon, Hadamard produced one of the most famous and cogent cases for the existence (...)
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  22.  62
    Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.
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  23.  2
    No Authorship Indicated (1999). Review of Animal Models of Human Psychology: Critique of Science, Ethics, and Policy. [REVIEW] Journal of Theoretical and Philosophical Psychology 19 (2):227-228.
    Reviews the book, Animal models of human psychology: Critique of science, ethics, and policy by Kenneth J. Shapiro . The principle focus of most of this text is on the present-day use of animals in psychological research. In particular, Shapiro examines contemporary animal models of eating disorders, showing how psychology came to rely so heavily on animal models in the first place and how prevalent scientific attitudes about the use of animals in the laboratory have (...)
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  24.  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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  25. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.
    What is the mind? How does it work? How does it influence behavior? Some psychologists hope to answer such questions in terms of concepts drawn from computer science and artificial intelligence. They test their theories by modeling mental processes in computers. This book shows how computer models are used to study many psychological phenomena--including vision, language, reasoning, and learning. It also shows that computer modeling involves differing theoretical approaches. Computational psychologists disagree about some basic questions. For instance, should the (...)
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  26.  2
    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.
    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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  27.  4
    Katharina Teresa Kraus (2016). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 24 (2):331-357.
    This paper shows why Kant's critique of empirical psychology should not be read as a scathing criticism of quantitative scientific psychology, but has valuable lessons to teach in support of it. By analysing Kant's alleged objections in the light of his critical theory of cognition, it provides a fresh look at the problem of quantifying first-person experiences, such as emotions and sense-perceptions. An in-depth discussion of applying the mathematical principles, which are defined in the Critique of Pure (...)
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  28.  24
    Jacques Hadamard (2008). An Essay on the Psychology of Invention in the Mathematical Field. Read Books.
    We are republishing these classic works in affordable, high quality, modern editions, using the original text and artwork.
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  29. 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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  30.  21
    Katharina T. Kraus (2016). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 24 (2):331-357.
    This paper shows why Kant's critique of empirical psychology should not be read as a scathing criticism of quantitative scientific psychology, but has valuable lessons to teach in support of it. By analysing Kant's alleged objections in the light of his critical theory of cognition, it provides a fresh look at the problem of quantifying first-person experiences, such as emotions and sense-perceptions. An in-depth discussion of applying the mathematical principles, which are defined in the Critique of Pure (...)
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  31.  21
    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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  32.  9
    Jean-François Bonnefon (2013). Formal Models of Reasoning in Cognitive Psychology. Argument and Computation 4 (1):1 - 3.
    (2013). Formal Models of Reasoning in Cognitive Psychology. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 1-3. doi: 10.1080/19462166.2013.767559.
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  33.  9
    Alberto Greco, Heuristic Value of Simulation Models in Psychology.
    Starting from some remarks about the use of models in psychology, Human Information Processing (henceforth called H.I.P.) models which sometimes use computer simulation will be examined. An attempt to show that simulation in psychology does not necessarily imply an H.I.P. approach is then made.
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  34.  4
    Richard E. Petty (2004). Multi-Process Models in Social Psychology Provide a More Balanced View of Social Thought and Action. Behavioral and Brain Sciences 27 (3):353-354.
    Krueger & Funder (K&F) describe social psychology as overly consumed with maladaptive heuristics and biases. This characterization fails to consider multi-process models of social thought and action. Such models, especially with respect to attitudes, have outlined the situational and individual difference variables responsible for determining when thoughts and actions are relatively thoughtful versus when they are more reliant on mental shortcuts.
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  35. Jerzy Brzezinski & Tadeusz Marek (eds.) (1990). Action and Performance: Models and Tests: Contributions to the Quantitative Psychology and its Methodology. Rodopi.
    Models and Tests : Contributions to the Quantitative Psychology and Its Methodology Jerzy Brzeziński, Tadeusz Marek. Marek Gaul INTERACTIONAL MODELS IN BEHAVIORAL RESEARCH Testing interaction on non-interval level of ...
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  36. Katharina Teresa Kraus (2016). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 24 (2):331-357.
    This paper shows why Kant's critique of empirical psychology should not be read as a scathing criticism of quantitative scientific psychology, but has valuable lessons to teach in support of it. By analysing Kant's alleged objections in the light of his critical theory of cognition, it provides a fresh look at the problem of quantifying first-person experiences, such as emotions and sense-perceptions. An in-depth discussion of applying the mathematical principles, which are defined in the Critique of Pure (...)
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  37.  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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  38.  48
    C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.
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  39.  5
    Andrea Loettgers (2007). Getting Abstract Mathematical Models in Touch with Nature. Science in Context 20 (1):97.
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  40.  21
    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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  41. 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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  42.  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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  43.  1
    Salahaddin Khalilov (2014). The Alternative Mathematical Models of the World. Philosophy Study 4 (5).
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  44.  10
    John V. Gillespie & Dina A. Zinnes (1975). Progressions in Mathematical Models of International Conflict. Synthese 31 (2):289 - 321.
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  45.  3
    Daniel Breslau & Yuval Yonay (1999). Beyond Metaphor: Mathematical Models in Economics as Empirical Research. Science in Context 12 (2).
  46.  32
    David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.
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  47. 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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  48.  23
    Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.
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  49.  7
    Mehmet Elgin (2010). Mathematical Models, Explanation, Laws, and Evolutionary Biology. History and Philosophy of the Life Sciences 32 (4).
  50.  6
    Dominik Wodarz & Martin A. Nowak (2002). Mathematical Models of HIV Pathogenesis and Treatment. Bioessays 24 (12):1178-1187.
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