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

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  1. Evert Willem Beth (1966). Mathematical Epistemology and Psychology. New York, Gordon and Breach.score: 360.0
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  2. Curt F. Fey (1961). An Investigation of Some Mathematical Models for Learning. Journal of Experimental Psychology 61 (6):455.score: 303.0
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  3. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 291.0
    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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  4. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 291.0
    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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  5. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons.. 2--493.score: 246.0
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  6. John Dagsvik (1983). Discrete Dynamic Choice: An Extension of the Choice Models of Thurstone and Luce. I Kommisjon Hos H. Aschehoug Og Universitetsforlaget.score: 225.0
     
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  7. Andrei G. Khromov (2001). Logical Self-Reference as a Model for Conscious Experience. Journal of Mathematical Psychology 45 (5):720-731.score: 213.0
  8. 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..score: 195.0
    A selection of 15 papers on choice modeling are presented in this volume.
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  9. 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 (...)
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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.score: 180.0
    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 (...)
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  11. Jacques Hadamard (1945/1996). The Mathematician's Mind: The Psychology of Invention in the Mathematical Field. Princeton University Press.score: 168.0
    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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  12. 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: 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 (...)
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  13. 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 (...)
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  14. 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
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  15. 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 (...)
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  16. Carsten Held, Markus Knauff & Gottfried Vosgerau (eds.) (2006). Mental Models and the Mind: Current Developments in Cognitive Psychology, Neuroscience, and Philosophy of Mind. Elsevier.score: 156.0
    "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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  17. Thomas Sturm (2006). Is There a Problem with Mathematical Psychology in the Eighteenth Century? A Fresh Look at Kant’s Old Argument. . Journal of the History of the Behavioral Sciences 42:353-377.score: 156.0
    Common opinion ascribes to Immanuel Kant the view that psychology cannot become a science properly so called, because it cannot be mathematized. It is equally common to claim that this reflects the state of the art of his times; that the quantification of the mind was not achieved during the eighteenth century, while it was so during the nineteenth century; or that Kant's so-called “impossibility claim” was refuted by nineteenth-century developments, which in turn opened one path for psychology (...)
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  18. Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.score: 153.0
    Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that (...)
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  19. 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 (...)
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  20. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.score: 144.0
    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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  21. Alberto Greco, Heuristic Value of Simulation Models in Psychology.score: 144.0
    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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  22. Katharina T. Kraus (2013). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 22 (3):n/a-n/a.score: 144.0
    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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  23. Jacques Hadamard (2008/1954). An Essay on the Psychology of Invention in the Mathematical Field. Read Books.score: 144.0
    We are republishing these classic works in affordable, high quality, modern editions, using the original text and artwork.
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  24. Jean-François Bonnefon (2013). Formal Models of Reasoning in Cognitive Psychology. Argument and Computation 4 (1):1 - 3.score: 144.0
    (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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  25. 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.score: 144.0
    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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  26. 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
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  27. C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.score: 140.0
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  28. Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.score: 140.0
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  29. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.score: 140.0
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  30. Adam Morton (1993). Mathematical Models: Questions of Trustworthiness. British Journal for the Philosophy of Science 44 (4):659-674.score: 140.0
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  31. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.score: 140.0
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  32. Mehmet Elgin (2010). Mathematical Models, Explanation, Laws, and Evolutionary Biology. History and Philosophy of the Life Sciences 32 (4).score: 140.0
  33. 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
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  34. Joseph Goguen (2006). Mathematical Models of Cognitive Space and Time. In D. Andler, M. Okada & I. Watanabe (eds.), Reasoning and Cognition. 125--128.score: 140.0
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  35. 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: 140.0
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  36. 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
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  37. Richard M. Warren (1989). The Use of Mathematical Models in Perceptual Theory. Behavioral and Brain Sciences 12 (4):776.score: 140.0
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  38. Dominik Wodarz & Martin A. Nowak (2002). Mathematical Models of HIV Pathogenesis and Treatment. Bioessays 24 (12):1178-1187.score: 140.0
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  39. John C. Barrett (1974). Conception and Birth Mathematical Models of Conception and Birth Mindel C. Sheps Jane A. Menken. BioScience 24 (10):598-598.score: 140.0
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  40. S. G. Bloom & G. E. Raines (1971). Mathematical Models for Predicting the Transport of Radionuclides in a Marine Environment. BioScience 21 (12):691-696.score: 140.0
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  41. Josephine Donaghy (2014). Temporal Decomposition: A Strategy for Building Mathematical Models of Complex Metabolic Systems. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 48:1-11.score: 140.0
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  42. 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.score: 140.0
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  43. Andrea Loettgers (2007). Getting Abstract Mathematical Models in Touch with Nature. Science in Context 20 (1):97.score: 140.0
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  44. Joseph S. Alper & Robert V. Lange (1984). Mathematical Models for Gene–Culture Coevolution. Behavioral and Brain Sciences 7 (4):739.score: 140.0
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  45. Daniel Breslau & Yuval Yonay (1999). Beyond Metaphor: Mathematical Models in Economics as Empirical Research. Science in Context 12 (2).score: 140.0
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  46. Valerie Debuiche (2013). Leibnizian Expression and its Mathematical Models. Journal of the History of Philosophy 51 (3):409-439.score: 140.0
     
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  47. John V. Gillespie & Dina A. Zinnes (1975). Progressions in Mathematical Models of International Conflict. Synthese 31 (2):289 - 321.score: 140.0
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  48. Robert J. Good (1977). Modeling Cell Rearrangement Mathematical Models for Cell Rearrangement G. D. Mostow. BioScience 27 (12):816-816.score: 140.0
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  49. Ileana Maria Greca & Marco Antonio Moreira (2002). Mental, Physical, and Mathematical Models in the Teaching and Learning of Physics. Science Education 86 (1):106-121.score: 140.0
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  50. L. E. Grinin & A. V. Korotayev (2009). Urbanization and Political Instability: To the Working Out Mathematical Models of Political Processes. Polis 4:34-52.score: 140.0
     
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