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: 96.0
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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: 73.0
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  3. Christian Hennig (2010). Mathematical Models and Reality: A Constructivist Perspective. [REVIEW] Foundations of Science 15 (1):29-48.score: 69.3
    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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  4. Andrei G. Khromov (2001). Logical Self-Reference as a Model for Conscious Experience. Journal of Mathematical Psychology 45 (5):720-731.score: 69.0
  5. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 69.0
    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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  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: 69.0
     
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  7. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 69.0
    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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  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: 63.0
    A selection of 15 papers on choice modeling are presented in this volume.
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  9. Jacques Hadamard (1945/1996). The Mathematician's Mind: The Psychology of Invention in the Mathematical Field. Princeton University Press.score: 60.0
    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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  10. 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: 56.0
    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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  11. 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: 56.0
    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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  12. Philippe Tracqui (1995). From Passive Diffusion to Active Cellular Migration in Mathematical Models of Tumour Invasion. Acta Biotheoretica 43 (4).score: 56.0
    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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  13. 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: 55.3
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  14. 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: 54.0
    "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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  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: 54.0
    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. 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: 54.0
    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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  17. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons.. 2--493.score: 54.0
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  18. Mauro Dorato (2012). Mathematical Biology and the Existence of Biological Laws. In DieksD (ed.), Probabilities, Laws and Structure. Springer.score: 51.0
    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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  19. 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: 51.0
    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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  20. Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.score: 49.0
    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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  21. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.score: 48.0
    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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  22. Jacques Hadamard (2008/1954). An Essay on the Psychology of Invention in the Mathematical Field. Read Books.score: 48.0
    We are republishing these classic works in affordable, high quality, modern editions, using the original text and artwork.
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  23. Alberto Greco, Heuristic Value of Simulation Models in Psychology.score: 48.0
    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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  24. Katharina Teresa Kraus (2013). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 22 (1).score: 48.0
    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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  25. Jean-François Bonnefon (2013). Formal Models of Reasoning in Cognitive Psychology. Argument and Computation 4 (1):1 - 3.score: 48.0
    (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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  26. 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: 48.0
    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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  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: 48.0
    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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  28. Li-kung Shaw (1972). A Mathematical Model of Life and Living. Buenos Aires,Libreria Inglesa.score: 48.0
    [v. 1. Basic theories]--v. 2. Applications.--v. 3. Theory of plants and other essays.
     
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  29. Li-kung[from old catalog] Shaw (1959). A Mathematical Model of Human Life. Rosario, Argentina.score: 48.0
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  30. Robert McDowell Thrall (1966). Foundations [of Mathematics Oriented Toward the Concept of Mathematical Model]. Ann Arbor.score: 48.0
     
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  31. Dirk Schlimm (2009). Learning From the Existence of Models: On Psychic Machines, Tortoises, and Computer Simulations. Synthese 169 (3):521 - 538.score: 45.0
    Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation (...)
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  32. Joachim I. Krueger & David C. Funder (2004). Towards a Balanced Social Psychology: Causes, Consequences, and Cures for the Problem-Seeking Approach to Social Behavior and Cognition. Behavioral and Brain Sciences 27 (3):313-327.score: 45.0
    Mainstream social psychology focuses on how people characteristically violate norms of action through social misbehaviors such as conformity with false majority judgments, destructive obedience, and failures to help those in need. Likewise, they are seen to violate norms of reasoning through cognitive errors such as misuse of social information, self-enhancement, and an over-readiness to attribute dispositional characteristics. The causes of this negative research emphasis include the apparent informativeness of norm violation, the status of good behavior and judgment as unconfirmable (...)
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  33. 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.score: 45.0
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  34. Peter Godfrey-Smith (2005). Folk Psychology as a Model. Philosophers' Imprint 5 (6):1-16.score: 43.3
    I argue that everyday folk-psychological skill might best be explained in terms of the deployment of something like a model, in a specific sense drawn from recent philosophy of science. Theoretical models in this sense do not make definite commitments about the systems they are used to understand; they are employed with a particular kind of flexibility. This analysis is used to dissolve the eliminativism debate of the 1980s, and to transform a number of other questions about the status (...)
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  35. Jacques Ricard & Käty Ricard (1997). Mathematical Models in Biology. In. In Evandro Agazzi & György Darvas (eds.), Philosophy of Mathematics Today. Kluwer. 299--304.score: 43.0
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  36. 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: 42.0
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  37. Robert McCauley, Reduction: Models of Cross-Scientific Relations and Their Implications for the Psychology-Neuroscience Interface.score: 42.0
    University Abstract Philosophers have sought to improve upon the logical empiricists’ model of scientific reduction. While opportunities for integration between the cognitive and the neural sciences have increased, most philosophers, appealing to the multiple realizability of mental states and the irreducibility of consciousness, object to psychoneural reduction. New Wave reductionists offer a continuum of comparative goodness of intertheoretic mapping for assessing reductions. Their insistence on a unified view of intertheoretic relations obscures epistemically significant crossscientific relations and engenders dismissive conclusions about (...)
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  38. Joop Leo (2008). Modeling Relations. Journal of Philosophical Logic 37 (4):353 - 385.score: 42.0
    In the ordinary way of representing relations, the order of the relata plays a structural role, but in the states themselves such an order often does not seem to be intrinsically present. An alternative way to represent relations makes use of positions for the arguments. This is no problem for the love relation, but for relations like the adjacency relation and cyclic relations, different assignments of objects to the positions can give exactly the same states. This is a puzzling situation. (...)
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  39. C. L. Hamblin (1971). Mathematical Models of Dialogue. Theoria 37 (2):130-155.score: 42.0
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  40. Paul Humphreys (1995). Computational Science and Scientific Method. Minds and Machines 5 (4):499-512.score: 42.0
    The process of constructing mathematical models is examined and a case made that the construction process is an integral part of the justification for the model. The role of heuristics in testing and modifying models is described and some consequences for scientific methodology are drawn out. Three different ways of constructing the same model are detailed to demonstrate the claims made here.
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  41. Christopher Pincock (2012). Mathematical Models of Biological Patterns: Lessons From Hamilton's Selfish Herd. Biology and Philosophy 27 (4):481-496.score: 42.0
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  42. Adam Morton (1993). Mathematical Models: Questions of Trustworthiness. British Journal for the Philosophy of Science 44 (4):659-674.score: 42.0
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  43. David Berlinski (1975). Mathematical Models of the World. Synthese 31 (2):211 - 227.score: 42.0
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  44. Eugen Altschul & Erwin Biser (1948). The Validity of Unique Mathematical Models in Science. Philosophy of Science 15 (1):11-24.score: 42.0
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  45. 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: 42.0
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  46. Reinout W. Wiers, Remco Havermans, Roland Deutsch & Alan W. Stacy (2008). A Mismatch with Dual Process Models of Addiction Rooted in Psychology. Behavioral and Brain Sciences 31 (4):460-460.score: 42.0
    The model of addiction proposed by Redish et al. shows a lack of fit with recent data and models in psychological studies of addiction. In these dual process models, relatively automatic appetitive processes are distinguished from explicit goal-directed expectancies and motives, whereas these are all grouped together in the planning system in the Redish et al. model. Implications are discussed.
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  47. Margaret A. Boden (1981). Minds And Mechanisms: Philosophical Psychology And Computational Models. Ithaca: Cornell University Press.score: 42.0
  48. Joseph S. Alper & Robert V. Lange (1984). Mathematical Models for Gene–Culture Coevolution. Behavioral and Brain Sciences 7 (4):739.score: 42.0
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  49. 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: 42.0
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  50. 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: 42.0
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