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: 288.0
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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: 219.0
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  3. Andrei G. Khromov (2001). Logical Self-Reference as a Model for Conscious Experience. Journal of Mathematical Psychology 45 (5):720-731.score: 207.0
  4. Alex Mintz, Nehemia Geva & Karl Derouen (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 207.0
    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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  5. Alex Mintz, Nehemia Geva & Karl Derouen Jr (1994). Mathematical Models of Foreign Policy Decision-Making: Compensatory Vs. Noncompensatory. Synthese 100 (3):441 - 460.score: 207.0
    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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  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: 207.0
     
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  7. 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: 189.0
    A selection of 15 papers on choice modeling are presented in this volume.
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  8. Anatol Rapoport (1963). Mathematical Models of Social Interaction. In D. Luce (ed.), Handbook of Mathematical Psychology. John Wiley & Sons.. 2--493.score: 162.0
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  9. 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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  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: 124.0
    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: 120.0
    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. Mike Page (2000). Connectionist Modelling in Psychology: A Localist Manifesto. Behavioral and Brain Sciences 23 (4):443-467.score: 117.0
    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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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: 108.0
    "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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  18. 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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  19. Laurence D. Smith (1990). Models, Mechanisms, and Explanation in Behavior Theory: The Case of Hull Versus Spence. Behavior and Philosophy 18 (1):1 - 18.score: 108.0
    The neobehaviorist Clark L. Hull and his disciple Kenneth Spence shared in common many views on the nature of science and the role of theories in psychology. However, a telling exchange in their correspondence of the early 1940s reveals a disagreement over the nature of intervening variables in behavior theory. Spence urged Hull to abandon his interpretations of intervening variables in terms of physiological models in favor of positivistic, purely mathematical interpretations that conflicted with Hull's mechanistic (...)
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  20. 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: 108.0
    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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  21. Lance Rips (2011). Lines of Thought: Central Concepts in Cognitive Psychology. OUP USA.score: 108.0
    Lines of Thought addresses how we are able to think about abstract possibilities: How can we think about math, despite the immateriality of numbers, sets, and other mathematical entities? How are we able to think about what might have happened if history had taken a different turn? Questions like these turn up in nearly every part of cognitive science, and they are central to our human position of having only limited knowledge concerning what is or might be true. Because (...)
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  22. Ephraim Nissan (2009). Computational Models of the Emotions: From Models of the Emotions of the Individual to Modelling the Emerging Irrational Behaviour of Crowds. [REVIEW] AI and Society 24 (4):403-414.score: 108.0
    Computational models of emotions have been thriving and increasingly popular since the 1990s. Such models used to be concerned with the emotions of individual agents when they interact with other agents. Out of the array of models for the emotions, we are going to devote special attention to the approach in Adamatzky’s Dynamics of Crowd-Minds. The reason it stands out, is that it considers the crowd, rather than the individual agent. It fits in computational intelligence. It works (...)
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  23. 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: 108.0
    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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  24. Joseph A. King, Christopher Donkin, Franziska M. Korb & Tobias Egner (2012). Model-Based Analysis of Context-Specific Cognitive Control. Frontiers in Psychology 3.score: 108.0
    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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  25. Leonid Grinin, Andrey Korotayev & Sergey Malkov (2010). A Mathematical Model of Juglar Cycles and the Current Global Crisis. In Leonid Grinin, Peter Herrmann, Andrey Korotayev & Arno Tausch (eds.), History & Mathematics: Processes and Models of Global Dynamics.score: 103.0
    The article presents a verbal and mathematical model of medium-term business cycles (with a characteristic period of 7–11 years) known as Juglar cycles. The model takes into account a number of approaches to the analysis of such cycles; in the meantime it also takes into account some of the authors' own generalizations and additions that are important for understanding the internal logic of the cycle, its variability and its peculiarities in the present-time conditions. The authors argue that the most (...)
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  26. 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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  27. 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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  28. Paul Humphreys (2013). Data Analysis: Models or Techniques? [REVIEW] Foundations of Science 18 (3):579-581.score: 98.0
    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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  29. Jaap M. J. Murre, Antonio G. Chessa & Martijn Meeter (2013). A Mathematical Model of Forgetting and Amnesia. Frontiers in Psychology 4.score: 97.0
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  30. Margaret A. Boden (1988). Computer Models On Mind: Computational Approaches In Theoretical Psychology. Cambridge University Press.score: 96.0
    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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  31. Alberto Greco, Heuristic Value of Simulation Models in Psychology.score: 96.0
    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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  32. Katharina Teresa Kraus (2013). Quantifying Inner Experience?—Kant's Mathematical Principles in the Context of Empirical Psychology. European Journal of Philosophy 22 (2).score: 96.0
    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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  33. Jacques Hadamard (2008/1954). An Essay on the Psychology of Invention in the Mathematical Field. Read Books.score: 96.0
    We are republishing these classic works in affordable, high quality, modern editions, using the original text and artwork.
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  34. 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: 96.0
    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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  35. Jean-François Bonnefon (2013). Formal Models of Reasoning in Cognitive Psychology. Argument and Computation 4 (1):1 - 3.score: 96.0
    (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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  36. 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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  37. 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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  38. Li-kung[from old catalog] Shaw (1959). A Mathematical Model of Human Life. Rosario, Argentina.score: 96.0
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  39. Robert McDowell Thrall (1966). Foundations [of Mathematics Oriented Toward the Concept of Mathematical Model]. Ann Arbor.score: 96.0
     
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  40. Jonathon Love Andrew Heathcote (2012). Linear Deterministic Accumulator Models of Simple Choice. Frontiers in Psychology 3.score: 94.0
    We examine theories of simple choice as a race among evidence accumulation processes. We focus on the class of deterministic race models, which assume that the effects of fluctuations in the parameters of the accumulation processes between choice trials (between-choice noise) dominate the effects of fluctuations occurring while making a choice (within-choice noise) in behavioural data (i.e., response times and choices). The latter deterministic approximation, when combined with the assumption that accumulation is linear, leads to a class of (...) that can be readily applied to simple-choice behaviour because they are computationally tractable. We develop a new and mathematically simple exemplar within the class of linear deterministic models, the Lognormal Race (LNR). We then examine how the LNR, and another widely applied linear deterministic model, Brown and Heathcote’s (2008) LBA, account for a range of benchmark simple-choice effects in lexical-decision task data reported by Wagenmakers, Ratcliff, Gomez and McKoon (2008). (shrink)
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  41. Andrew Heathcote & Jonathon Love (2012). Linear Deterministic Accumulator Models of Simple Choice. Frontiers in Psychology 3.score: 94.0
    We examine theories of simple choice as a race among evidence accumulation processes. We focus on the class of deterministic race models, which assume that the effects of fluctuations in the parameters of the accumulation processes between choice trials (between-choice noise) dominate the effects of fluctuations occurring while making a choice (within-choice noise) in behavioural data (i.e., response times and choices). The latter deterministic approximation, when combined with the assumption that accumulation is linear, leads to a class of (...) that can be readily applied to simple-choice behaviour because they are computationally tractable. We develop a new and mathematically simple exemplar within the class of linear deterministic models, the Lognormal Race (LNR). We then examine how the LNR, and another widely applied linear deterministic model, Brown and Heathcote’s (2008) LBA, account for a range of benchmark simple-choice effects in lexical-decision task data reported by Wagenmakers, Ratcliff, Gomez and McKoon (2008). (shrink)
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  42. Dirk Schlimm (2009). Learning From the Existence of Models: On Psychic Machines, Tortoises, and Computer Simulations. Synthese 169 (3):521 - 538.score: 90.0
    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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  43. 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: 90.0
    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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  44. 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: 90.0
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  45. Pieter A. M. Seuren, Venanizo Capretta & Herman Geuvers (2001). The Logic and Mathematics of Occasion Sentences. Linguistics and Philosophy 24 (5):531-595.score: 90.0
    The prime purpose of this paper is, first, to restore to discourse-bound occasion sentences their rightful central place in semantics and secondly, taking these as the basic propositional elements in the logical analysis of language, to contribute to the development of an adequate logic of occasion sentences and a mathematical (Boolean) foundation for such a logic, thus preparing the ground for more adequate semantic, logical and mathematical foundations of the study of natural language. Some of the insights elaborated (...)
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  46. Pieter A. M. Seuren, Venanzio Capretta & Herman Geuvers (2001). The Logic and Mathematics of Occasion Sentences. Linguistics and Philosophy 24 (5):531 - 595.score: 90.0
    The prime purpose of this paper is, first, to restore to discourse-bound occasion sentences their rightful central place in semantics and secondly, taking these as the basic propositional elements in the logical analysis of language, to contribute to the development of an adequate logic of occasion sentences and a mathematical (Boolean) foundation for such a logic, thus preparing the ground for more adequate semantic, logical and mathematical foundations of the study of natural language. Some of the insights elaborated (...)
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  47. Peter Richerson, Cultural Evolutionary Theory: A Synthetic Theory for Fragmented Disciplines.score: 87.0
    Cultural evolutionary theory, like other evolutionary theories, links individual-level and population or society-level phenomena. It provides numerous bridges between social psychology and other disciplines and sub-disciplines. The theory uses mathematical models to understand the population-level consequences of the individual-level processes of individual and social learning. The theory has been used to explain group-level behavior such as cooperation, altruism, and the cross-cultural variation associated with social institutions. The empirical study of social psychological assumptions of such models and (...)
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  48. Keith Stenning (2012). To Naturalize or Not to Naturalize? An Issue for Cognitive Science as Well as Anthropology. Topics in Cognitive Science 4 (3):413-419.score: 87.0
    Several of Beller, Bender, and Medin’s (2012) issues are as relevant within cognitive science as between it and anthropology. Knowledge-rich human mental processes impose hermeneutic tasks, both on subjects and researchers. Psychology's current philosophy of science is ill suited to analyzing these: Its demand for ‘‘stimulus control’’ needs to give way to ‘‘negotiation of mutual interpretation.’’ Cognitive science has ways to address these issues, as does anthropology. An example from my own work is about how defeasible logics are (...) models of some aspects of simple hermeneutic processes. They explain processing relative to databases of knowledge and belief—that is, content. A specific example is syllogistic reasoning, which raises issues of experimenters’ interpretations of subjects’ reasoning. Science, especially since the advent of understandings of computation, does not have to be reductive. How does this approach transfer onto anthropological topics? Recent cognitive science approaches to anthropological topics have taken a reductive stance in terms of modules. We end with some speculations about a different cognitive approach to, for example, religion. (shrink)
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  49. Peter Godfrey-Smith (2005). Folk Psychology as a Model. Philosophers' Imprint 5 (6):1-16.score: 86.7
    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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  50. Jacques Ricard & Käty Ricard (1997). Mathematical Models in Biology. In. In Evandro Agazzi & György Darvas (eds.), Philosophy of Mathematics Today. Kluwer. 299--304.score: 86.0
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