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  1. Choices, Values, and Frames.Daniel Kahneman & Amos Tversky (eds.) - 2000 - Cambridge University Press.
    This book presents the definitive exposition of 'prospect theory', a compelling alternative to the classical utility theory of choice. Building on the 1982 volume, Judgement Under Uncertainty, this book brings together seminal papers on prospect theory from economists, decision theorists, and psychologists, including the work of the late Amos Tversky, whose contributions are collected here for the first time. While remaining within a rational choice framework, prospect theory delivers more accurate, empirically verified predictions in key test cases, as well as (...)
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  • A theory of memory retrieval.Roger Ratcliff - 1978 - Psychological Review 85 (2):59-108.
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  • Probabilistic mental models: A Brunswikian theory of confidence.Gerd Gigerenzer, Ulrich Hoffrage & Heinz Kleinbölting - 1991 - Psychological Review 98 (4):506-528.
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  • Probability Theory. The Logic of Science.Edwin T. Jaynes - 2002 - Cambridge University Press: Cambridge. Edited by G. Larry Bretthorst.
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  • On small differences in sensation.Charles Peirce & Joseph Jastrow - 1884 - Memoirs of the National Academy of Sciences 3:75-83.
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  • Simple Heuristics That Make Us Smart.Gerd Gigerenzer, Peter M. Todd & A. B. C. Research Group - 1999 - New York, NY, USA: Oxford University Press USA. Edited by Peter M. Todd.
    Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is about (...)
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  • Toward a method of selecting among computational models of cognition.Mark A. Pitt, In Jae Myung & Shaobo Zhang - 2002 - Psychological Review 109 (3):472-491.
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  • Intuitive evaluation of likelihood judgment producers: evidence for a confidence heuristic.Paul Price & Eric Stone - 2004 - Journal of Behavioral Decision Making 17 (1):39–57.
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  • Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  • How persuasive is a good fit? A comment on theory testing.Seth Roberts & Harold Pashler - 2000 - Psychological Review 107 (2):358-367.
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  • Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment.Jerome R. Busemeyer & James T. Townsend - 1993 - Psychological Review 100 (3):432-459.
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  • An exemplar-based random walk model of speeded classification.Robert M. Nosofsky & Thomas J. Palmeri - 1997 - Psychological Review 104 (2):266-300.
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  • Sequential sampling models of human text classification.Michael D. Lee & Elissa Y. Corlett - 2003 - Cognitive Science 27 (2):159-193.
    Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from (...)
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