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  1. (1 other version)Judgment under Uncertainty: Heuristics and Biases.Amos Tversky & Daniel Kahneman - 1974 - Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...)
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  • Cybernetics or Control and Communication in the Animal and the Machine.N. Wiener - 1948 - Revue Philosophique de la France Et de l'Etranger 141:578-580.
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  • Rational choice and the structure of the environment.Herbert A. Simon - 1955 - Psychological Review 63 (2):129-138.
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  • Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources.Falk Lieder & Thomas L. Griffiths - forthcoming - Behavioral and Brain Sciences:1-85.
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  • A quantum theoretical explanation for probability judgment errors.Jerome R. Busemeyer, Emmanuel M. Pothos, Riccardo Franco & Jennifer S. Trueblood - 2011 - Psychological Review 118 (2):193-218.
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  • The Potential of Using Quantum Theory to Build Models of Cognition.Zheng Wang, Jerome R. Busemeyer, Harald Atmanspacher & Emmanuel M. Pothos - 2013 - Topics in Cognitive Science 5 (4):672-688.
    Quantum cognition research applies abstract, mathematical principles of quantum theory to inquiries in cognitive science. It differs fundamentally from alternative speculations about quantum brain processes. This topic presents new developments within this research program. In the introduction to this topic, we try to answer three questions: Why apply quantum concepts to human cognition? How is quantum cognitive modeling different from traditional cognitive modeling? What cognitive processes have been modeled using a quantum account? In addition, a brief introduction to quantum probability (...)
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  • Theory of Games and Economic Behavior.John von Neumann & Oskar Morgenstern - 1944 - Science and Society 9 (4):366-369.
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  • Hierarchically organized behavior and its neural foundations: A reinforcement-learning perspective.Andrew C. Barto Matthew M. Botvinick, Yael Niv - 2009 - Cognition 113 (3):262.
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  • Overrepresentation of extreme events in decision making reflects rational use of cognitive resources.Falk Lieder, Thomas L. Griffiths & Ming Hsu - 2018 - Psychological Review 125 (1):1-32.
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  • Planning as inference.Matthew Botvinick & Marc Toussaint - 2012 - Trends in Cognitive Sciences 16 (10):485-488.
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  • Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective.Matthew M. Botvinick, Yael Niv & Andew G. Barto - 2009 - Cognition 113 (3):262-280.
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  • Can motto-goals outperform learning and performance goals? Influence of goal setting on performance and affect in a complex problem solving task.Miriam Sophia Rohe, Joachim Funke, Maja Storch & Julia Weber - 2016 - Journal of Dynamic Decision Making 2 (1):1-15.
    In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model, so-called motto-goals should activate a person’s resources through positive affect. It was (...)
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