35 found
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  1.  16
    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.
  2.  86
    Can Quantum Probability Provide a New Direction for Cognitive Modeling?Emmanuel M. Pothos & Jerome R. Busemeyer - 2013 - Behavioral and Brain Sciences 36 (3):255-274.
    Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...)
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  3.  8
    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.
  4. 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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  5.  77
    A Quantum Question Order Model Supported by Empirical Tests of an A Priori and Precise Prediction.Zheng Wang & Jerome R. Busemeyer - 2013 - Topics in Cognitive Science 5 (4):689-710.
    Question order effects are commonly observed in self-report measures of judgment and attitude. This article develops a quantum question order model (the QQ model) to account for four types of question order effects observed in literature. First, the postulates of the QQ model are presented. Second, an a priori, parameter-free, and precise prediction, called the QQ equality, is derived from these mathematical principles, and six empirical data sets are used to test the prediction. Third, a new index is derived from (...)
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  6.  11
    Two-Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence.Timothy J. Pleskac & Jerome R. Busemeyer - 2010 - Psychological Review 117 (3):864-901.
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  7. A Quantum Probability Account of Order Effects in Inference.Jennifer S. Trueblood & Jerome R. Busemeyer - 2011 - Cognitive Science 35 (8):1518-1552.
    Order of information plays a crucial role in the process of updating beliefs across time. In fact, the presence of order effects makes a classical or Bayesian approach to inference difficult. As a result, the existing models of inference, such as the belief-adjustment model, merely provide an ad hoc explanation for these effects. We postulate a quantum inference model for order effects based on the axiomatic principles of quantum probability theory. The quantum inference model explains order effects by transforming a (...)
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  8.  60
    Quantum Cognition: A New Theoretical Approach to Psychology.Peter D. Bruza, Zheng Wang & Jerome R. Busemeyer - 2015 - Trends in Cognitive Sciences 19 (7):383-393.
  9. Microprocess Models of Decision Making.Jerome R. Busemeyer & Joseph G. Johnson - 2008 - In Ron Sun (ed.), The Cambridge Handbook of Computational Psychology. Cambridge University Press. pp. 302--321.
     
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  10.  9
    The Rational Status of Quantum Cognition.Emmanuel M. Pothos, Jerome R. Busemeyer, Richard M. Shiffrin & James M. Yearsley - 2017 - Journal of Experimental Psychology: General 146 (7):968-987.
  11.  8
    The Conjunction Fallacy, Confirmation, and Quantum Theory: Comment on Tentori, Crupi, and Russo.Jerome R. Busemeyer, Zheng Wang, Emmanuel M. Pothos & Jennifer S. Trueblood - 2015 - Journal of Experimental Psychology: General 144 (1):236-243.
  12.  13
    A Quantum Geometric Model of Similarity.Emmanuel M. Pothos, Jerome R. Busemeyer & Jennifer S. Trueblood - 2013 - Psychological Review 120 (3):679-696.
  13.  14
    Comparison of Decision Learning Models Using the Generalization Criterion Method.Woo‐Young Ahn, Jerome R. Busemeyer, Eric‐Jan Wagenmakers & Julie C. Stout - 2008 - Cognitive Science 32 (8):1376-1402.
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  14.  7
    Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-Based Decisions.Jerome R. Busemeyer, Sebastian Gluth, Jörg Rieskamp & Brandon M. Turner - 2019 - Trends in Cognitive Sciences 23 (3):251-263.
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  15.  7
    A Dynamic, Stochastic, Computational Model of Preference Reversal Phenomena.Joseph G. Johnson & Jerome R. Busemeyer - 2005 - Psychological Review 112 (4):841-861.
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  16.  3
    An Adaptive Approach to Human Decision Making: Learning Theory, Decision Theory, and Human Performance.Jerome R. Busemeyer & In Jae Myung - 1992 - Journal of Experimental Psychology: General 121 (2):177-194.
  17.  11
    A Quantum Probability Model of Causal Reasoning.Jennifer S. Trueblood & Jerome R. Busemeyer - 2012 - Frontiers in Psychology 3.
  18.  12
    Sometimes It Does Hurt to Ask: The Constructive Role of Articulating Impressions.Lee C. White, Emmanuel M. Pothos & Jerome R. Busemeyer - 2014 - Cognition 133 (1):48-64.
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  19.  47
    Quantum Cognition: Key Issues and Discussion.Jerome R. Busemeyer & Zheng Wang - 2014 - Topics in Cognitive Science 6 (1):43-46.
    Quantum cognition is an emerging field that uses mathematical principles of quantum theory to help formalize and understand cognitive systems and processes. The topic on the potential of using quantum theory to build models of cognition (Volume 5, issue 4) introduces and synthesizes its new development through an introduction and six core articles. The current issue presents 14 commentaries on the core articles. Five key issues surface, some of which are interestingly controversial and debatable as expected for a new emerging (...)
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  20.  3
    A Probabilistic, Dynamic, and Attribute-Wise Model of Intertemporal Choice.Junyi Dai & Jerome R. Busemeyer - 2014 - Journal of Experimental Psychology: General 143 (4):1489-1514.
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  21.  13
    Quantum Principles in Psychology: The Debate, the Evidence, and the Future.Emmanuel M. Pothos & Jerome R. Busemeyer - 2013 - Behavioral and Brain Sciences 36 (3):310-327.
    The attempt to employ quantum principles for modeling cognition has enabled the introduction of several new concepts in psychology, such as the uncertainty principle, incompatibility, entanglement, and superposition. For many commentators, this is an exciting opportunity to question existing formal frameworks (notably classical probability theory) and explore what is to be gained by employing these novel conceptual tools. This is not to say that major empirical challenges are not there. For example, can we definitely prove the necessity for quantum, as (...)
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  22.  6
    Theoretical Developments in Decision Field Theory: Comment on Tsetsos, Usher, and Chater.Jared M. Hotaling, Jerome R. Busemeyer & Jiyun Li - 2010 - Psychological Review 117 (4):1294-1298.
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  23.  5
    Dynamic and Consequential Consistency of Choices Between Paths of Decision Trees.Jerome R. Busemeyer, Ethan Weg, Rachel Barkan, Xuyang Li & Zhengping Ma - 2000 - Journal of Experimental Psychology: General 129 (4):530-545.
  24.  8
    An Improved Cognitive Model of the Iowa and Soochow Gambling Tasks with Regard to Model Fitting Performance and Tests of Parameter Consistency.Junyi Dai, Rebecca Kerestes, Daniel J. Upton, Jerome R. Busemeyer & Julie C. Stout - 2015 - Frontiers in Psychology 6.
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  25.  5
    Cognitive Science Contributions to Decision Science.Jerome R. Busemeyer - 2015 - Cognition 135:43-46.
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  26. Multiple-Stage Decision-Making: The Effect of Planning Horizon Length on Dynamic Consistency.Joseph G. Johnson & Jerome R. Busemeyer - 2001 - Theory and Decision 51 (2/4):217-246.
    Many decisions involve multiple stages of choices and events, and these decisions can be represented graphically as decision trees. Optimal decision strategies for decision trees are commonly determined by a backward induction analysis that demands adherence to three fundamental consistency principles: dynamic, consequential, and strategic. Previous research found that decision-makers tend to exhibit violations of dynamic and strategic consistency at rates significantly higher than choice inconsistency across various levels of potential reward. The current research extends these findings under new conditions; (...)
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  27.  41
    DFT-D: A Cognitive-Dynamical Model of Dynamic Decision Making.Jared M. Hotaling & Jerome R. Busemeyer - 2012 - Synthese 189 (S1):67-80.
    The study of decision making has traditionally been dominated by axiomatic utility theories. More recently, an alternative approach, which focuses on the micro-mechanisms of the underlying deliberation process, has been shown to account for several "paradoxes" in human choice behavior for which simple utility-based approaches cannot. Decision field theory (DFT) is a cognitive-dynamical model of decision making and preferential choice, built on the fundamental principle that decisions are based on the accumulation of subjective evaluations of choice alternatives until a threshold (...)
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  28. Preferences Constructed From Dynamic Micro-Processing Mechanisms.Jerome R. Busemeyer, Joseph G. Johnson & Ryan K. Jessup - 2006 - In Sarah Lichtenstein & Paul Slovic (eds.), The Construction of Preference. Cambridge University Press. pp. 220--234.
     
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  29.  3
    Contrast Effects or Loss Aversion? Comment on Usher and McClelland.Jerome R. Busemeyer, James T. Townsend, Adele Diederich & Rachel Barkan - 2005 - Psychological Review 112 (1):253-255.
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  30.  6
    Hilbert Space Multidimensional Theory.Jerome R. Busemeyer & Zheng Wang - 2018 - Psychological Review 125 (4):572-591.
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  31.  15
    Quantum Probability Theory as a Common Framework for Reasoning and Similarity.Jennifer S. Trueblood, Emmanuel M. Pothos & Jerome R. Busemeyer - 2014 - Frontiers in Psychology 5.
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  32.  12
    Interference Effects of Categorization on Decision Making.Zheng Wang & Jerome R. Busemeyer - 2016 - Cognition 150:133-149.
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  33.  10
    Progress and Current Challenges with the Quantum Similarity Model.Emmanuel M. Pothos, Albert Barque-Duran, James M. Yearsley, Jennifer S. Trueblood, Jerome R. Busemeyer & James A. Hampton - 2015 - Frontiers in Psychology 6.
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  34.  8
    A Case for Limited Prescriptive Normativism.Emmanuel M. Pothos & Jerome R. Busemeyer - 2011 - Behavioral and Brain Sciences 34 (5):264-265.
    Understanding cognitive processes with a formal framework necessitates some limited, internal prescriptive normativism. This is because it is not possible to endorse the psychological relevance of some axioms in a formal framework, but reject that of others. The empirical challenge then becomes identifying the remit of different formal frameworks, an objective consistent with the descriptivism Elqayam & Evans (E&E) advocate.
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  35.  3
    "Two-Stage Dynamic Signal Detection: A Theory of Choice, Decision Time, and Confidence": Erratum.Timothy J. Pleskac & Jerome R. Busemeyer - 2011 - Psychological Review 118 (1):56-56.
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