34 found
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Michael D. Lee [32]Michael David Lee [2]
  1.  30
    A Survey of Model Evaluation Approaches With a Tutorial on Hierarchical Bayesian Methods.Richard M. Shiffrin, Michael D. Lee, Woojae Kim & Eric-Jan Wagenmakers - 2008 - Cognitive Science 32 (8):1248-1284.
  2.  85
    Sampling Assumptions in Inductive Generalization.Daniel J. Navarro, Matthew J. Dry & Michael D. Lee - 2012 - Cognitive Science 36 (2):187-223.
    Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, (...)
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  3.  17
    A Model of Knower‐Level Behavior in Number Concept Development.Michael D. Lee & Barbara W. Sarnecka - 2010 - Cognitive Science 34 (1):51-67.
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  4.  6
    Bayesian Statistical Inference in Psychology: Comment on Trafimow.Michael D. Lee & Eric-Jan Wagenmakers - 2005 - Psychological Review 112 (3):662-668.
  5. The Wisdom of the Crowd in Combinatorial Problems.Sheng Kung Michael Yi, Mark Steyvers, Michael D. Lee & Matthew J. Dry - 2012 - Cognitive Science 36 (3):452-470.
    The “wisdom of the crowd” phenomenon refers to the finding that the aggregate of a set of proposed solutions from a group of individuals performs better than the majority of individual solutions. Most often, wisdom of the crowd effects have been investigated for problems that require single numerical estimates. We investigate whether the effect can also be observed for problems where the answer requires the coordination of multiple pieces of information. We focus on combinatorial problems such as the planar Euclidean (...)
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  6. Learned Categorical Perception for Natural Faces.Daniel Joseph Navarro, Michael David Lee & H. C. Nikkerud - manuscript
     
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  7.  15
    Exemplars, Prototypes, Similarities, and Rules in Category Representation: An Example of Hierarchical Bayesian Analysis.Michael D. Lee & Wolf Vanpaemel - 2008 - Cognitive Science 32 (8):1403-1424.
  8.  24
    Quantum Models of Cognition as Orwellian Newspeak.Michael D. Lee & Wolf Vanpaemel - 2013 - Behavioral and Brain Sciences 36 (3):295-296.
  9.  15
    Sequential Sampling Models of Human Text Classification.Michael D. Lee & Elissa Y. Corlett - 2003 - Cognitive Science 27 (2):159-193.
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  10.  12
    A Hierarchical Bayesian Model of Human Decision‐Making on an Optimal Stopping Problem.Michael D. Lee - 2006 - Cognitive Science 30 (3):1-26.
  11.  4
    Time-Varying Boundaries for Diffusion Models of Decision Making and Response Time.Shunan Zhang, Michael D. Lee, Joachim Vandekerckhove, Gunter Maris & Eric-Jan Wagenmakers - 2014 - Frontiers in Psychology 5.
  12.  17
    Number-Knower Levels in Young Children: Insights From Bayesian Modeling.Michael D. Lee & Barbara W. Sarnecka - 2011 - Cognition 120 (3):391-402.
  13.  23
    Emergent and Structured Cognition in Bayesian Models: Comment on Griffiths Et Al. And McClelland Et Al.Michael D. Lee - 2010 - Trends in Cognitive Sciences 14 (8):345-346.
  14.  27
    The Perceptual Organization of Point Constellations.Matthew J. Dry, Daniel J. Navarro, Kym Preiss & Michael D. Lee - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  15. Inferring Expertise in Knowledge and Prediction Ranking Tasks.Michael D. Lee, Mark Steyvers, Mindy de Young & Brent Miller - 2012 - Topics in Cognitive Science 4 (1):151-163.
    We apply a cognitive modeling approach to the problem of measuring expertise on rank ordering problems. In these problems, people must order a set of items in terms of a given criterion (e.g., ordering American holidays through the calendar year). Using a cognitive model of behavior on this problem that allows for individual differences in knowledge, we are able to infer people's expertise directly from the rankings they provide. We show that our model-based measure of expertise outperforms self-report measures, taken (...)
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  16.  39
    An Empirical Evaluation of Models of Text Document Similarity.Michael David Lee, B. M. Pincombe & Matthew Brian Welsh - unknown
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  17.  21
    Decision Making and Confidence Given Uncertain Advice.Michael D. Lee & Matthew J. Dry - 2006 - Cognitive Science 30 (6):1081-1095.
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  18.  6
    Correcting the SIMPLE Model of Free Recall.Michael D. Lee & James P. Pooley - 2013 - Psychological Review 120 (1):293-296.
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  19.  17
    Individual Differences in Attention During Category Learning.Michael D. Lee & Ruud Wetzels - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 387--392.
  20.  17
    Learning to Adapt Evidence Thresholds in Decision Making.Ben R. Newell & Michael D. Lee - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  21.  21
    Finding Feature Representations of Stimuli: Combining Feature Generation and Similarity Judgment Tasks.Matthew D. Zeigenfuse & Michael D. Lee - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1825--1830.
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  22.  14
    Extending and Testing the Bayesian Theory of Generalization.Daniel J. Navarro, Michael D. Lee, Matthew J. Dry & Benjamin Schultz - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  23.  7
    Regular Articles Learning to Divide the Labor: An Account of Deficits in Light and Heavy Verb Production 1 Jean K. Gordon, Gary S. Dell Semantic Grounding in Models of Analogy: An Environmental Approach 41.Michael Ramscar, Daniel Yarlett, Shimon Edelman, Nathan Intrator, Gergely Csibra, Szilvia Bıró, Orsolya Koós, György Gergely, Holk Cruse & Michael D. Lee - 2003 - Cognitive Science 27:945-948.
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  24.  10
    A Model-Based Approach to the Wisdom of the Crowd in Category Learning.Irina Danileiko & Michael D. Lee - 2018 - Cognitive Science 42 (S3):861-883.
    We apply the “wisdom of the crowd” idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model-based method that predicts (...)
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  25.  19
    Extending Bayesian Concept Learning to Deal with Representational Complexity and Adaptation.Michael D. Lee - 2001 - Behavioral and Brain Sciences 24 (4):685-686.
    While Tenenbaum and Griffiths impressively consolidate and extend Shepard's research in the areas of stimulus representation and generalization, there is a need for complexity measures to be developed to control the flexibility of their “hypothesis space” approach to representation. It may also be possible to extend their concept learning model to consider the fundamental issue of representational adaptation. [Tenenbaum & Griffiths].
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  26.  32
    In Praise of Ecumenical Bayes.Michael D. Lee - 2011 - Behavioral and Brain Sciences 34 (4):206-207.
    Jones & Love (J&L) should have given more attention to Agnostic uses of Bayesian methods for the statistical analysis of models and data. Reliance on the frequentist analysis of Bayesian models has retarded their development and prevented their full evaluation. The Ecumenical integration of Bayesian statistics to analyze Bayesian models offers a better way to test their inferential and predictive capabilities.
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  27.  7
    Postscript: Bayesian Statistical Inference in Psychology: Comment on Trafimow.Michael D. Lee & Eric-Jan Wagenmakers - 2005 - Psychological Review 112 (3):668-668.
  28.  24
    The Accuracy of Small-Group Estimation and the Wisdom of Crowds.Michael D. Lee & Jenny Shi - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1124--1129.
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  29.  46
    A Hierarchical Bayesian Modeling Approach to Searching and Stopping in Multi-Attribute Judgment.Don van Ravenzwaaij, Chris P. Moore, Michael D. Lee & Ben R. Newell - 2014 - Cognitive Science 38 (7):1384-1405.
    In most decision-making situations, there is a plethora of information potentially available to people. Deciding what information to gather and what to ignore is no small feat. How do decision makers determine in what sequence to collect information and when to stop? In two experiments, we administered a version of the German cities task developed by Gigerenzer and Goldstein (1996), in which participants had to decide which of two cities had the larger population. Decision makers were not provided with the (...)
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  30.  3
    Neural Network and Tree Search Algorithms for the Generation of Path-Following (Trail-Making) Tests.Michael D. Lee, Mark Brown & Douglas Vickers - 1997 - Journal of Intelligent Systems 7 (1-2):117-144.
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  31.  18
    Towards a Dynamic Connectionist Model of Memory.Douglas Vickers & Michael D. Lee - 1997 - Behavioral and Brain Sciences 20 (1):40-41.
    Glenberg's account falls short in several respects. Besides requiring clearer explication of basic concepts, his account fails to recognize the autonomous nature of perception. His account of what is remembered, and its description, is too static. His strictures against connectionist modeling might be overcome by combining the notions of psychological space and principled learning in an embodied and situated network.
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  32.  19
    Repeated Judgments in Elicitation Tasks: Efficacy of the MOLE Method.Matthew B. Welsh, Michael D. Lee & Steve H. Begg - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  33.  23
    Wisdom of Crowds in Minimum Spanning Tree Problems.Sheng Kung Michael Yi, Mark Steyvers, Michael D. Lee & Matthew Dry - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
  34.  14
    Heuristics for Choosing Features to Represent Stimuli.Matthew D. Zeigenfuse & Michael D. Lee - 2010 - In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1565--1570.
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