35 found
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Michael D. Lee [33]Michael David Lee [2]
  1.  8
    One‐year‐old infants use teleological representations of actions productively.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 (1):111-133.
    Two experiments investigated whether infants represent goal‐directed actions of others in a way that allows them to draw inferences to unobserved states of affairs (such as unseen goal states or occluded obstacles). We measured looking times to assess violation of infants' expectations upon perceiving either a change in the actions of computer‐animated figures or in the context of such actions. The first experiment tested whether infants would attribute a goal to an action that they had not seen completed. The second (...)
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  2.  33
    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.
    This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a (...)
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  3.  20
    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. 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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  5.  21
    Number-knower levels in young children: Insights from Bayesian modeling.Michael D. Lee & Barbara W. Sarnecka - 2011 - Cognition 120 (3):391-402.
  6.  8
    Bayesian statistical inference in psychology: Comment on Trafimow (2003).Michael D. Lee & Eric-Jan Wagenmakers - 2005 - Psychological Review 112 (3):662-668.
  7. 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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  8.  17
    A Hierarchical Bayesian Model of Human Decision‐Making on an Optimal Stopping Problem.Michael D. Lee - 2006 - Cognitive Science 30 (3):1-26.
    We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We (...)
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  9.  22
    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.
    This article demonstrates the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. This is done using a worked example that considers an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but this article shows how a hierarchical Bayesian analysis can provide a unifying explanation (...)
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  10.  6
    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 5:112331.
    Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empirical data, and apply our new method to two problems. The (...)
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  11.  19
    Sequential sampling models of human text classification.Michael D. Lee & Elissa Y. Corlett - 2003 - Cognitive Science 27 (2):159-193.
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  12. Learned categorical perception for natural faces.Daniel Joseph Navarro, Michael David Lee & H. C. Nikkerud - manuscript
     
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  13.  30
    Quantum models of cognition as Orwellian newspeak.Michael D. Lee & Wolf Vanpaemel - 2013 - Behavioral and Brain Sciences 36 (3):295-296.
  14.  17
    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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  15. 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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  16.  16
    Evaluating the complexity and falsifiability of psychological models.Manuel Villarreal, Alexander Etz & Michael D. Lee - 2023 - Psychological Review 130 (4):853-872.
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  17.  24
    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.  45
    An empirical evaluation of models of text document similarity.Michael David Lee, B. M. Pincombe & Matthew Brian Welsh - unknown
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  19. 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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  20.  8
    Correcting the SIMPLE model of free recall.Michael D. Lee & James P. Pooley - 2013 - Psychological Review 120 (1):293-296.
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  21.  24
    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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  22.  19
    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.
  23.  35
    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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  24.  12
    Postscript: Bayesian Statistical Inference in Psychology: Comment on Trafimow (2003).Michael D. Lee & Eric-Jan Wagenmakers - 2005 - Psychological Review 112 (3):668-668.
  25. 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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  26. 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.
     
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  27. 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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  28.  5
    Model‐Based Wisdom of the Crowd for Sequential Decision‐Making Tasks.Bobby Thomas, Jeff Coon, Holly A. Westfall & Michael D. Lee - 2021 - Cognitive Science 45 (7):e13011.
    We study the wisdom of the crowd in three sequential decision‐making tasks: the Balloon Analogue Risk Task (BART), optimal stopping problems, and bandit problems. We consider a behavior‐based approach, using majority decisions to determine crowd behavior and show that this approach performs poorly in the BART and bandit tasks. The key problem is that the crowd becomes progressively more extreme as the decision sequence progresses, because the diversity of opinion that underlies the wisdom of the crowd is lost. We also (...)
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  29.  50
    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.  5
    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.  22
    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. 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. 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.  23
    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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  35.  19
    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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