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  1. Guy Hawkins, Scott D. Brown, Mark Steyvers & Eric-Jan Wagenmakers (2012). Context Effects in Multi-Alternative Decision Making: Empirical Data and a Bayesian Model. Cognitive Science 36 (3):498-516.
    For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates—sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy (...)
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  2. Michael D. Lee, Mark Steyvers, Mindy de Young & Brent Miller (2012). Inferring Expertise in Knowledge and Prediction Ranking Tasks. 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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  3. Sheng Kung Michael Yi, Mark Steyvers, Michael D. Lee & Matthew J. Dry (2012). The Wisdom of the Crowd in Combinatorial Problems. 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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  4. Mark Steyvers, Padhraic Smyth & Chaitanya Chemuduganta (2011). Combining Background Knowledge and Learned Topics. Topics in Cognitive Science 3 (1):18-47.
    Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human-defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this study, we review a (...)
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  5. Sheng Kung Michael Yi, Mark Steyvers, Michael D. Lee & Matthew Dry (2010). Wisdom of Crowds in Minimum Spanning Tree Problems. In S. Ohlsson & R. Catrambone (eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society. Cognitive Science Society.
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  6. Pernille Hemmer & Mark Steyvers (2009). A Bayesian Account of Reconstructive Memory. Topics in Cognitive Science 1 (1):189-202.
  7. Mark Steyvers & Thomas L. Griffiths (2008). Rational Analysis as a Link Between Human Memory and Information Retrieval. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford. 329--349.
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  8. Mark Steyvers, Thomas L. Griffiths & Simon Dennis (2006). Probabilistic Inference in Human Semantic Memory. Trends in Cognitive Sciences 10 (7):327-334.
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  9. Mark Steyvers, Thomas L. Griffiths & Simon Dennis (2006). Retrieving Effectively From Memory (REM). Trends in Cognitive Sciences 10 (7):327-334.
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  10. Robert L. Goldstone, Steven A. Sloman, David A. Lagnado, Mark Steyvers, Joshua B. Tenenbaum, Saskia Jaarsveld, Cees van Leeuwen, Murray Shanahan, Terry Dartnall & Simon Dennis (2005). Subject Index to Volume 29. Cognitive Science 29:1093-1096.
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  11. Mark Steyvers & Joshua B. Tenenbaum (2005). The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth. Cognitive Science 29 (1):41-78.
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  12. Mark Steyvers, Joshua B. Tenenbaum, Eric‐Jan Wagenmakers & Ben Blum (2003). Inferring Causal Networks From Observations and Interventions. Cognitive Science 27 (3):453-489.
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  13. Mark Steyvers (2002). Multidimensional Scaling. In Lynn Nadel (ed.), The Encyclopedia of Cognitive Science. Macmillan.
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  14. Robert L. Goldstone & Mark Steyvers (2001). The Sensitization and Differentiation of Dimensions During Category Learning. Journal of Experimental Psychology: General 130 (1):116.
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  15. Robert Goldstone, Mark Steyvers, Jesse Spencer-Smith & Alan Kersten (2000). Interactions Between Perceptual and Conceptual Learning. In Eric Dietrich Art Markman (ed.), Cognitive Dynamics: Conceptual Change in Humans and Machines. Lawrence Erlbaum.
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  16. Robert L. Goldstone, Mark Steyvers & Kenneth Larimer (1996). Categorical Perception of Novel Dimensions. In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. 243--248.
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