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  1. Jay B. Martin, Thomas L. Griffiths & Adam N. Sanborn (2012). Testing the Efficiency of Markov Chain Monte Carlo With People Using Facial Affect Categories. Cognitive Science 36 (1):150-162.
    Exploring how people represent natural categories is a key step toward developing a better understanding of how people learn, form memories, and make decisions. Much research on categorization has focused on artificial categories that are created in the laboratory, since studying natural categories defined on high-dimensional stimuli such as images is methodologically challenging. Recent work has produced methods for identifying these representations from observed behavior, such as reverse correlation (RC). We compare RC against an alternative method for inferring the structure (...)
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  2. Adam N. Sanborn, Vikash Mansinghka & Thomas L. Griffiths (2009). A Bayesian Framework for Modeling Intuitive Dynamics. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  3. Adam N. Sanborn & Ricardo Silva (2009). Belief Propagation and Locally Bayesian Learning. In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society. 31.
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  4. Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Daniel J. Navarro (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
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  5. Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini & Navarro & J. Daniel (2008). Categorization as Nonparametric Bayesian Density Estimation. In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oup Oxford.
     
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