11 found
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  1.  38
    Toward a method of selecting among computational models of cognition.Mark A. Pitt, In Jae Myung & Shaobo Zhang - 2002 - Psychological Review 109 (3):472-491.
  2.  29
    Global model analysis by parameter space partitioning.Mark A. Pitt, Woojae Kim, Daniel J. Navarro & Jay I. Myung - 2006 - Psychological Review 113 (1):57-83.
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  3.  27
    Distal rhythm influences whether or not listeners hear a word in continuous speech: Support for a perceptual grouping hypothesis.Tuuli H. Morrill, Laura C. Dilley, J. Devin McAuley & Mark A. Pitt - 2014 - Cognition 131 (1):69-74.
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  4.  24
    Optimal experimental design for model discrimination.Jay I. Myung & Mark A. Pitt - 2009 - Psychological Review 116 (3):499-518.
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  5.  10
    Distractor probability influences suppression in auditory selective attention.Heather R. Daly & Mark A. Pitt - 2021 - Cognition 216 (C):104849.
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  6.  24
    Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example.Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro & James Pooley - 2008 - Cognitive Science 32 (8):1285-1303.
    A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data‐fitting potential (i.e., flexibility) of the model. In the first part of this article, methods for comparing models and studying their flexibility are reviewed, with a focus on parameter space partitioning (PSP), a general‐purpose method for analyzing and comparing all classes of cognitive models. PSP (...)
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  7. Mathematical modeling.In Jae Myung & Mark A. Pitt - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
     
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  8. What Should Be the Data Sharing Policy of Cognitive Science?Mark A. Pitt & Yun Tang - 2013 - Topics in Cognitive Science 5 (1):214-221.
    There is a growing chorus of voices in the scientific community calling for greater openness in the sharing of raw data that lead to a publication. In this commentary, we discuss the merits of sharing, common concerns that are raised, and practical issues that arise in developing a sharing policy. We suggest that the cognitive science community discuss the topic and establish a data-sharing policy.
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  9.  20
    How do PDP models learn quasiregularity?Woojae Kim, Mark A. Pitt & Jay I. Myung - 2013 - Psychological Review 120 (4):903-916.
  10.  33
    Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.Woojae Kim, Mark A. Pitt, Zhong-Lin Lu & Jay I. Myung - 2017 - Cognitive Science:2234-2252.
    Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one-step-ahead strategies in which the stimulus on each trial is (...)
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  11. Model fitting.In J. Myung & Mark A. Pitt - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
     
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