This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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291
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #308079 |
Title:
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A Hierarchical Bayesian Model of Learning in the Acquisition of a Skill
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Author(s):
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Jun Lu*+ and Dongchu Sun and Paul Speckman and Jeff Rouder
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Companies:
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American University and University of Missouri and University of Missouri-Columbia and University of Missouri
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Address:
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Dept. of Math/Stat, American University, Washington, DC, 20016,
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Keywords:
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Hierarchical Bayesian ;
time effects ;
Weibull distribution ;
learning ;
skill acquisition
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Abstract:
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How people learn new skills is a subject of much theorizing in experimental psychology. For many skills, proficiency may be measured as the time needed to complete a specific instance. Such "response time" serves as a suitable dependent variable and the change of its distribution over time and repetition serves as a suitable target for testing theoretical mechanisms of learning. We provide Bayesian analysis of a hierarchical three-parameter Weibull model for response time. Log-linear models with subject and time/practice effects are placed on the Weibull rate parameters to evaluate learning. Several priors on the time/practice effects are introduced and compared to reflect different psychological theories. The Bayesian model is applied to analyze data collected from a psychology experiment. A simulation study is also conducted to further evaluate the model.
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