This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 291
Type: Topic Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308079
Title: A Hierarchical Bayesian Model of Learning in the Acquisition of a Skill
Author(s): Jun Lu*+ and Dongchu Sun and Paul Speckman and Jeff Rouder
Companies: American University and University of Missouri and University of Missouri-Columbia and University of Missouri
Address: Dept. of Math/Stat, American University, Washington, DC, 20016,
Keywords: Hierarchical Bayesian ; time effects ; Weibull distribution ; learning ; skill acquisition
Abstract:

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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