A tutorial on adaptive design optimization

被引:78
作者
Myung, Jay I. [1 ]
Cavagnaro, Daniel R. [2 ]
Pitt, Mark A. [1 ]
机构
[1] Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA
[2] Calif State Univ Fullerton, Mihaylo Coll Business & Econ, Fullerton, CA 92831 USA
关键词
Cognitive modeling; Optimal experimental design; Bayesian adaptive estimation; Sequential Monte Carlo; Mutual information; MODEL; SELECTION; DISCRIMINATION;
D O I
10.1016/j.jmp.2013.05.005
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Experimentation is ubiquitous in the field of psychology and fundamental to the advancement of its science, and one of the biggest challenges for researchers is designing experiments that can conclusively discriminate the theoretical hypotheses or models under investigation. The recognition of this challenge has led to the development of sophisticated statistical methods that aid in the design of experiments and that are within the reach of everyday experimental scientists. This tutorial paper introduces the reader to an implementable experimentation methodology, dubbed Adaptive Design Optimization, that can help scientists to conduct "smart" experiments that are maximally informative and highly efficient, which in turn should accelerate scientific discovery in psychology and beyond. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:53 / 67
页数:15
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