Journal
NEURAL COMPUTATION
Volume 26, Issue 11, Pages 2465-2492Publisher
MIT PRESS
DOI: 10.1162/NECO_a_00654
Keywords
-
Funding
- National Institutes of Health [R01-MH093838]
- National Eye Institute [R01-EY021553-01]
Ask authors/readers for more resources
Experimentation is at the core of research in the behavioral and neural sciences, yet observations can be expensive and time-consuming to acquire (e.g., MRI scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of observations. In addressing this challenge, statisticians have developed adaptive design optimization methods. This letter introduces a hierarchical Bayes extension of adaptive design optimization that provides a judicious way to exploit two complementary schemes of inference (with past and future data) to achieve even greater accuracy and efficiency in information gain. We demonstrate the method in a simulation experiment in the field of visual perception.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available