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Monte Carlo Analysis of Incomplete Paired-Comparison Experiments

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Abstract

The use of paired-comparison psychophysical experiments is an important technique that is used widely in imaging studies. It is sometimes difficult to compare every stimulus with every other; the number of paired comparisons for n stimuli becomes prohibitive for large values of n. Thus, experiments are often designed by missing some pairs. However, the effect on the accuracy of the estimations of the scale values is not clear. Similarly, if more resources are available, would it be better to recruit more observers making the same paired comparisons or to have the original observers carry out additional paired comparisons? This work seeks to develop a framework for addressing these practical questions surrounding incomplete paired-comparison experiment design. A Monte Carlo computational simulation is carried out with an ideal-observer model. Results suggest that the proportion of paired comparisons is more critical than the number of observers with small numbers of stimuli.
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Document Type: Research Article

Publication date: 2014-09-01

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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