The sonic mapper: An interactive program for obtaining similarity ratings with auditory stimuli

Presented at the 8th International Conference on Auditory Display (ICAD), Kyoto, Japan, July 2-5, 2002.The Sonic Mapper is an interactive Linux-based graphical program that affords increased methodological flexibility and sophistication to researchers who collect proximity data for auditory research. The Sonic Mapper consists of a mapping environment in which participants can position and group icons in the two-dimensional plane of the screen. Options for collecting data concerning hierarchical groupings, category prototypicality, and verbal labeling provide additional opportunities to test hypotheses in a convergent manner. The Sonic Mapper also offers an environment for traditional pairwise comparisons, as well as one for performing free sorting tasks. A pilot study that attempts to use many of the Sonic Mapper's key features is described briefly below

Comparison of methods for collecting and modeling dissimilarity data: applications to complex sound stimuli

Sorting procedures are frequently adopted as an alternative to dissimilarity ratings to measure the dissimilarity of large sets of stimuli in a comparatively short time. However, systematic empirical research on the consequences of this experiment-design choice is lacking. We carried out a behavioral experiment to assess the extent to which sorting procedures compare to dissimilarity ratings in terms of efficiency, reliability, and accuracy, and the extent to which data from different data-collection methods are redundant and are better fit by different distance models. Participants estimated the dissimilarity of either semantically charged environmental sounds or semantically neutral synthetic sounds. We considered free and hierarchical sorting and derived indications concerning the properties of constrained and truncated hierarchical sorting methods from hierarchical sorting data. Results show that the higher efficiency of sorting methods comes at a considerable cost in terms of data reliability and accuracy. This loss appears to be minimized with truncated hierarchical sorting methods that start from a relatively low number of groups of stimuli. Finally, variations in data-collection method differentially affect the fit of various distance models at the group-average and individual levels. On the basis of these results, we suggest adopting sorting as an alternative to dissimilarity-rating methods only when strictly necessary. We also suggest analyzing the raw behavioral dissimilarities, and avoiding modeling them with one single distance model