A Multidimensional-Scaling Study of Images from Diverse Everyday-Object Categories

We propose and implement an approach to deriving multidimensional scaling (MDS) solutions for objects from diverse everyday-object categories. The goal is for the MDS solutions to capture relative similarities between pairs of objects both within the categories and across the categories. For example, if the members of the category apples are more similar to one another than are the members of the category lamps, then the MDS solution for the apples will be more compressed overall than will the MDS solution for lamps. To achieve this goal, the key idea is that rather than collecting similarity-judgment data one category at a time, we alternate in random fashion across trials the category from which the similarity-judgment data are collected. We hypothesize that if similarity-judgment data are collected one category at a time, observers may recalibrate their judgment scale with respect to each individual category, which could cause loss of information of overall discriminability relations across the different categories. By using the alternating-category approach, observers may be able to maintain a more nearly constant judgment scale across the different categories. We combine the alternating-category procedure with use of metric forms of MDS that produce MDS solutions in which differences in overall discriminability relations across categories are maintained. We provide preliminary evidence of the success of the approach by showing that, when used as input to a simple computational model of recognition memory, the derived MDS solutions predict reasonably well false-alarm rates associated with the different categories observed in an old-new recognition experiment.