Abstract
Cognitive theories in visual attention and perception, categorization, and memory often critically rely on concepts of similarity among objects, and empirically require measures of “sameness” among their stimuli. For instance, a researcher may require similarity estimates among multiple exemplars of a target category in visual search, or targets and lures in recognition memory. Quantifying similarity, however, is challenging when everyday items are the desired stimulus set, particularly when researchers require several different pictures from the same category. In this article, we document a new multidimensional scaling database with similarity ratings for 240 categories, each containing color photographs of 16–17 exemplar objects. We collected similarity ratings using the spatial arrangement method. Reports include: the multidimensional scaling solutions for each category, up to five dimensions, stress and fit measures, coordinate locations for each stimulus, and two new classifications. For each picture, we categorized the item's prototypicality, indexed by its proximity to other items in the space. We also classified pairs of images along a continuum of similarity, by assessing the overall arrangement of each MDS space. These similarity ratings will be useful to any researcher that wishes to control the similarity of experimental stimuli according to an objective quantification of “sameness.”
Highlights
Researchers across domains of cognitive science often require stimuli with varying degrees of similarity to one another
multidimensional scaling (MDS) algorithm For each data set, we used the INDSCAL scaling algorithm, which is a version of the ALSCAL algorithm that provides individual differences metrics, via SPSS 22.0 [14])
Dimensionality of the MDS space In order to determine the appropriate dimensionality in which the data should be scaled, analysts often rely on the stress of the solutions
Summary
Researchers across domains of cognitive science (and related fields) often require stimuli with varying degrees of similarity to one another. One approach is to employ simplistic stimuli, and vary a single feature of each item, such as the color or orientation of a rectangular bar [3]. This is a suboptimal approach with higherlevel vision, because real-world objects contain many features that may be ill-defined or inconsistent across exemplars of a category. Restricting stimulus complexity to simple, arbitrary objects (e.g., rotated, colored shapes) permits tight experimental control; visual similarity across items is assessed and manipulated. MDS does not require a priori identification of feature dimensions, or arbitrary rating schemes, such as ranking the similarity of colored bars based on degree of rotation, or distance in RGB color space
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