Figure 2.

Dissimilarity judgments by multi-arrangement (MA). (A) Dissimilarity judgments were acquired using a novel MA method, which allows efficient and subject-tailored acquisition of perceived similarity for large sets of objects. Subjects were asked to arrange the objects according to their similarity, using mouse drag-and-drop on a computer display. Perceived similarity was communicated by adjusting the distances between the objects: objects perceived as similar were placed close together; objects perceived as dissimilar were placed further apart. The upper panel of the figure shows screenshots taken at different moments during the acquisition of the dissimilarity judgments for one subject. Columns correspond to trials and rows show object arrangements over time, running from the start (first row) to the end of each trial (final arrangement, last row). The first trial contained all object images; subsequent trials contained subsets of images that were adaptively selected to optimally estimate perceived similarity for each subject. The black dots represent not-shown arrangements during a trial (small dots) and not-shown trials (large dots). (B) Once acquisition of the dissimilarity judgments was completed, inter-object distances of the final trial arrangements were combined over trials by rescaling and averaging to yield a single dissimilarity estimate for each object pair. Conceptually, this step can be seen as “inverse” multidimensional scaling, since it combines several lower-dimensional (2D) similarity representations into one higher-dimensional similarity representation. This process is shown for two example objects pairs: a boy’s face and a hand (red), and carrots and a stop sign (blue). Their single-trial dissimilarity estimates (arrows) are combined into a single dissimilarity estimate, which is placed at the corresponding entry of the RDM (lower panel). Mirror-symmetric entries are indicated by lighter colors.