The model captured most explainable variance in similarity judgements and produced 49 highly reproducible and meaningful object dimensions that reflect various conceptual and perceptual properties of those objects. To identify these core dimensions of object representations, we developed a data-driven computational model of similarity judgements for real-world images of 1,854 objects. Objects can be characterized according to a vast number of possible criteria (such as animacy, shape, colour and function), but some dimensions are more useful than others for making sense of the objects around us.
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Each trial yields the results of 28 pairwise comparisons of the form, “Was A more similar to the reference than B was to the reference?” While directly comparing all pairs of pairs of stimuli would have required 221445 trials, this design enables reconstruction of the perceptual space from 5994 such comparisons obtained from 222 trials.
The approach is illustrated with an experiment measuring similarities among 37 words. The approach was validated for stimuli drawn from Euclidean spaces of up to five dimensions. By judicious selection of combinations of stimuli used in each trial, the approach has internal controls for consistency and context effects. Typical trials consist of eight stimuli around a central reference stimulus: the subject ranks stimuli in order of their similarity to the reference. This is much more efficient (the number of ratings grows quadratically with set size rather than quartically), but these ratings tend to be unstable and have limited resolution, and the approach also assumes that there are no context effects.Here, a novel ranking paradigm for efficient collection of similarity judgments is presented, along with an analysis pipeline (software provided) that tests whether Euclidean distance models account for the data. An alternative strategy is to ask a subject to rate similarities of isolated pairs, e.g., on a Likert scale. For example, if one asks a subject to compare the similarity of two items with the similarity of two other items, the number of comparisons grows with the fourth power of the stimulus set size. Ideally, one might want to compare estimates of perceived similarity between all pairs of stimuli, but this is often impractical. This approach has been used to characterize perceptual spaces in many domains: colors, objects, images, words, and sounds. Similarity judgments are commonly used to study mental representations and their neural correlates.