Toward the development of a feature-space representation for a complex natural category domain

• Published in: Behavior research methods 2018-04, Vol.50 (2), p.530-556
• Main Authors: Nosofsky, Robert M., Sanders, Craig A., Meagher, Brian J., Douglas, Bruce J.
• Format: Article
• Language: English
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Color Perception - physiology; Female; Geology - classification; Humans; Judgment; Male; Pattern Recognition, Visual - physiology; Photic Stimulation; Psychology; Size Perception; Young Adult
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-017-0884-8

This article reports data sets aimed at the development of a detailed feature-space representation for a complex natural category domain, namely 30 common subtypes of the categories of igneous, metamorphic, and sedimentary rocks. We conducted web searches to develop a library of 12 tokens each of the 30 subtypes, for a total of 360 rock pictures. In one study, subjects provided ratings along a set of 18 hypothesized primary dimensions involving visual characteristics of the rocks. In other studies, subjects provided similarity judgments among pairs of the rock tokens. Analyses are reported to validate the regularity and information value of the dimension ratings. In addition, analyses are reported that derive psychological scaling solutions from the similarity-ratings data and that interrelate the derived dimensions of the scaling solutions with the directly rated dimensions of the rocks. The stimulus set and various forms of ratings data, as well as the psychological scaling solutions, are made available on an online website (https://osf.io/w64fv/) associated with the article. The study provides a fundamental data set that should be of value for a wide variety of research purposes, including: (1) probing the statistical and psychological structure of a complex natural category domain, (2) testing models of similarity judgment, and (3) developing a feature-space representation that can be used in combination with formal models of category learning to predict classification performance in this complex natural category domain.