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

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 th...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1554-3528
1554-3528
DOI:10.3758/s13428-017-0884-8