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 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.

[1]  R. Shepard.  The analysis of proximities: Multidimensional scaling with an unknown distance function. I. , 1962 .

[2]  Bradley C. Love,et al.  Optimal Teaching for Limited-Capacity Human Learners , 2014, NIPS.

[3]  M. Lee,et al.  Avoiding the dangers of averaging across subjects when using multidimensional scaling , 2003 .

[4]  A. Tversky,et al.  Additive similarity trees , 1977 .

[5]  Wolf Vanpaemel,et al.  Exemplar by feature applicability matrices and other Dutch normative data for semantic concepts , 2008, Behavior research methods.

[6]  K. Holyoak,et al.  Induction of category distributions: a framework for classification learning. , 1984, Journal of experimental psychology. Learning, memory, and cognition.

[7]  J. D. Smith,et al.  Prototypes in the Mist: The Early Epochs of Category Learning , 1998 .

[8]  Reed Wicander,et al.  Essentials of geology , 1995 .

[9]  M. Posner,et al.  On the genesis of abstract ideas. , 1968, Journal of experimental psychology.

[10]  M. Lee.  Three case studies in the Bayesian analysis of cognitive models , 2008, Psychonomic bulletin & review.

[11]  Kensuke Okada,et al.  A Bayesian approach to modeling group and individual differences in multidimensional scaling , 2016 .

[12]  Gregory Ashby,et al.  On the Dangers of Averaging Across Subjects When Using Multidimensional Scaling or the Similarity-Choice Model , 1994 .

[13]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[14]  Martial Hebert,et al.  Measures of Similarity , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[15]  J. Swets,et al.  Enhanced interpretation of diagnostic images. , 1988, Investigative radiology.

[16]  Gregory Ashby,et al.  Decision rules in the perception and categorization of multidimensional stimuli. , 1988, Journal of experimental psychology. Learning, memory, and cognition.

[17]  J. D. Stein,et al.  Field Guide to Native Oak Species of Eastern North America , 2012 .

[18]  J. Townsend,et al.  Computational, Geometric, and Process Perspectives on Facial Cognition : Contexts and Challenges , 2005 .

[19]  E. Rosch,et al.  Family resemblances: Studies in the internal structure of categories , 1975, Cognitive Psychology.

[20]  M. Lee,et al.  Extending the ALCOVE model of category learning to featural stimulus domains , 2002, Psychonomic bulletin & review.

[21]  Michael C. Hout,et al.  The versatility of SpAM: a fast, efficient, spatial method of data collection for multidimensional scaling. , 2013, Journal of experimental psychology. General.

[22]  J. Corter,et al.  An Efficient Metric Combinatorial Algorithm for Fitting Additive Trees. , 1998, Multivariate behavioral research.

[23]  R. Nosofsky.  Attention, similarity, and the identification-categorization relationship. , 1986, Journal of experimental psychology. General.

[24]  J A Swets,et al.  Enhancing and Evaluating Diagnostic Accuracy , 1991, Medical decision making : an international journal of the Society for Medical Decision Making.

[25]  R. Nosofsky,et al.  On Learning Natural-Science Categories That Violate the Family-Resemblance Principle , 2017, Psychological science.

[26]  Stephen K. Reed,et al.  Pattern recognition and categorization , 1972 .

[27]  Joshua B. Tenenbaum,et al.  Learning the Structure of Similarity , 1995, NIPS.

[28]  Mark Steyvers,et al.  Predicting Similarity Ratings to Faces using Physical Descriptions , 2000 .

[29]  M. Lee.  Determining the Dimensionality of Multidimensional Scaling Representations for Cognitive Modeling. , 2001, Journal of mathematical psychology.

[30]  J. D. Smith,et al.  "Prototypes in the mist: The early epochs of category learning": Correction to Smith and Minda (1998). , 1999 .

[31]  D. Spencer,et al.  Field theory handbook: Including coordinate systems, differential equations, and their solutions , 1988 .

[32]  R. Shepard.  The analysis of proximities: Multidimensional scaling with an unknown distance function. II , 1962 .

[33]  J. R. V. BROOKS,et al.  Earth Science , 1973, Nature.

[34]  F. Gregory Ashby,et al.  Multidimensional Models of Perception and Cognition , 2014 .

[35]  R N Shepard,et al.  Multidimensional Scaling, Tree-Fitting, and Clustering , 1980, Science.

[36]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[37]  Steven Verheyen,et al.  Determining the dimensionality in spatial representations of semantic concepts , 2007, Behavior research methods.

[38]  Michael D Lee,et al.  Finding the features that represent stimuli. , 2010, Acta psychologica.

[39]  Emmanuel M. Pothos,et al.  Formal Approaches in Categorization: Contents , 2011 .

[40]  Phillip B. Gibbons,et al.  Sage , 2020, Proceedings of the VLDB Endowment.

[41]  Roger N. Shepard,et al.  Additive clustering: Representation of similarities as combinations of discrete overlapping properties. , 1979 .

[42]  R. Nosofsky.  Similarity Scaling and Cognitive Process Models , 1992 .

[43]  Tom Verguts,et al.  Measures of similarity in models of categorization , 2004, Memory & cognition.

[44]  A. H. Munsell,et al.  A Color Notation: An Illustrated System Defining All Colors and Their Relations by Measured Scales of Hue, Value and Chroma , 2013 .

[45]  R. Goldstone.  An efficient method for obtaining similarity data , 1994 .

[46]  Mark D. Fairchild,et al.  Charting color from the eye of the beholder , 2005 .

[47]  Wolf Vanpaemel,et al.  Caveats for the spatial arrangement method: Comment on Hout, Goldinger, and Ferguson (2013). , 2016, Journal of experimental psychology. General.

[48]  Robert M Nosofsky,et al.  A hybrid-similarity exemplar model for predicting distinctiveness effects in perceptual old-new recognition. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[49]  Harold Pashler,et al.  Optimizing Instructional Policies , 2013, NIPS.

[50]  Snigdhansu Chatterjee,et al.  Procrustes Problems , 2005, Technometrics.