Search for the Missing Dimensions: Building a Feature-Space Representation for a Natural-Science Category Domain

Abstract

An important goal in cognitive and mathematical psychology is to scale up the application of computational models of human classification learning to real-world, naturalistic domains. Application of the models, however, requires the derivation of the complex, multidimensional “feature spaces” in which the to-be-classified objects are embedded and to which the formal models make reference. In recent work, using rock classification in the geologic sciences as an example target domain, we used multidimensional scaling (MDS) of similarity-judgment data as an approach to deriving the feature space (Nosofsky et al., Behavior Research Methods 50:530–556, 2018c). However, subsequent work involving the modeling of independent sets of classification-learning data led us to the hypothesis that the MDS solution had many “missing dimensions” that were crucial to categorization performance (Nosofsky et al., Psychonomic Bulletin & Review 26:48–76, 2019). In the present work, we conduct a “search for the missing dimensions” in an effort to develop a more comprehensive feature-space representation for the rock stimuli. By supplementing the original MDS solution with the missing dimensions, we achieve dramatically improved accounts of varied sets of classification-learning data in this domain. We outline future steps for continuing and expanding the work to meet the goal of achieving meaningful computational modeling of human classification in naturalistic object domains.