One Giant Leap for Categorizers: One Small Step for Categorization Theory

• Published in: PloS one Vol. 10; no. 9; p. e0137334
• Main Authors: Smith, J David, Ell, Shawn W
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.09.2015; Public Library of Science (PLoS)
• Subjects: Accuracy; Animals; Categorization (Psychology); Classification; Classifiers; Cognition & reasoning; Computer Simulation; Concept Formation; Discrimination Learning; Experimental psychology; Feedback; Health care costs; Humans; Learning; Machine learning; Memory; Modelling; Models, Psychological; Neurobiology; Neurosciences; Normal distribution; Psychology; Psychology - methods; Psychology of learning; Quantitative psychology; Simulation; Trends; United States; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0137334

We explore humans' rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans' rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans' psychology in these tasks. An important next step will be to build a new generation of models that can do so.