title: On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.
abstract: The general view in psychological science is that natural categories obey a coherent, family-resemblance principle. In this investigation, we documented an example of an important exception to this principle: Results of a multidimensional-scaling study of igneous, metamorphic, and sedimentary rocks (Experiment 1) suggested that the structure of these categories is disorganized and dispersed. This finding motivated us to explore what might be the optimal procedures for teaching dispersed categories, a goal that is likely critical to science education in general. Subjects in Experiment 2 learned to classify pictures of rocks into compact or dispersed high-level categories. One group learned the categories through focused high-level training, whereas a second group was required to simultaneously learn classifications at a subtype level. Although high-level training led to enhanced performance when the categories were compact, subtype training was better when the categories were dispersed. We provide an interpretation of the results in terms of an exemplar-memory model of category learning.
authors.affiliation: 1 Department of Psychological and Brain Sciences, Indiana University Bloomington.
authors.forename: Robert M
authors.initials: RM
authors.lastname: Nosofsky
authors.affiliation: 1 Department of Psychological and Brain Sciences, Indiana University Bloomington.
authors.forename: Craig A
authors.initials: CA
authors.lastname: Sanders
authors.affiliation: 1 Department of Psychological and Brain Sciences, Indiana University Bloomington.
authors.forename: Alex
authors.initials: A
authors.lastname: Gerdom
authors.affiliation: 2 Department of Geological Sciences, Indiana University Bloomington.
authors.forename: Bruce J
authors.initials: BJ
authors.lastname: Douglas
authors.affiliation: 3 Department of Psychological and Brain Sciences, Washington University in St. Louis.
authors.forename: Mark A
authors.initials: MA
authors.lastname: McDaniel
mesh_terms: D003210:Concept Formation / Q000502:physiology*; D004193:Discrimination Learning / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D066230:Patient-Specific Modeling; D010364:Pattern Recognition, Visual / Q000502:physiology; D013334:Students / Q000523:psychology
keywords: category learning; computational modeling; open data; similarity