GSA Annual Meeting in Seattle, Washington, USA - 2017

Paper No. 259-8
Presentation Time: 9:00 AM-6:30 PM

TESTS OF AN EXEMPLAR-MEMORY MODEL OF HUMAN CLASSIFICATION LEARNING IN THE DOMAIN OF ROCK CATEGORIZATION


NOSOFSKY, Robert, Psychological and Brain Sciences, Indiana University, 1101 E. Tenth Street, Bloomington, IN 47405, MCDANIEL, Mark, Psychological and Brain Sciences, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130 and DOUGLAS, Bruce J., Department of Earth and Atmospheric Sciences, Indiana Univ, 1001 E. 10th St, Bloomington, IN 47405, nosofsky@indiana.edu

Experiments were conducted in which novice participants learned to classify pictures of rocks into their scientifically-defined categories. The experiments manipulated the distribution of training instances during an initial study phase, and then tested for correct classification and generalization performance during a transfer phase. The similarity structure of the to-be-learned categories was also manipulated across the experiments. A low-parameter version of an exemplar-memory model, used in combination with a high-dimensional feature-space representation for the rock stimuli, provided good overall accounts of the categorization data. The successful accounts included: 1) predicting how performance on individual item types within the categories varied with the distributions of training examples; 2) predicting the overall levels of classification accuracy across the different rock categories; and 3) predicting the patterns of between-category confusions that arose when classification errors were made. The work represents a promising initial step in scaling up the application of formal models of human perceptual classification learning to complex natural-category domains. We also provide evidence that training with images of the rocks can provide a significant head-start on learners’ ability to generalize to novel physical rock samples from the trained categories. Finally, we discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching rock categories in the geology classroom.