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

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.