Model-guided search for optimal natural-science-category training exemplars: A work in progress.

Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category. Nevertheless, systematic departures from the model predictions are also observed. Further analyses lead us to the hypothesis that the high-dimensional feature-space representation derived for the rock stimuli (to which the exemplar model makes reference) systematically underestimates within-category similarities. We suggest that this limitation is likely to arise in numerous situations in which investigators attempt to build detailed feature-space representations for naturalistic categories. A low-parameter extended version of the model that adjusts for this limitation provides dramatically improved accounts of performance across the four conditions. We outline future steps for enhancing the current feature-space representation and continuing our goal of using formal psychological models to guide the search for effective methods of teaching science categories.

Toward the development of a feature-space representation for a complex natural category domain.

This article reports data sets aimed at the development of a detailed feature-space representation for a complex natural category domain, namely 30 common subtypes of the categories of igneous, metamorphic, and sedimentary rocks. We conducted web searches to develop a library of 12 tokens each of the 30 subtypes, for a total of 360 rock pictures. In one study, subjects provided ratings along a set of 18 hypothesized primary dimensions involving visual characteristics of the rocks. In other studies, subjects provided similarity judgments among pairs of the rock tokens. Analyses are reported to validate the regularity and information value of the dimension ratings. In addition, analyses are reported that derive psychological scaling solutions from the similarity-ratings data and that interrelate the derived dimensions of the scaling solutions with the directly rated dimensions of the rocks. The stimulus set and various forms of ratings data, as well as the psychological scaling solutions, are made available on an online website (https://osf.io/w64fv/) associated with the article. The study provides a fundamental data set that should be of value for a wide variety of research purposes, including: (1) probing the statistical and psychological structure of a complex natural category domain, (2) testing models of similarity judgment, and (3) developing a feature-space representation that can be used in combination with formal models of category learning to predict classification performance in this complex natural category domain.

Tests of an exemplar-memory model of classification learning in a high-dimensional natural-science category domain.

Experiments were conducted in which novice participants learned to classify pictures of rocks into real-world, 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 (a) predicting how performance on individual item types within the categories varied with the distributions of training examples, (b) predicting the overall levels of classification accuracy across the different rock categories, and (c) 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 perceptual classification learning to complex natural-category domains. We discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching categories in the science classroom. (PsycINFO Database Record

On Learning Natural-Science Categories That Violate the Family-Resemblance Principle.

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.