Teaching natural-science categories is highly challenging because the objects in such categories are composed of numerous complex dimensions that need to be perceived, evaluated, and integrated. Furthermore, the boundaries separating such categories are often fuzzy. A technique that has been proposed and investigated for enhancing the teaching of natural-science categories is feature highlighting, in which diagnostic features for identifying category members are explicitly described and illustrated. Using rock classification in geology as an example target domain, the present study further investigated the potential benefits of feature highlighting and also of providing causal explanations for the highlighted features. The authors found that feature highlighting did not always lead to improved generalization to novel members of the taught categories. However, robust beneficial effects were seen when the categories were relatively confusable ones and the stated diagnostic features were highly valid for distinguishing among the categories. Finally, at least under the present conditions, supplementing the highlighted features with causal explanations of the reasons for their occurrence did not further enhance the participants' rock-classification learning and generalization. Although the teaching of causal explanations is fundamental to science education, clear evidence that causal explanations enhance classification-learning per se in this domain remains to be demonstrated. Public Significance Statement The present study suggests that visual highlighting of diagnostic features can enhance novice participants' ability to learn fundamental rock classifications, which is a core goal in introductory undergraduate geoscience education. Although providing causal explanations for the presence of the features is also central to geoscience education, it does not further enhance the novices' ability to learn the classifications.

In a novel version of the classic dot-pattern prototype-distortion paradigm of category learning, Homa et al. (2019) tested a condition in which individual training instances never repeated, and observed results that they claimed severely challenged exemplar models of classification and recognition. Among the results was a dissociation in which participants classified transfer items with high accuracy in the norepeat condition, yet in old-new recognition tests showed no ability to discriminate between old and new items of the same level of distortion from the prototype. In addition, speed of classification learning was no faster in a condition in which a small set of training instances was repeated continuously compared with the no-repeat condition. Here we show through computer-simulation modeling that exemplar models naturally capture the classification-recognition dissociation in the no-repeat condition, as well as a wide variety of other qualitative effects reported by Homa et al. (2019). We also conduct new conceptual-replication experiments to investigate their reported null effect of repeated versus nonrepeated training instances on speed of classification learning. In contrast to Homa et al. (2019) we find that speed of learning is substantially faster in the repeat condition than in the no-repeat condition, precisely as exemplar models predict. The exemplar model also captures a wide variety of transfer effects observed following the completion of category learning, including the classification-recognition dissociation observed across the repeat and no-repeat conditions.

An important goal in cognitive and mathematical psychology is to scale up the application of computational models of human classification learning to real-world, naturalistic domains. Application of the models, however, requires the derivation of the complex, multidimensional “feature spaces” in which the to-be-classified objects are embedded and to which the formal models make reference. In recent work, using rock classification in the geologic sciences as an example target domain, we used multidimensional scaling (MDS) of similarity-judgment data as an approach to deriving the feature space (Nosofsky et al., Behavior Research Methods 50:530–556, 2018c). However, subsequent work involving the modeling of independent sets of classification-learning data led us to the hypothesis that the MDS solution had many “missing dimensions” that were crucial to categorization performance (Nosofsky et al., Psychonomic Bulletin & Review 26:48–76, 2019). In the present work, we conduct a “search for the missing dimensions” in an effort to develop a more comprehensive feature-space representation for the rock stimuli. By supplementing the original MDS solution with the missing dimensions, we achieve dramatically improved accounts of varied sets of classification-learning data in this domain. We outline future steps for continuing and expanding the work to meet the goal of achieving meaningful computational modeling of human classification in naturalistic object domains. © 2019, Society for Mathematical Psychology.

Category learning is a core component of course curricula in science education. For instance, geology courses teach categorization of rock types. Using the educationally authentic rock categories. the current project examined whether category learning at a broad-level (BL; igneous, sedimentary, and metamorphic rocks) could be enhanced by learning category information at a more specific-level (SL; e.g., diorite under igneous, breccia under sedimentary, etc.). Experiments 1 and 2 showed that SL training was inferior to BL training when participants were required to respond at the BL regardless of whether BL and SL category labels were presented simultaneously during classification training or SL categories were learned initially followed by training on the specific-broad level name associations. However, Experiments 3 and 4 showed that SL training was as good as BL training when the training was more extensive and participants were allowed to respond at the trained level. By considering confusion matrices (i.e., probabilities that instances in a given category was erroneously classified as belonging to other categories). we conjectured that between-SL category similarity, specifically the degree to which similar-looking SL categories belong to the same BL category, is an important factor in determining the efficacy of SL training.

Learning naturalistic categories, which tend to have fuzzy boundaries and vary on many dimensions, can often be harder than learning well defined categories. One method for facilitating the category learning of naturalistic stimuli may be to provide explicit feature descriptions that highlight the characteristic features of each category. Although this method is commonly used in textbooks and classrooms. theoretically it remains uncertain whether feature descriptions should advantage learning complex natural-science categories. In three experiments, participants were trained on 12 categories of rocks, either without or with a brief description highlighting key features of each category. After training, they were tested on their ability to categorize both old and new rocks from each of the categories. Providing feature descriptions as a caption under a rock image failed to improve category learning relative to providing only the rock image with its category label (Experiment 1). However, when these same feature descriptions were presented such that they were explicitly linked to the relevant parts of the rock image (feature highlighting), participants showed significantly higher performance on both immediate generalization to new rocks (Experiment 2) and generalization after a 2-day delay (Experiment 3). Theoretical and practical implications are discussed.

Background Most science categories are hierarchically organized, with various high-level divisions comprising numerous subtypes. If we suppose that one's goal is to teach students to classify at the high level, past research has provided mixed evidence about whether an effective strategy is to require simultaneous classification learning of the subtypes. This past research was limited, however, either because authentic science categories were not tested, or because the procedures did not allow participants to form strong associations between subtype-level and high-level category names. Here we investigate a two-stage response-training procedure in which participants provide both a high-level and subtype-level response on most trials, with feedback provided at both levels. The procedure is tested in experiments in which participants learn to classify large sets of rocks that are representative of those taught in geoscience classes. Results The two-stage procedure yielded high-level classification performance that was as good as the performance of comparison groups who were trained solely at the high level. In addition, the two-stage group achieved far greater knowledge of the hierarchical structure of the categories than did the comparison controls. Conclusion In settings in which students are tasked with learning high-level names for rock types that are commonly taught in geoscience classes, it is best for students to learn simultaneously at the high and subtype levels (using training techniques similar to the presently investigated one). Beyond providing insights into the nature of category learning and representation, these findings have practical significance for improving science education.

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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.

Because of their complex structures, many natural-science categories are difficult to learn. Yet achieving accuracy in classification is crucial to scientific inference and reasoning. Thus, an emerging theme in cognitive-psychology and cognitive-science research has been to investigate better ways to instruct about categories. This article briefly reviews major findings that will help inform policies for teaching categories in the science classroom. Many of the examples come from our specific project that examines teaching rock classifications in the geologic sciences. This project uses formal models of human category learning—developed in cognitive psychology—to search for optimal teaching procedures. The model-suggested category-teaching procedures often lead to better learning outcomes than do alternative procedures motivated by teachers’ and students’ intuitive judgments. In addition to reviewing these enhanced procedures for teaching natural-science categories, the article points to recent broader efforts for fostering collaborations between cognitive-science researchers and education researchers. © The Author(s) 2018.

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.