SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUS-TAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into

Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. The authors tested this hypothesis by conducting three category-learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effects were difficult to obtain. Conversely, when participants learned to predict an outcome in a task with the same formal structure and materials, blocking effects were robust and followed the predictions of error-driven learning. The authors discuss their findings in relation to models of category learning and the usefulness of category knowledge in the environment.

Research has shown that learning a concept via standard supervised classification leads to a focus on diagnostic features, whereas learning by inferring missing features promotes the acquisition of withincategory information. Accordingly, we predicted that classification learning would produce a deficit in people’s ability to draw novel contrasts—distinctions that were not part of training—compared with feature inference learning. Two experiments confirmed that classification learners were at a disadvantage at making novel distinctions. Eye movement data indicated that this conceptual inflexibility was due to (a) a narrower attention profile that reduces the encoding of many category features and (b) learned inattention that inhibits the reallocation of attention to newly relevant information. Implications of these costs of supervised classification learning for views of conceptual structure are discussed.

In addition to supervised classification learning, people can also learn categories by predicting the features of category members. One account of feature inference learning is that it induces a prototype representation of categories. Another is that it results in a set of category-to-feature rules. Because neither model provides an adequate account of existing data, we propose instead that inference learning induces an anticipatory learning strategy in which learners attend to aspects of training items they think will be needed in the future, and by so doing incidentally encode information about the category’s internal structure. The proposal is formalized by an exemplar fragment model (EFM) that represents partial exemplars, namely, those parts that are attended during training. EFM’s attention weights are approximated by eyetracking data, resulting in fewer free parameters as compared to competing theories. When people classify objects, problem solve, describe concepts, or infer missing information, they must access conceptual knowledge. Thus, the question of how people learn and represent concepts has been central to the overall mission of cognitive psychology. Researchers have developed sophisticated formal theories that explain many aspects of concept acquisition. These theories are largely based on supervised classification learning in which subjects classify items whose category membership is unknown and receive immediate feedback. Recently, to understand the interplay between how categorical knowledge is used and the concept acquired, researchers have begun to investigate a wider range of learning tasks

The effect of immediate versus delayed feedback on rule-based and information-integration category learning was investigated. Accuracy rates were examined to isolate global performance deficits, and model-based analyses were performed to identify the types of response strategies used by observers. Feedback delay had no effect on the accuracy of responding or on the distribution of best fitting models in the rule-based category-learning task. However, delayed feedback led to less accurate responding in the information-integration category-learning task. Model-based analyses indicated that the decline in accuracy with delayed feedback was due to an increase in the use of rule-based strategies to solve the information-integration task. These results provide support for a multiple-systems approach to category learning and argue against the validity of single-system approaches. Categorization is fundamental to the survival of all organisms (Ashby & Maddox, 1998). Every day people make thousands of categorization judgments and are often remarkably accurate. An understanding of the psychological processes involved in category learning, and whether different processes are involved in learning different types of category structures, is of critical importance. We learn about categories in a number of different ways (e.g., Ashby,

The present research explored the role of working memory (WM) in unsupervised category learning, learning without an external tutor or even knowing that categories exist, by investigating its role using a pattern-sequence manipulation. A pattern-sequence manipulation compares learning when items from categories are presented together (blocked) versus when the items are presented in random order (mixed). Experiment 1 extended the pattern-sequence manipulation to assess category knowledge separate from paired-associate learning. Participants performed equally well on new and studied items, supporting the hypothesis that the pattern-sequence manipulation results in the acquisition of category information, not simply memory for item-feature associations. Experiment 2 introduced a WM factor, administering the method used in Experiment 1 to a group of high and low WM span participants. High WM span was predicted to interact with the pattern-sequence effect to produce greater learning when the items were blocked than mixed. There was reliable support for a role of WM span in the discovery and acquisition of category knowledge, but this role was different from the one hypothesized. The high WM span participants exhibited higher overall accuracies than the low WM span participants. This result supports a role for WM in unsupervised category learning, but did not benefit more from the pattern-sequence effect than did the low WM span participants as predicted. Implications for theories of category learning and WM are discussed.

A behavioral study investigated how college students judge similarity between cell pictures. The study indicates that there is a strong tendency to rely on classinclusion relations in judgments of similarity. This means that biological concepts are likely to be organized and conceptualized with respect to classinclusion relations even for non-experts. 1

Previous research has shown that inference learning may motivate learners to acquire more within-category and prototypical information compared to standard supervised classification. We hypothesized that as a result inference learners would build flexible representations that could facilitate making novel category contrasts. An experiment tested the flexibility of category representations across inference and classification tasks by (1) having people make novel contrasts with categories learned earlier in the experiment and (2) recording eye movements as participants acquired and made various category contrasts across four categories. Significant differences in the attention patterns were observed in the eye movement data. Differences in attention were coupled with an advantage for inference

In addition to traditional supervised classification learning, people can also learn categories by predicting the features of category members. It has been proposed that feature inference learning promotes the learning of more within-category information and a prototype representation of the category, as compared to classification learning that promotes learning of diagnostic information. We tracked learners ' eye movements during inference learning and found (Expt. 1) that they indeed fixated other features (even though those features were not necessary to predict the missing feature), providing the opportunity to extract within-category information. But those fixations were limited to only those features that needed to be predicted on future trials (Expt. 2). In other words, inference learning promotes the acquisition of within-category information not because participants are motivated to learn that information, but rather because of the anticipatory learning it induces. Whenever a person classifies an object, describes a concept verbally, engages in problem solving, or infers missing information, they must access their conceptual knowledge. As a result, the study of concept acquisition has been a critical part of understanding how people experience the world and how they interact with it in appropriate ways. Concept researchers have developed sophisticated formal theories that explain certain aspects of concept acquisition. These theories are largely based on the study of what has come to be known as standard supervised classification—a task that occupies the majority of experimental research in this area (Solomon, Medin, & Lynch, 1999). However, an emerging literature is focused on expanding the range of tasks that can be used to inform our models of concept acquisition. By studying different learning tasks we can understand other aspects of concept acquisition, including the interplay between category use and the type of concept

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