Author ' s personal copy Feature inference learning and eyetracking

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category’s internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of diagnostic information. We tracked learners’ eye movements and found in Experiment 1 that inference learners indeed fixated features that were unnecessary for inferring the missing feature, behavior consistent with acquiring the categories’ internal structure. However, Experiments 3 and 4 showed that fixations were generally limited to features that needed to be predicted on future trials. We conclude that inference learning induces both supervised and unsupervised learning of category-to-feature associations rather than a general motivation to learn the internal structure of categories. 2008 Elsevier Inc. All rights reserved. When people classify objects, describe concepts verbally, engage in problem solving, or infer missing information, they must access their conceptual knowledge. As a result, the question of how people acquire concepts has been a critical part of understanding how people experience the world and how they interact with it in appropriate ways. 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 supervised classification learning—a task that dominates experimental research in this area (Solomon, Median, & Lynch, 1999). In a supervised classification learning experiment, subjects are presented with items whose category membership is unknown; they classify each item and then receive immediate feedback. However, an emerging literature has expanded the range of learning tasks that can be used to inform our models of concept acquisition. By studying different tasks we can understand other aspects of concept acquisition, including the interplay between how categorical knowledge is used and the concept acquired (Brooks, 1978; Chin-Parker & Ross, 2002; Ross, 2000; Yamauchi, Love, & Markman, 2002; Yamauchi & Markman, 1998, 2000a). For example, investigators have compared supervised classification learning with feature inference learning in which learners are presented with an item whose category membership is already identified and asked to infer one of its unknown features. That is, rather than predicting a missing category label on the basis of features, feature inference learners predict a missing feature on the basis of the category label (and perhaps other features). One reason that classification and feature inference learning have been compared is that the two tasks can be equated in a number of ways (Markman & Ross, 2003). Indeed, classification and certain forms of an inference task can be shown to be formally equivalent (Anderson, 1991). Research comparing classification and feature inference learning has revealed that the two tasks result in the acquisition of different sorts of information about concepts. Whereas it has been established that classification promotes learning the most diagnostic features for determining category membership (Medin, Wattenmaker, & Hampson, 1987; Rehder & Hoffman, 2005a, 2005b; Shepard, Hovland, & Jenkins, 1961; Tversky, 1977), there is evidence that feature inference fosters learning additional category information. For example, Chin-Parker and Ross 0749-596X/$ see front matter 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.jml. 2008.12. 001 * Corresponding author. Fax: +1 212 995 4349. E-mail address: bob.rehder@nyu.edu (B. Rehder). Journal of Memory and Language 60 (2009) 393–419