Intentional and Incidental Classification Learning in Category Use

Intentional and Incidental Classification Learning in Category Use Michael Romano (mrr233@nyu.edu) Department of Psychology, New York University, 6 Washington Place New York, NY 10003 USA Abstract Traditional classification learning studies tell us that people learn to attend to the diagnostic features of exemplars (Kruschke, 1992; Medin & Schaffer, 1978; Shepard, Hovland, & Jenkins, 1961). But recent research has discovered that the learning task influences what information people learn about categories (Markman & Ross, 2003). A learning task can either be the primary goal or be incidental to some other larger goal. This study investigated how intentional vs. incidental classification changes the kind of category information learned. The intentional classification group replicated previous studies by learning the diagnostic features, while the incidental classification group acquired information beyond the diagnostic features. Keywords: categorization; implicit learning; implicit memory Introduction For much of the last 30 years, classification learning has been considered the central strategy to forming concepts (Barsalou, 1990; Chin-Parker & Ross, 2004; Estes, 1994; Kruschke, 1992). But outside the laboratory, people do not always learn categories as a main goal; they learn categories for some kind of use (Brooks, 1999). Markman and Ross (2003) argue that traditional classification learning studies overemphasize explicit classifications, even though people often make implicit classifications outside the laboratory. For example, customers do not examine merchandise in a sports store and explicitly ask, “Is this a running shoe or a boxing glove? ” Rather, they implicitly classify items as part of the larger goal of shopping. In this light, classification learning can be split into (at least) two types: intentional classification and incidental classification. Intentional classification learning occurs when one is explicitly aware of the classification task, such that it becomes the primary goal. Incidental classification learning is performed in support of some other goal. Category information can be split into (at least) two types: diagnostic features and prototypical features. Diagnostic features are those that inform us how to select members that belong to a category while excluding those that do not. The diagnostic features of a category are defined by other categories. For example, having hair is useful in distinguishing between dolphins and sharks, but not between dolphins and whales. Thus, diagnostic features provide between-category information. In family resemblance category structures (Rosch & Mervis, 1975), prototypical features are those commonly found in the members of a category. The prototypical features of a category stay the same regardless of the contrasting category. For example, some prototypical features of a dog are that it has four legs, fur, and a tail that it wags when it’s happy. Thus, prototypical features provide within-category information. Because intentional classification learning encourages people to explicitly focus on between-category information, this task facilitates the learning of diagnostic features (Kruschke, 1992; Medin & Schaffer, 1978; Shepard, Hovland, & Jenkins, 1961), but not the non-diagnostic, yet prototypical, features (Chin-Parker & Ross, 2004). On the other hand, incidental classification does not as strongly stress the importance of distinguishing between categories, which could create the opportunity to learn more within- category information (the prototypical features). In the current research, it was hypothesized that the intentional classification group would learn a high amount of diagnostic information and a low (close to zero) amount of non-diagnostic information. It was also hypothesized that the incidental classification group would learn less diagnostic information than the intentional group, but more of the non-diagnostic (prototypical) information. In other words, each learning task would benefit from a gain in one type of information and a loss in the other. There has been some previous research investigating how intentional vs. incidental classification affects what is learned about categories. For example, incidental learners are more likely to claim that they discovered a single defining feature that perfectly predicted category membership (Brooks, Squire-Graydon, & Wood, 1998). Three groups learned family resemblance categories by explicitly analyzing items for rules, by memorizing items, or by learning items incidentally to playing a board game. The incidental group made an average of 2.5% errors in the 10 trials preceding the test phase, but any single-feature rule would have resulted in at least 20% errors. That is, incidental learners believed that the categories had simple defining features, despite the fact that their categorization behavior was more complex. Brooks et al. called this the “simpler than it is” phenomenon, in that a person’s belief about the nature of categories is not necessarily consistent with actual categorization behavior (Murphy, 2002). Current Research In this experiment, the performance of participants in the intentional classification task was compared with the performance of participants in the incidental classification task. Borrowing from the Brooks et al. (1998) study, participants in both conditions learned two categories of bugs while playing a board game. But in each trial, the intentional group classified exemplars before playing the