Dual Processes in the Acquisition of Categorical Concepts

Two experiments and computational simulations investig ated the way people make classifications and inferences when the information about category membership was available to participants. On a classification question, participants were asked to predict the category to which a stimulus belongs, and on an inference question, participants were asked to predict the feature value of a stimulus given the category membership of the stimulus. Given classification questions, participants ‘ performance was influenced greatly by the concrete appearance of individual stimuli, but such an influenced was not present in participants answering inference questions. Classification and inference constitute two of the most important aspects of concept acquisition (Smith, 1994). In this article, I will examine whether or not a similarity-based process, such as the one formalized in the Generalized Context Model (Nosofsky & Zaki, 2002), can account for judgment processes involved in classification and in inference. Despite the formidable successes of the similarity-based account of concept formation (Medin & Schaffer, 1978), recent findings suggest that forming categorical knowledge involves mu ltiple routes, which include a similarity-based associative process as well as a rule-based abstract process (Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998). The validity of this hybrid view has been, however, questioned recently for at least two reasons. First, most of the findings that support the hybrid account are also consistent with exemplar-based models (Nosofsky & Johansen, 2000). Second, studies have shown that depending on the way people interact with a category, they extract different types of feature information (Whittlesea, Brooks, & Westcott, 1994). For this reason, it is difficult to probe the mechanism underlying category formation by simply analyzing the effect of category learning. In this article, I will investigate how people arrive at classification judgments and inference judgments when the membership about categories is readily available to participants at the time of judgment (see Yamauchi & Markman, 2000 for a similar procedure). In so doing, I intend to demonstrate that classification and inference – two of the most important functions of categories – involves two separable processes. Overview of the Experiments In one experiment, participants were given a sample sheet depicting 10 members of two categories, and were asked to answer 60 classification questions or 60 inference questions on the basis of the 10 samples (Figure 1). Each category consisted of schematic figures of imaginary bugs that possess 5 features with binary values and a category label (e.g., "monek" or "plaple"). The two categories have a family-resemblance structure, which was derived from prototypes (M0 & P0) (Table 1). On the classification questions, participants predicted the category label of a stimulus given 5 dimensions of feature values (1 1 1 1 0 ? ). On the inference questions, participants predicted the value of one of the 5 features, given 4 dimensions of feature values and its category label (? 1 1 1 0 1). Thus, the two types of questions were formally equivalent if category labels and category features are the same thing (Table 1). Two key variables – feature-matching and featuremanifestation – measured the extent to which participants adopt a similarity-based associative process. I manipulated the number of matching features of the test stimulus to the prototype of the corresponding sample category, and devised two levels of matching features (i.e., highand medium-levels and see Table 1). In the second key variable – feature-manifestation, I controlled the appearance of individual stimuli. One set of test stimuli was composed of the same instances as used for the sample stimuli (i.e., Same-manifestation and Set A in Figure 2). The other sets of test stimuli consisted of new instances that were different from the sample stimuli (Different-manifestation; and Sets B/C/D/E in Figure 2). These new instances, however, had some abstract characteristics in common with the features of the sample stimuli (e.g., having eight legs). Thus, answering these questions required awareness of commonalties that go beyond specific appearance of individual items. The dependent variable of the experiment was the number of responses made in accordance with the prototype of the corresponding category (i.e., category-accordance responses, and see Table 2 for the definition). For example, given the classification question of stimulus M1, responding with the value 1 (i.e., selecting “Monek”) was considered a category-accordance response, and given the inference question of stimulus M1, responding with the value 1 (i.e., selecting the long horns) was considered a categoryaccordance response.