Dimension-Wide vs. Exemplar-Specific Attention in Category Learning and Recognition

Items that violate a category rule are remembered better than items that follow the rule. This finding cannot be predicted by exemplar models when all exemplars share the same attention along a dimension. With dimension-wide attention, violating and rule-following items are treated equally. When each exemplar selects which dimensions to attend to, exemplar models can predict the memory advantage for violating items. With exemplar-specific attention, attention is distributed uniformly for exemplars encoding violating items but is allocated to the rule dimension of exemplars encoding rulefollowing items. This differential attention makes violating items distinctive in memory. In addition to exemplar-specific attention, exemplar models need the ability to distinguish important errors from negligible ones to predict better memory for items that violate a stronger than a weaker rule. Humans are confronted with more information than they can process. Consequently, the ability to selectively attend to salient information is fundamental to our cognitive behavior. Many category learning models utilize the same attention at all locations along a dimension in the representational space (e.g., Kruschke, 1992; Love, Medin, & Gureckis, 2004; Nosofsky, 1986). The dimension-wide attention is well suited for many artificial category learning studies, in which categories are symmetric and category members are differentiated by the values on the same dimensions. For example, category A members may be large on the size dimension, whereas category B members may be small. In natural categories, however, there are inconsistent items that do not follow the structure. For example, penguins do not fly but are members of category birds, whereas bats fly but belong to category mammals. Dimension-wide attention may not fare as well when categories contain inconsistent members. Some laboratory work does suggest that attention is specific to the region along a dimension in the representational space (e.g., Aha & Goldstone, 1992; Barsalou & Medin, 1986; Lewandowsky, Kalish, & Ngang, 2002). Humans attend to different dimensions of an item depending on the context the item is in. For example, humans may attend to the color dimension when shopping for clothing but not as much when shopping for a computer. Exemplar models have a long history of explaining key psychological phenomena in the category learning research (Kruschke, 1992; Medin & Schaffer, 1978; Nosofsky, 1986). However, one finding that is problematic for exemplar models is that people better remember items that violate a structure (e.g., rule) than structure-consistent (e.g., rule-following) items (Palmeri & Nosofsky, 1995; Sakamoto & Love, in press). By storing every studied item as a separate trace and using the dimension-wide attention, current exemplar models treat consistent and inconsistent items in the same fashion and cannot predict the memory advantage for inconsistent items. In this paper, we show that an exemplar model with exemplar-specific attention (see Kruschke, 2001 for a related model with exemplar-specific “specificity”) can differentiate violating items from rulefollowing items and predict the memory advantage for violating items. While exemplars encoding rulefollowing items result in attention allocated to the rule dimension, exemplars encoding violating items result in attention distributed to the non-rule dimensions. This differential attention makes violating items distinctive in memory. We further show that in addition to the exemplarspecific attention, exemplar models need a mechanism that accentuates larger errors and minimizes the impact of smaller ones to predict a better memory for items that violate a stronger rule than items that violate a weaker rule (e.g., Sakamoto & Love, in press). In the remainder of the paper, we review previous category learning work that examines recognition memory for violating items, introduce the models, present model fits to previous findings, and discuss our modeling results. Memory Advantage for Exceptions Palmeri and Nosofsky (1995) found that humans remember exceptions to the category rule better than items that follow the rule. In their study, subjects learned to classify geometric stimuli into two contrasting categories. An imperfect rule successfully classified the majority of study items (e.g., most small items were in category A, whereas most large items were in category B), but two exceptions violated the rule (e.g., a large item that was a member of category A). Following learning, subjects completed a recognition test consisting of studied items and novel items that served as foils. The basic finding was that people recognized the exceptions better than the studied rule-following items. The special status of violating items is also suggested by the schema (e.g., Rojahn & Pettigrew, 1992) research and is not specific to category learning studies. Exemplar models with dimension-wide attention, such as ALCOVE (Kruschke, 1992) and the context model (Medin & Schaffer, 1978), cannot predict the memory advantage of exceptions. Exemplar models store every studied item as a separate trace. The likelihood of recognizing an item is determined by the item’s absolute similarity to all exemplars from both categories A and B. Exemplar models cannot predict the enhanced memory for exceptions because the exceptions share the same similarity relations with other items in memory as rule-following items do. Exceptions are distinguished from rule-following items because the exceptions’ category assignment runs counter to the rule. According to exemplar models, this reversal is not germane to recognition. In contrast, a rule-based model, such as RULEX (Nosofsky, Palmeri, & McKinley, 1994), can predict the memory advantage for exceptions. RULEX constructs rules and stores exceptions to the rules. Rule-following items are not individually stored but rather are captured by the rule. Information about exceptions is explicitly stored. The likelihood of recognizing an item is determined by the items in the exception store. The separate storage of exception information allows RULEX to predict the memory advantage of exceptions. However, RULEX cannot predict the entire pattern of Palmeri and Nosofsky’s results. In addition to the memory advantage for exceptions, they found better recognition for studied rule-following over novel items. RULEX cannot predict the memory advantage for rule-following over novel items, which the context model can predict, because rules encode very little information about rule-following items. Thus, Palmeri and Nosofsky combined RULEX and the context model (see Erikson & Kruschke, 1998 for a similar approach involving knowledge gating). The combined model was able to predict the entire pattern of their recognition data. The inability of RULEX to predict the memory advantage of rule-following over novel items suggests that humans encode more than just rules to represent rule-following items. In support of this idea, Allen and Brooks (1991) found that even when a rule is explicitly applied to a novel item, humans are still somewhat sensitive to the similarity between the novel item and previously encountered examples. This line of work argues that humans have both rule and exemplar systems for learning and recognition. An alternative approach is clustering as in SUSTAIN (Love, Medin, & Gureckis, 2004). SUSTAIN has aspects of both rule violation and exemplar memory and can predict better memory for exceptions than studied rule-following items as well as better memory for studied rule-following items than novel items. We predict that introducing exemplarspecific attention to exemplar models will allow those models to be sensitive to rule violation. Exemplar-Specific Attention An exemplar model, such as ALCOVE (Kruschke, 1992), treats exceptions and rule-following items in the same manner because all exemplars share the same attention along a dimension. Consequently, ALCOVE cannot predict the memory advantage of exceptions. In contrast, ALCOVE with exemplarspecific attention (ES-ALCOVE) should be able to predict the memory advantage for exceptions. ESALCOVE shifts attention to the rule dimension. To classify an exception in the correct category, ESALCOVE will distribute attention to the non-rule dimensions so that the exception is distinguished from the rule-following items from the opposing category. While attention will be distributed to the non-rule dimensions for the exceptions, the rulefollowing items will receive attention on the rule dimension. ES-ALCOVE could predict a memory advantage for exceptions because the exceptions will be “differentiated” from the rule-following items. We simulate ES-ALCOVE to Palmeri and Nosofsky’s results to test this intuition. In the next section, we formalize ES-ALCOVE.