Causal status as a determinant of feature centrality

One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.     

A rose in any other font would not smell as sweet: effects of perceptual fluency on categorization

Fluency--the ease with which people process information--is a central piece of information we take into account when we make judgments about the world. Prior research has shown that fluency affects judgments in a wide variety of domains, including frequency, familiarity, and confidence. In this paper, we present evidence that fluency also plays a role in categorization judgments. In Experiment 1, participants judged a variety of different exemplars to be worse category members if they were less fluent (because they were presented in a smaller typeface). In Experiment 2, we found that fluency also affected judgments of feature typicality. In Experiment 3, we demonstrated that the effects of fluency can be reversed when a salient attribution for reduced fluency is available (i.e., the stimuli are hard to read because they were printed by a printer with low toner). In Experiment 4 we replicated these effects using a within-subject design, which ruled out the possibility that the effects were a statistical artifact caused by aggregation of data. We propose a possible mechanism for these effects: if an exemplar and its category are closely related, activation of one will cause priming of the other, leading to increased fluency. Over time, feelings of fluency come to be used as a valid cue that can become confused with more traditional sources of information about category membership.     

A rule-based categorization deficit in Alzheimer's disease?

This study examined the categorization processes that Alzheimer's disease (AD) patients use during assessments of semantic memory. Rule-based categorization involves the careful, analytic processing of strict criteria to determine category membership, particularly for items from graded categories with ambiguous category membership; similarity-based categorization requires an overall comparison of a test stimulus with a prototype or remembered exemplar of the category and is relatively effective for the rapid categorization of items with unambiguous category membership. To assess these processes in AD, patients were asked to decide the category membership of test stimuli for categories with poorly defined or fuzzy boundaries (e.g., VEGETABLE) and for categories with well-defined boundaries (e.g., FEMALE) and then to judge the representativeness of the test stimulus for its chosen category. A subgroup of AD patients demonstrated a typical pattern of impaired semantic memory compared to healthy control subjects; that is, difficulty deciding the category membership of test items from fuzzy categories. Among these patients, we found no deficit in category membership decisions about items taken from well-defined categories. We also found that AD patients and healthy controls do not differ in their representativeness judgments of items within a correctly judged category. These findings are most consistent with the hypothesis that rule-based categorization difficulty limits semantic memory in AD.     

Metamorphosis: Essence, appearance, and behavior in the categorization of natural kinds

The transformation paradigm (Rips, 1989) was used to contrast causal homeostasis and strict essentialist beliefs about biological kinds. Participants read scenarios describing animals that changed their appearance and behavior through either accidental mutation or developmental maturation and then rated the animals on the basis of similarity, typicality, and category membership both before and after the change. Experiment 1 in the present study replicated the dissociation of typicality and categorization reported by Rips (1989) but also revealed systematic individual differences in categorization. With typicality and membership ratings collected between participants, however, Experiment 2 found no evidence for the dissociation and few essentialist responders. In Experiment 3, excluding information about offspring led most participants to categorize on the basis of appearance and behavior alone. However, with offspring information included and with questioning focused on the change of kind, essentialist categorization was still surprisingly rare. We conclude that strict essentialist categorization in the transformation task is relatively rare and highly task dependent, and that categorization is more commonly based on causal homeostasis.     

Explanation and categorization: How “why?” informs “what?”

Recent theoretical and empirical work suggests that explanation and categorization are intimately related. This paper explores the hypothesis that explanations can help structure conceptual representations, and thereby influence the relative importance of features in categorization decisions. In particular, features may be differentially important depending on the role they play in explaining other features or aspects of category membership. Two experiments manipulate whether a feature is explained mechanistically, by appeal to proximate causes, or functionally, by appeal to a function or goal. Explanation type has a significant impact on the relative importance of features in subsequent categorization judgments, with functional explanations reversing previously documented effects of ‘causal status’. The findings suggest that a feature’s explanatory importance can impact categorization, and that explanatory relationships, in addition to causal relationships, are critical to understanding conceptual representation.     

Causal knowledge and categories: the effects of causal beliefs on categorization, induction, and similarity

Despite the recent interest in the theoretical knowledge embedded in human representations of categories, little research has systematically manipulated the structure of such knowledge. Across four experiments this study assessed the effects of interattribute causal laws on a number of category-based judgments. The authors found that (a) any attribute occupying a central position in a network of causal relationships comes to dominate category membership, (b) combinations of attribute values are important to category membership to the extent they jointly confirm or violate the causal laws, and (c) the presence of causal knowledge affects the induction of new properties to the category. These effects were a result of the causal laws, rather than the empirical correlations produced by those laws. Implications for the doctrine of psychological essentialism, similarity-based models of categorization, and the representation of causal knowledge are discussed.     

Classification in well-defined and ill-defined categories: evidence for common processing strategies

Early work in perceptual and conceptual categorization assumed that categories had criterial features and that category membership could be determined by logical rules for the combination of features. More recent theories have assumed that categories have an ill-defined structure and have prosposed probabilistic or global similarity models for the verification of category membership. In the experiments reported here, several models of categorization were compared, using one set of categories having criterial features and another set having an ill-defined structure. Schematic faces were used as exemplars in both cases. Because many models depend on distance in a multidimensional space for their predictions, in Experiment 1 a multidimensional scaling study was performed using the faces of both sets as stimuli, In Experiment 2, subjects learned the category membership of faces for the categories having criterial features. After learning, reaction times for category verification and typicality judgments were obtained. Subjects also judged the similarity of pairs of faces. Since these categories had characteristic as well as defining features, it was possible to test the predictions of the feature comparison model (Smith et al.), which asserts that reaction times and typicalities are affected by characteristic features. Only weak support for this model was obtained. Instead, it appeared that subjects developed logical rules for the classification of faces. A characteristic feature affected reaction times only when it was part of the rule system devised by the subject. The procedure for Experiment 3 was like that for Experiment 2, but with ill-defined rather than well-defined categories. The obtained reaction times had high correlations with some of the models for ill-defined categories. However, subjects' performance could best be described as one of feature testing based on a logical rule system for classification. These experiments indicate that whether or not categories have criterial features, subjects attempt to develop a set of feature tests that allow for exemplar classification. Previous evidence supporting probabilistic or similarity models may be interpreted as resulting from subjects' use of the most efficient rules for classification and the averaging of responses for subjects using different sets of rules.     

Can semi-supervised learning explain incorrect beliefs about categories?

Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory for the items encountered during the supervised learning phase. These changes were influenced by both the range and frequency distribution of the unlabeled stimuli: mental category boundaries shifted toward the middle of the range and toward the trough of the bimodal distribution of unlabeled items, whereas beliefs about the most representative category members shifted toward the modes of the unlabeled distribution. One consequence of this shift in representations is a false-consensus effect (Experiment 3) where participants, despite receiving very disparate training experiences, show strong agreement in judgments about representativeness and boundary location following unsupervised category judgments.     

Theory-based categorization under speeded conditions

It is widely accepted that similarity influences rapid categorization, whereas theories can influence only more leisurely category judgments. In contrast, we argue that it is not the type of knowledge used that determines categorization speed, but rather the complexity of the categorization processes. In two experiments, participants learned four categories of items, each consisting of three causally related features. Participants gave more weight to cause features than to effect features, even under speeded response conditions. Furthermore, the time required to make judgments was equivalent, regardless of whether participants were using causal knowledge or base-rate information. We argue that both causal knowledge and base-rate information, once precompiled during learning, can be used at roughly the same speeds during categorization, thus demonstrating an important parallel between these two types of knowledge.     

Learning and transfer of category knowledge in an indirect categorization task

Knowledge representations acquired during category learning experiments are ‘tuned’ to the task goal. A useful paradigm to study category representations is indirect category learning. In the present article, we propose a new indirect categorization task called the “same”–“different” categorization task. The same–different categorization task is a regular same–different task, but the question asked to the participants is about the stimulus category membership instead of stimulus identity. Experiment 1 explores the possibility of indirectly learning rule-based and information-integration category structures using the new paradigm. The results suggest that there is little learning about the category structures resulting from an indirect categorization task unless the categories can be separated by a one-dimensional rule. Experiment 2 explores whether a category representation learned indirectly can be used in a direct classification task (and vice versa). The results suggest that previous categorical knowledge acquired during a direct classification task can be expressed in the same–different categorization task only when the categories can be separated by a rule that is easily verbalized. Implications of these results for categorization research are discussed.     

Classification as diagnostic reasoning

An ongoing goal in the field of categorization has been to determine how objects’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed.     

Induction with uncertain categories: When do people consider the category alternatives?

These three experiments examined how people make property inferences about exemplars whose category membership is uncertain. Participants were shown two categories and a novel exemplar with a feature that indicated that the exemplar was more likely to belong to one category (target) than to the other (nontarget). Participants then made categorization decisions and property inferences about the novel exemplar. In some conditions, property inferences could be made only by considering both target and nontarget categories. In other conditions, predictions could be based on both categories or on the target category alone. Consistent with previous studies (e.g., Murphy & Ross, 1994, 2005), we found that many people made predictions based only on consideration of the target category. However, the prevalence of such single-category reasoning was greatly reduced by highlighting the costs of neglecting nontarget alternatives and by asking for inferences before categorization decisions. The results suggest that previous work may have exaggerated the prevalence of single-category reasoning and that people may be more flexible in their use of multiple categories in property inference than has been previously recognized.     

Domain differences in absolute judgments of category membership: evidence for an essentialist account of categorization

There has been some debate about the correspondence between typicality gradients and category membership. The present study investigates the relationship between these two measures in the domains of animals and artifacts. Forty-two adults judged the degree of typicality or category membership of 293 animals and artifacts. The subjects’ tendency for animals, but not for artifacts, was to make more absolute ratings on category membership (i.e., judging exemplars as definitely members or definitely not members of their respective category) than on typicality. More importantly, at almost every level of typicality, subjects were more likely to make absolute judgments of category membership for animals than for artifacts. These results indicate that people treat category membership of animals as relatively absolute (which best fits an essentialist model of categorization) and treat category membership of artifacts as relatively graded (which best fits a prototype model of categorization). These domain differences add crucial supporting evidence for claims about the domain-specificity of essentialism.     

On the nature of implicit categorization

Current categorization models disagree about whether people make a priori assumptions about the structure of unfamiliar categories. Data from two experiments provided strong evidence that people do not make such assumptions. These results rule out prototype models and many decision bound models of categorization. We review previously published neuropsychological results that favor the assumption that category learning relies on a procedural-memory-based system, rather than on an instance- based system (as is assumed by exemplar models). On the basis of these results, a new categorylearning model is proposed that makes no a priori assumptions about category structure and that relies on procedural learning and memory.     

The role of explanation in categorization decisions

Research into the role of explanation on the categorization process has yielded conflicting conclusions. Some theorists stress the importance of explanation, arguing that explanations provide a causal structure necessary to the categorization process. Others discount its significance, arguing that explanation is neither necessary nor sufficient for categorization. Experimentally, explanation has shown modest success in accounting for some categories but not others. Across three experiments, we test whether the central features of a category are the ones that capture the most explanatory structure. The objectives of the current study are threefold: to determine the importance of explanation in natural kind and artifact categorization; to understand the implications of feature correlations as they relate to explanation; and to further delineate the benefit of explanation outside of a functional role. Experiment 1 demonstrates that explanation plays a more dominant role in artifact versus natural kind categorization. Experiment 2 provides evidence that correlated features are neither necessary nor sufficient for categorization. Moreover, it demonstrates that, in the absence of correlation, people still rely on explanation for artifacts but not for natural kinds. Experiment 3 tests to what extent functional information underlies explanation in artifact categorization. We demonstrate that functional relations are not necessary for explanatory‐based categorization. Our results, coupled with previous evidence, suggest a dichotomous role for explanation‐based categorization. On the one hand, explanation is virtually ignored when categorizing biological kinds; on the other, explanation fosters artifact categorization. Implications for categorization and category‐specific disorder are considered.     

Generalization: A Practice of Situated Categorization in Talk

This paper analyzes four instances in talk of generalization about people, that is, of using statements about one or more people as the basis of stating something about a category. Generalization can be seen as a categorization practice which involves a reflexive relationship between the generalized-from person or people and the generalized-to category. One thing that is accomplished through generalization is instruction in how to understand the identity of the generalized-from person or people, so in addition to being understood as a practice of categorization, generalization can also be understood as a practice of identification. Somewhat incidentally, this paper also illustrates the importance of certain methodological issues related to membership categorization analysis and contributes to the growing body of work that connects membership categorization analysis with sequential conversation analysis.     

Prototypes and particulars: geometric and experience-dependent spatial categories

People use geometric cues to form spatial categories. This study investigated whether people also use the spatial distribution of exemplars. Adults pointed to remembered locations on a tabletop. In Experiment 1, a target was placed in each geometric category, and the location of targets was varied. Adults' responses were biased away from a midline category boundary toward geometric prototypes located at the centers of left and right categories. Experiment 2 showed that prototype effects were not influenced by cross-category interactions. In Experiment 3, subsets of targets were positioned at different locations within each category. When prototype effects were removed, there was a bias toward the center of the exemplar distribution, suggesting that common categorization processes operate across spatial and object domains.     

Prior knowledge and subtyping effects in children's category learning

Two experiments examined how 5- and 10-year-old children revise their category representations when exposed to exemplars that are congruent or incongruent with existing knowledge. During training children were presented with exemplars containing features that were congruent or incongruent with children's social stereotypes together with a stereotype-neutral feature. In the knowledge-subtyping condition this neutral feature predicted the stereotype-congruence of the other features. In the knowledge-standard condition the neutral feature was uncorrelated with stereotype-congruence. At test children made judgements about feature co-occurrence within the learned category. In each experiment these judgements were influenced by both stereotypical beliefs and exemplar observation. Stereotypical beliefs, however, had a greater influence on co-occurrence judgements in the knowledge-subtyping than in the standard conditions. In Experiment 2 these effects were shown to generalize to judgements about features that were not presented during training. These results challenge current models of knowledge-based categorization by showing that exemplar structure determines whether novel exemplar features are incorporated into category representations.