类别使用的特征诊断效应

运用预备实验评定过的鱼轮廓图,考察了类别判断和特征预测这两种类别使用方式下的特征诊断效应。被试是48名大学生,学习材料是两类鱼的类别成员,在测验阶段,实验1判断新项目类别标签,实验2预测新项目的缺失特征。结果表明,类别判断条件下,单个特征诊断力加强和诊断性特征数量增加都对类别判断有促进作用;特征预测条件下,只有单个特征诊断力加强可以促进特征预测,诊断性特征数量增加无助于特征预测     

On the Interaction of Prior Knowledge and Stimulus Structure in Category Learning

Contemporary theories of categorization propose that concepts are coherent in virtue of being embedded in a network of theories about the world. Those theories function to pick out some of the many possible features of a set of objects as most salient for purposes of classification, a process that is complex and still poorly understood (Murphy & Medin, 1985). Part of what makes this account incomplete is a lack of information as to (1) what makes a feature salient on a given occasion and (2) how feature salience interacts with category structure to determine the course of learning. We report on the results of three studies of category learning using complex schematic drawings to show that (1) the contrast set defined by one's initial encounters with category exemplars can be a source of individual differences in feature salience assignments; (2) such effects are short-lived in the face of clear evidence about actual feature diagnosticity; and (3) more robust prior hypotheses interact with category structure to either enhance learning or impede it. The enhancement occurs when the hypothesis emphasizes category-relevant features, even if the hypothesis is in fact incorrect. A hypothesis that assigns high salience to irrelevant features impedes learning. Learning does occur as feedback concerning category structure leads to enhanced salience for relevant features. Salience of irrelevant features remains high, however, suggesting that such learning as occurs involves augmentation and not total revision of the (incorrect) prior hypothesis.     

项目诊断性在特征推理中作用的研究

该研究在Murphy等人2005年研究的基础上,探讨了项目诊断性对特征推理的影响。实验1探讨归类情况下,项目诊断性对特征推理的影响。实验2探讨不归类情况下,项目诊断性对特征推理的影响。实验结果表明,归类与否决定项目诊断性是否影响特征推理。在归类情况下,高低两种诊断性条件下的特征概率判断没有差异,表明归类情况下,项目诊断性不影响特征推理;在不归类的情况下,高低两种诊断性条件下的特征概率判断存在显著差异,表明不归类情况下,项目诊断性影响特征推理。     

Learning abstract relations from using categories

When people learn categories, the importance of the features and relations in the category representation reflects both their diagnosticity for classification and their relevance to the use of the category. In earlier work in which the influence of category use on the representation has been shown, only cases in which the features and relations were simple, observable, and very specific were examined. Learners may begin to understand the underlying similarities of category members by using the categories. In the four experiments presented here, learners applied a simple category-specific formula to category members. The test results showed that the learners had incorporated relations among features from this use, including cases in which the relations were abstract. This learning occurred even though the relations were actually not predictive of category membership but just perceived to be so as a function of the use.     

Past experience influences object representation in working memory

The nature of object representation in working memory is vital to establishing the capacity of working memory, which in turn shapes the limits of visual cognition and awareness. Although current theories discuss whether representations in working memory are feature-based or object-based, no theory has considered the role of past experience. However, work with humans and non-human primates suggests that once participants learn which features are important for category membership, these diagnostic features become more salient than non-diagnostic features in long-term memory and object recognition. Critically, the brain areas involved in this diagnosticity effect are also recruited during working memory tasks. We report two experiments testing whether a diagnosticity effect exists in working memory; and whether it is present when visual information is encoded into working memory, or if it is the result of maintenance within working memory. Results showed a diagnosticity effect which was present at encoding. Maintenance did not influence the nature of object representation in working memory. These findings show that the meaning we glean from our past experience has a profound influence on the nature of object representation in working memory.     

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.     

Packing: A Geometric Analysis of Feature Selection and Category Formation

This paper presents a geometrical analysis of how local interactions in a large population of categories packed into a feature space create a global structure of feature relevance. The theory is a formal proof that the joint optimization of discrimination and inclusion creates a smooth space of categories such that near categories in the similarity space have similar generalization gradients. Packing theory offers a unified account of several phenomena in human categorization including the differential importance of different features for different kinds of categories, the dissociation between judgments of similarity and judgments of category membership, and children's ability to generalize a category from very few examples.     

Caught in the act: When impressions based on highly diagnostic behaviours are resistant to contradiction

Research and theory emphasizing the role of cue diagnosticity in judgment (e.g. Skowronski and Carlston, 1987, 1989) suggests that under the proper conditions: (a) negativity effects should be observed in judgments of honesty/dishonesty; (b) positivity effects should be observed in judgments of intelligence/unintelligence, and (c) intelligence-implicative and dishonesty-implicative cues should be increasingly difficult to contradict as those cues become more extreme. Two experiments yielded data consistent with these predictions. In addition, two other important findings emerged from these studies. The results of Experiments 1 and 2 indicated that subjects do not respond as if highly diagnostic cues are sufficient for category membership, suggesting that the representational format of trait categories does not correspond to the format suggested by the ‘classical model’ of categorization. The results of Experiment 2 also indicated that negativity and positivity effects are not substantially altered by a role-playing manipulation designed to increase subjects' involvement in the judgment task.     

Concept learning and feature interpretation

Models of categorization often assume that people classify new instances directly on the basis of the presented, observable features. Recent research, however, has suggested that the coherence of a category may depend in part on more abstract features that can link together observable features that might otherwise seem to have little similarity. Thus, category learning may also involve the determination of the appropriate abstract features that underlie a category and link together the observable features. We show in four experiments that observable features of a category member are often interpreted as congruent with abstract features that are suggested by observable features of other highly available category members. Our discussion focuses on the implications of these findings for future research.     

Feature Centrality and Conceptual Coherence

Conceptual features differ in how mentally tranformable they are. A robin that does not eat is harder to imagine than a robin that does not chirp. We argue that features are immutable to the extent that they are central in a network of dependency relations. The immutability of a feature reflects how much the internal structure of a concept depends on that feature; i.e., how much the feature contributes to the concept's coherence. Complementarily, mutability reflects the aspects in which a concept is flexible. We show that features can be reliably ordered according to their mutability using tasks that require people to conceive of objects missing a feature, and that mutability (conceptual centrality) can be distinguished from category centrality and from diagnosticity and salience. We test a model of mutability based on asymmetric, unlabeled, pairwise dependency relations. With no free parameters, the model provides reasonable fits to data. Qualitative tests of the model show that mutability judgments are unaffected by the type of dependency relation and that dependency structure influences judgments of variability.     

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.     

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.     

Diagnosticity and prototypicality in category learning: a comparison of inference learning and classification learning

Category knowledge allows for both the determination of category membership and an understanding of what the members of a category are like. Diagnostic information is used to determine category membership; prototypical information reflects the most likely features given category membership. Two experiments examined 2 means of category learning, classification and inference learning, in terms of sensitivity to diagnostic and prototypical information. Classification learners were highly sensitive to diagnostic features but not sensitive to nondiagnostic, but prototypical, features. Inference learners were less sensitive to the diagnostic features than were classification learners and were also sensitive to the nondiagnostic, prototypical, features. Discussion focuses on aspects of the 2 learning tasks that might lead to this differential sensitivity and the implications for learning real-world categories.     

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.     

Where to look first for an explanation of induction with uncertain categories

Recent research has examined how people predict unobserved features of an object when its category membership is ambiguous. The debate has focused on whether predictions are based solely on information from the most likely category, or whether information from other possible categories is also used. In the present experiment, we compared these category-based approaches with feature conjunction reasoning, where predictions are based on a comparison among exemplars (rather than categories) that share features with a target object. Reasoning strategies were assessed by examining patterns of feature prediction and by using an eye gaze measure of attention during induction. The main findings were (1) the majority of participants used feature conjunction rather than categorical strategies, (2) people predominantly gazed at the exemplars that were most similar to the target object, and (3) although people gazed most at the most probable category to which an object could belong, they also attended to other plausible category alternatives during induction. These findings question the extent to which category-based reasoning is used for induction when category membership is uncertain.     

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.     

Inference using categories

How do people use category membership and similarity for making inductive inferences? The authors addressed this question by examining the impact of category labels and category features on inference and classification tasks that were designed to be comparable. In the inference task, participants predicted the value of a missing feature of an item given its category label and other feature values. In the classification task, participants predicted the category label of an item given its feature values. The results from 4 experiments suggest that category membership influences inference even when similarity information contradicts the category label. This tendency was stronger when the category label conveyed class inclusion information than when the label reflected a feature of the category. These findings suggest that category membership affects inference beyond similarity and that category labels and category features are 2 different things.     

Overcoming default categorical bias in spatial memory

In the present study, we investigated whether a strong default categorical bias can be overcome in spatial memory by using alternative membership information. In three experiments, we tested location memory in a circular space while providing participants with an alternative categorization. We found that visual presentation of the boundaries of the alternative categories (Experiment 1) did not induce the use of the alternative categories in estimation. In contrast, visual cuing of the alternative category membership of a target (Experiment 2) and unique target feature information associated with each alternative category (Experiment 3) successfully led to the use of the alternative categories in estimation. Taken together, the results indicate that default categorical bias in spatial memory can be overcome when appropriate cues are provided. We discuss how these findings expand the category adjustment model (Huttenlocher, Hedges, & Duncan, 1991) in spatial memory by proposing a retrieval-based category adjustment (RCA) model.