Acquisition of basic object categories

Two implications of best-example theory for category acquisition were considered. The first is that categories which people acquire based on initial exposure to good exemplars should be learned more easily and (at first) more accurately than categories based on initial exposure to poor exemplars. The second is that people should generally learn that the best exemplars are category members, before learning that the poor exemplars are category members. These implications are based on the premise that people generalize based on similarity, and that the best example has maximal within-category similarity and minimal extra-category similarity, while the poor examples have minimal within-category similarity and relatively high extra-category similarity. Both implications were strongly supported by the present research. It was also found that when pressure to communicate was removed, comprehension and production of category names were virtually identical. The predictions of best-example theory concerning the conceptual structures underlying the words used by children who are just beginning to talk were discussed briefly. This research also allowed the replication of several important categorization results which had previously been found with real-world categories, with a set of artificial concrete object categories.     

Differential brain responses when applying criterion attribute versus family resemblance rule learning

Subsystems of category learning have been identified on the basis of general domains of content (e.g., tools, faces). The present study examined categories from the standpoint of internal structure and determined brain topography associated with expressing two fundamentally different category rule structures (criterion attribute, CA, and family resemblance, FR). CA category learning involves processing stimuli by isolated features and classifying by properties held by all members. FR learning involves processing stimuli by integral wholes and classifying on overall similarity among members without sharing identical features. fMRI BOLD response to CA and FR categorization was measured with pseudowords as stimuli. Category knowledge for both tasks was mastered prior to brain imaging. Areas of activation emerged unique to the structure of each category and followed from the nature of the rule abstraction procedure. CA categorization was implemented by strong target monitoring and expectation (medial parietal), rule maintenance in working memory, feature selection processes (inferior frontal), and a sensitivity to high frequency components of the stimulus such as isolated features (anterior temporal). FR categorization, consistent with its multi-featural nature, involved word-level processing (left extrastriate) that evoked articulatory rehearsal (medial cerebellar). The data suggest category structure is an important determinant of brain response during categorization. For instance, anterior temporal structures may help attune visual processing systems to high frequency components to support the learning of criterial, highly predictive rules.     

What's behind different kinds of kinds: effects of statistical density on learning and representation of categories

This research examined how differences in category structure affect category learning and category representation across points of development. The authors specifically focused on category density--or the proportion of category-relevant variance to the total variance. Results of Experiments 1-3 showed a clear dissociation between dense and sparse categories: Whereas dense categories were readily learned without supervision, learning of sparse categories required supervision. There were also developmental differences in how statistical density affected category representation. Although children represented both dense and sparse categories on the basis of the overall similarity (Experiment 4A), adults represented dense categories on the basis of similarity and represented sparse categories on the basis of the inclusion rule (Experiment 4B). The results support the notion that statistical structure interacts with the learning regime in their effects on category learning. In addition, these results elucidate important developmental differences in how categories are represented, which presents interesting challenges for theories of categorization.     

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.     

Memory storage and retrieval processes in category learning

The detailed course of learning is studied for categorization tasks defined by independent or contingent probability distributions over the features of category exemplars. College-age subjects viewed sequences of bar charts that simulated symptom patterns and responded to each chart with a recognition and a categorization judgment. Fuzzy, probabilistically defined categories were learned relatively rapidly when individual features were correlated with category assignment, more slowly when only patterns carried category information. Limits of performance were suboptimal, evidently because of capacity limitations on judgmental processes as well as limitations on memory. Categorization proved systematically related to feature and exemplar probabilities, under different circumstances, and to similarity among exemplars of categories. Unique retrieval cues for exemplar patterns facilitated recognition but entered into categorization only at retention intervals within the range of short-term memory. The findings are interpreted within the framework of a general array model that yields both exemplar-similarity and feature-frequency models as special cases and provides quantitative accounts of the course of learning in each of the categorization tasks studied.     

The impact of training sequence and between-category similarity on unsupervised induction

Studies of supervised categorization often show better learning when examples are presented in random alternation rather than massed by category, but such interleaving impairs learning in unsupervised tasks. The exemplar comparison hypothesis explains this result by assuming that people in unsupervised tasks discover generalizations about categories by comparing individual examples, and that interleaving increases the difficulty of such within-category comparisons. The category invention hypothesis explains the interleaving effect by assuming that people are more likely to merge or aggregate potentially separable categories when they are interleaved, and this initial failure to recognize separate categories then acts as an effective barrier to further learning. The present experiments show that the interleaving effect depends on the similarity or alignability of the presented categories. This result provides evidence in favour of the category invention hypothesis, which expects that highly dissimilar (nonalignable) categories will resist aggregation and hence will not be affected by interleaving. The nonmonotonic pattern of learning, and the interaction between sequence and similarity, observed in the alignable conditions of Experiment 3 were also consistent with category invention, but not with exemplar comparison. Implications are discussed for real-world learning, especially the relationship between exposure and learning and between supervised and unsupervised learning.     

Effects of classification context on categorization in natural categories

The patterns of classification of borderline instances of eight common taxonomic categories were examined under three different instructional conditions to test two predictions: first, that lack of a specified context contributes to vagueness in categorization, and second, that altering the purpose of classification can lead to greater or lesser dependence on similarity in classification. The instructional conditions contrasted purely pragmatic with more technical/quasi-legal contexts as purposes for classification, and these were compared with a no-context control. The measures of category vagueness were between-subjects disagreement and within-subjects consistency, and the measures of similarity-based categorization were category breadth and the correlation of instance categorization probability with mean rated typicality, independently measured in a neutral context. Contrary to predictions, none of the measures of vagueness, reliability, category breadth, or correlation with typicality were generally affected by the instructional setting as a function of pragmatic versus technical purposes. Only one subcondition, in which a situational context was implied in addition to a purposive context, produced a significant change in categorization. Further experiments demonstrated that the effect of context was not increased when participants talked their way through the task, and that a technical context did not elicit more all-or-none categorization than did a pragmatic context. These findings place an important boundary condition on the effects of instructional context on conceptual categorization.     

Categorization reaction time,category structure,and category size in semantic memory using artificial categories

This study examined the effects of category structure and category size on categorization reaction time, artificial categories being used to obtain greater experimental control than is usually found in semantic memory research. Four artificial categories varying in structure (hierarchical and nonhierarchical) and size (8 or 16 instances) were introduced to 20 adolescent subjects over a 12-week period by means of stories, exercises, and discussions. Significantly longer categorization reaction times were required for instances from the hierarchical categories, but no set size effect was found. The application of clustering and multidimensional scaling procedures to subjects’ free recall data revealed that subjects had acquired the hierarchical structures but imposed their own structures on the other categories. These findings were interpreted within a spreading activation framework.     

The role of similarity in the development of categorization

Early in development, humans exhibit the ability to form categories and overlook differences for the sake of generality. This ability poses several important questions: How does categorization arise? What processes underlie category formation? And how are categories mentally represented? We argue that the development of categorization is grounded in perceptual and attentional mechanisms capable of detecting multiple correspondences or similarities in the environment. We present evidence that: (a) similarity can drive categorization early in development; and (b) early in development, humans have powerful learning mechanisms that enable them to extract regularities in the environment. We conclude that, despite remaining challenges, the similarity-based approach offers a promising account of the development of categorization.     

Domain differences in the structure of artifactual and natural categories

In three experiments, different methodologies, measures, and items were employed to address the question of whether, and to what extent, membership in a semantic category is all or none (i.e., absolute) or a matter of degree (i.e., graded). Resemblance theory claims that categorization is based on similarity, and because similarity is graded, category membership may also be graded. Psychological essentialism asserts that categorization is based on the presumption of the category essence. Because artifactual (e.g., FURNITURE) and natural (e.g., FRUIT) categories have different sorts of essences, artifacts and natural kinds may be categorized in qualitatively different manners. The results converged onthe finding of a robust domain difference in category structure: Artifactual categories were more graded than natural categories. Furthermore, typicality reliably predicted absolute category membership, but failed to predict graded category membership. These results suggest that resemblance theory and psychological essentialism may provide a concerted account of representation and categorization across domains.     

6岁儿童的类别学习能力:类别表征、注意和分类策略

探讨了6岁儿童的类别学习能力、类别表征和分类策略。62名儿童参加了实验,实验1采用了"5/4模型"类别结构,实验2采用了"3/3类别结构"。结果发现:6岁儿童已经具备了一定的类别学习能力;相对于原型表征,6岁儿童更倾向于进行样例表征;6岁儿童在注意上具有定位在高典型性特征维度上的能力,但不具备定位在区分性特征维度上的能力;在类别学习策略上主要采用单维度分类策略和规则加例外的分类策略。     

The effects of concurrent task interference on category learning: evidence for multiple category learning systems

Participants learned simple and complex category structures under typical single-task conditions and when performing a simultaneous numerical Stroop task. In the simple categorization tasks, each set of contrasting categories was separated by a unidimensional explicit rule, whereas the complex tasks required integrating information from three stimulus dimensions and resulted in implicit rules that were difficult to verbalize. The concurrent Stroop task dramatically impaired learning of the simple explicit rules, but did not significantly delay learning of the complex implicit rules. These results support the hypothesis that category learning is mediated by multiple learning systems.     

The Neuropsychological Bases of Category Learning

There is a long history of research into how people learn new categories of objects or events. Recent advances have led to new insights about the neuropsychological basis of this critically important cognitive process. In particular, there is now good evidence that the frontal cortex and basal ganglia contribute to category learning, that medial temporal lobe structures make a more minor contribution, and that categorization rules are not represented in visual cortex. There is also strong evidence that normal category learning is mediated by at least two separate systems. A recent neuropsychological theory of category learning that is consistent with these data is described.     

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.     

Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization

In three perceptual classification experiments involving ill-defined category structures, extreme prototype enhancement effects were observed in which prototypes were classified more accurately than other category instances. Such empirical findings can prove theoretically challenging to exemplar-based models of categorization if prototypes are psychological central tendencies of category instances. We found instead that category prototypes were sometimes better characterized as psychological extreme points relative to contrast categories. Extending a classic and widely cited study (Posner & Keele, 1968), participants learned categories created from distortions of dot patterns arranged in familiar shapes. Participants then made pairwise similarity judgements of the patterns. Multidimensional scaling (MDS) analyses of the similarity data revealed the prototypes to be psychological extreme points, not central tendencies. Evidence for extreme point representations was also found for novel prototype patterns displaying a symmetry structure and for prototypes of grid patterns used in recent studies by McLaren and colleagues (McLaren, Bennet, Guttman-Nahir, Kim, & M ackintosh, 1995). When used in combination with the derived M DS solutions, an exemplar-based model of categorization, the Generalized Context Model (Nosofsky, 1986), provided good fits to the observed categorization data in all three experiments.     

Array training in a categorization task

Two components of categorization, within-category commonalities and between-category distinctiveness, were investigated in a categorization task. Subjects learned three prototype categories composed of moderately high distortions, by observing arrays containing patterns that belonged either to a common prototype category or to three different categories; a third group learned patterns presented one at a time, mirroring the standard paradigm. Following 6 learning blocks, subjects transferred to old patterns and new patterns at low-, medium-, and high-level distortions of the category prototype. The results showed that array training facilitated learning, especially when patterns in the array belonged to the same category. Transfer results showed a strong gradient effect across pattern distortion level for all conditions, with the highest performance obtained following array training on different category patterns and worst in the control condition. Interestingly, the old training patterns were classified worse than new low and no better than medium distortions. Neither this ordering nor the steepness of the gradient across prototype similarity for each condition could be predicted by the generalized context model. A prototype model better captured the steep gradient and ordinal pattern of results, although the overall fits were only slightly better than the exemplar model. The crucial role played by category commonalities and distinctiveness on categorical representations is addressed.     

Procedural learning in perceptual categorization

In two experiments, observers learned two types of category structures: those in which perfect accuracy could be achieved via some explicit rule-based strategy and those in which perfect accuracy required integrating information from separate perceptual dimensions at some predecisional stage. At the end of training, some observers were required to switch their hands on the response keys, whereas the assignment of categories to response keys was switched for other observers. With the rule-based category structures, neither change in response instructions interfered with categorization accuracy. However, with the information-integration structures, switching response key assignments interfered with categorization performance, but switching hands did not. These results are consistent with the hypothesis that abstract category labels are learned in rule-based categorization, whereas response positions are learned in information-integration categorization. The association to response positions also supports the hypothesis of a procedural-learning-based component to information integration categorization.     

非监控类别学习的分类策略与表征

类别学习是人类对不同类别加以归类的过程。类别信息的表征、分类策略运用的特点一直是类别学习研究的重点。非监控类别学习可分为直接的非监控类别学习和间接的非监控类别学习。直接的非监控类别学习(非限制任务, 限制任务)中被试的分类策略具有分类“单维度倾向”策略特点,类别变异程度会影响类别表征; 间接的非监控类别学习更倾向形成相似性表征, 直接的非监控类别学习则为基于规则表征。现有的非监控类别学习的理论对分类策略和表征的解释仍显薄弱, 不同学习任务下类别迁移和知识效应的研究还存在不足, 未来研究还需要进一步验证知识效应对非监控类别学习的认知加工过程的影响、探索影响类别表征形成的因素等问题。     

Distinct mechanisms in visual category learning

The ways in which visual categories are learned, and in which well-established categories are represented and retrieved, are fundamental issues of cognitive neuroscience. Researchers have typically studied these issues separately, and the transition from the initial phase of category learning to expertise is poorly characterized. The acquisition of novel categories has been shown to depend on the striatum, hippocampus, and prefrontal cortex, whereas visual category expertise has been shown to involve changes in inferior temporal cortex. The goal of the present experiment is to understand the respective roles of these brain regions in the transition from initial learning to expertise when category judgments are being made. Subjects were explicitly trained, over 2 days, to classify realistic faces. Subjects then performed the categorization task during fMRI scanning, as well as a perceptual matching task, in order to characterize how brain regions respond to these faces when not explicitly categorizing them. We found that, during face categorization, face-selective inferotemporal cortex, lateral prefrontal cortex, and dorsal striatum are more responsive to faces near the category boundary, which are most difficult to categorize. In contrast, the hippocampus and left superior frontal sulcus responded most to faces farthest from the category boundary. These dissociable effects suggest that there are several distinct neural mechanisms involved in categorization, and provide a framework for understanding the contribution of each of these brain regions in categorization.