类别学习中的分类和推理

该文介绍了类别学习中的分类和推理两种任务,并从学习的条件、过程、结果和发展等方面的归纳了当前研究的最新进展。表明了类别的分类学习和推理学习有相同的形式,但学习的信息处理过程和学习的结果不同。分类学习关注类别间的区分性信息,更可能是样例学习结果;推理学习更为关注单个类别内部的共同性信息,更可能是原型学习结果。这方面的结论强化了基于解释的观点。     

社会分类的特性,维度及心理效应

社会分类是个体基于共享相似性将人群分为不同类别的一种主观心理过程, 具有自动化,灵活性和潜在模糊性等特性.社会分类的维度既包括性别,年龄和种族等自然线索, 也包括语言,职业和社会身份等重要社会线索.在社会分类过程中, 不同维度往往具有相对重要性, 表现为优势维度效应; 不同维度相互之间也会发生作用, 产生交叉分类效应, 研究者从不同的视角对这种交叉分类效应进行了解释.未来研究应该采用多样化的方法和技术从分类主体,分类目标,分类情境以及社会文化因素等多角度探讨社会分类的心理加工机制, 构建更具整合性的社会分类理论模型.     

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.     

Strategy development and learning differences in supervised and unsupervised categorization

The processes that determine unsupervised categorization, the task of classifying stimuli without guidance or feedback, are poorly understood. Two experiments examined the emergence and plasticity of unsupervised strategies using perceptual stimuli that varied along two separable dimensions. In the first experiment, participants either classified stimuli into any two categories of their choice or learned identical classifications by supervised categorization. Irrespective of the complexity of classification, supervised and unsupervised learning rates differed little when both modes of learning were maximally comparable. The second experiment examined the plasticity of unsupervised classifications by introducing novel stimuli halfway through training. Whether or not people altered their strategies, they responded to novel stimuli in a gradual manner. The gradual and continuous evolution and adaptation of strategies suggests that unsupervised categorization involves true learning which shares many properties of supervised category learning. We also show that the choice of unsupervised strategy cannot be predicted from the properties of early learning trials, but is best understood as a function of the initial distribution of dimensional attention.     

A bird's eye view: biological categorization and reasoning within and across cultures

Many psychological studies of categorization and reasoning use undergraduates to make claims about human conceptualization. Generalizability of findings to other populations is often assumed but rarely tested. Even when comparative studies are conducted, it may be challenging to interpret differences. As a partial remedy, in the present studies we adopt a 'triangulation strategy' to evaluate the ways expertise and culturally different belief systems can lead to different ways of conceptualizing the biological world. We use three groups (US bird experts, US undergraduates, and ordinary Itza' Maya) and two sets of birds (North American and Central American). Categorization tasks show considerable similarity among the three groups' taxonomic sorts, but also systematic differences. Notably, US expert categorization is more similar to Itza' than to US novice categorization. The differences are magnified on inductive reasoning tasks where only undergraduates show patterns of judgment that are largely consistent with current models of category-based taxonomic inference. The Maya commonly employ causal and ecological reasoning rather than taxonomic reasoning. Experts use a mixture of strategies (including causal and ecological reasoning), only some of which current models explain. US and Itza' informants differed markedly when reasoning about passerines (songbirds), reflecting the somewhat different role that songbirds play in the two cultures. The results call into question the importance of similarity-based notions of typicality and central tendency in natural categorization and reasoning. These findings also show that relative expertise leads to a convergence of thought that transcends cultural boundaries and shared experiences.     

Acquired Equivalence Changes Stimulus Representations

Acquired equivalence is a paradigm in which generalization is increased between two superficially dissimilar stimuli (or antecedents) that have previously been associated with similar outcomes (or consequents). Several possible mechanisms have been proposed, including changes in stimulus representations, either in the form of added associations or a change of feature salience. A different way of conceptualizing acquired equivalence is in terms of strategic inference: Confronted with a choice on which it has no evidence, the organism may infer from its history of reinforcement what the best option is, and that inference is observed as acquired equivalence. To test this account, we combined an incremental learning task with an episodic memory test. Drawings of faces were made equivalent through acquired equivalence training, and then paired with words in a list learning paradigm. When participants were asked to recognize specific face-word pairings, they confused faces more often when they had been made equivalent. This suggests that prior acquired equivalence training does influence how memories are coded. We also tested whether this change in coding reflected acquisition of new associations, as suggested by the associative mediation account, or whether stimuli become more similar through a reweighting of stimulus features, as assumed by some categorization theories. Results supported the associative mediation view. We discuss similarities between this view and exemplar theories of categorization performance.     

Learning about the internal structure of categories through classification and feature inference

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a “partial inference” condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.     

Neural markers of categorization in 6-month-old infants

Little is known of the neural processes that underlie concept-formation abilities in human infants. We investigated category-learning processes in infants both by using a common behavioral measure and by recording the brain's electrical activity (event-related potentials, or ERPs). ERPs were recorded while 6-month-olds viewed cat images during training, followed by novel cat images interspersed with novel dog images during test. The data indicate that distinct neural signals correspond with learning of a category presented during familiarization, preferential responding to a novel category, and representation of category exemplars at multiple levels of inclusiveness. The results suggest that fundamental components of the neural architecture supporting object categorization are functional within the first half-year of postnatal life, before infants acquire language and young children engage in formal learning of semantic categories. The findings are discussed in terms of their implications for models of category learning and development.     

Selective attention,diffused attention,and the development of categorization

How do people learn categories and what changes with development? The current study attempts to address these questions by focusing on the role of attention in the development of categorization. In Experiment 1, participants (adults, 7-year-olds, and 4-year-olds) were trained with novel categories consisting of deterministic and probabilistic features, and their categorization and memory for features were tested. In Experiment 2, participants’ attention was directed to the deterministic feature, and in Experiment 3 it was directed to the probabilistic features. Attentional cueing affected categorization and memory in adults and 7-year-olds: these participants relied on the cued features in their categorization and exhibited better memory of cued than of non-cued features. In contrast, in 4-year-olds attentional cueing affected only categorization, but not memory: these participants exhibited equally good memory for both cued and non-cued features. Furthermore, across the experiments, 4-year-olds remembered non-cued features better than adults. These results coupled with computational simulations provide novel evidence (1) pointing to differences in category representation and mechanisms of categorization across development, (2) elucidating the role of attention in the development of categorization, and (3) suggesting an important distinction between representation and decision factors in categorization early in development. These issues are discussed with respect to theories of categorization and its development.     

交叉分类及其对刻板印象的影响

社会分类(分类)是群体认知和印象评价的依据, 交叉分类作为一种认知过程, 是指在知觉或评价他人时, 同时在两个或多个类别维度上进行社会分类, 形成在多个类别维度上内外群体身份的交叉。研究发现交叉分类既可以降低刻板印象, 也可以增强刻板印象。对此, 类别化视角的解释强调群体身份的作用, 而个体化视角的解释强调多重类别背景下去类别化的作用。现有的研究在如何理解并考察这一影响的不同方向上仍存在分歧, 同时, 也未能认识到刻板印象对于个体认知与认同的意义, 存在明显不足。今后的研究应重点探讨类别化视角的动机与认知过程, 个体化视角的优势类别与加工方式, 以及以自我归类为基础的整合。     

Are there representational shifts during category learning?

Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusively used to mentally represent categories of objects. More recently, hybrid theories of categorization have been proposed that variously combine these different forms of category representation. Our research addressed the question of whether there are representational shifts during category learning. We report a series of experiments that tracked how individual subjects generalized their acquired category knowledge to classifying new critical transfer items as a function of learning. Individual differences were observed in the generalization patterns exhibited by subjects, and those generalizations changed systematically with experience. Early in learning, subjects generalized on the basis of single diagnostic dimensions, consistent with the use of simple categorization rules. Later in learning, subjects generalized in a manner consistent with the use of similarity-based exemplar retrieval, attending to multiple stimulus dimensions. Theoretical modeling was used to formally corroborate these empirical observations by comparing fits of rule, prototype, and exemplar models to the observed categorization data. Although we provide strong evidence for shifts in the kind of information used to classify objects as a function of categorization experience, interpreting these results in terms of shifts in representational systems underlying perceptual categorization is a far thornier issue. We provide a discussion of the challenges of making claims about category representation, making reference to a wide body of literature suggesting different kinds of representational systems in perceptual categorization and related domains of human cognition.     

Errors, efficiency, and the interplay between attention and category learning

Learning to identify objects as members of categories is an essential cognitive skill and learning to deploy attention effectively is a core component of that process. The present study investigated an assumption imbedded in formal models of categorization: error is necessary for attentional learning. Eye-trackers were used to record participants’ allocation of attention to task relevant and irrelevant features while learning a complex categorization task. It was found that participants optimized their fixation patterns in the absence of both performance errors and corrective external feedback. Optimization began immediately after each category was mastered and continued for many trials. These results demonstrate that error is neither necessary nor sufficient for all forms of attentional learning.     

Similarity and categorization: from vision to touch

Even though human perceptual development relies on combining multiple modalities, most categorization studies so far have focused on the visual modality. To better understand the mechanisms underlying multisensory categorization, we analyzed visual and haptic perceptual spaces and compared them with human categorization behavior. As stimuli we used a three-dimensional object space of complex, parametrically-defined objects. First, we gathered similarity ratings for all objects and analyzed the perceptual spaces of both modalities using multidimensional scaling analysis. Next, we performed three different categorization tasks which are representative of every-day learning scenarios: in a fully unconstrained task, objects were freely categorized, in a semi-constrained task, exactly three groups had to be created, whereas in a constrained task, participants received three prototype objects and had to assign all other objects accordingly. We found that the haptic modality was on par with the visual modality both in recovering the topology of the physical space and in solving the categorization tasks. We also found that within-category similarity was consistently higher than across-category similarity for all categorization tasks and thus show how perceptual spaces based on similarity can explain visual and haptic object categorization. Our results suggest that both modalities employ similar processes in forming categories of complex objects.     

Classification errors and response times over multiple distributed sessions as a function of category structure

Learning difficulty orderings for categorical stimuli have long provided an empirical foundation for concept learning and categorization research. The conventional approach seeks to determine learning difficulty orderings in terms of mean classification accuracy. However, it is relatively rare that the stability of such orderings is tested over a period of extended learning. Further, research rarely explores dependent variables beyond classification accuracy that may also indicate relative learning difficulty, such as classification response times (RTs). Using a family of category structures defined over three binary dimensions and four positive examples that is well-known for its robust learning difficulty ordering, we report the results of two experiments that test the stability of the ordering (in terms of both errors and RTs) over multiple category learning sessions. The experimental stimuli consisted of instantiations of each of the six category structures in the family. These take the form of categories consisting of four “flasks” that vary along the binary features of size (large or small), shape (circular or triangular), and color (black or white). Experiment 1 shows that when participants are randomly presented instances of all six types, the difficulty ordering remains stable across all three sessions. This stability is present in terms of mean accuracy (errors) as well as mean RTs. In Experiment 2, participants were repeatedly exposed to category instances of a single type. In terms of errors, the ordering is revealed in the first session and disappears in later sessions. The opposite trend is observed for classification RTs: The ordering is not present in the first session but is revealed in later sessions. This suggests that even when individuals reach a relative degree of expertise in terms of reduced errors, the original degree of difficulty continues to influence processing. We interpret these results in the context of the concept learning and perceptual expertise literatures.     

The development of features in object concepts

According to one productive and influential approach to cognition, categorization, object recognition, and higher level cognitive processes operate on a set of fixed features, which are the output of lower level perceptual processes. In many situations, however, it is the higher level cognitive process being executed that influences the lower level features that are created. Rather than viewing the repertoire of features as being fixed by low-level processes, we present a theory in which people create features to subserve the representation and categorization of objects. Two types of category learning should be distinguished. Fixed space category learning occurs when new categorizations are representable with the available feature set. Flexible space category learning occurs when new categorizations cannot be represented with the features available. Whether fixed or flexible, learning depends on the featural contrasts and similarities between the new category to be represented and the individuals existing concepts. Fixed feature approaches face one of two problems with tasks that call for new features: If the fixed features are fairly high level and directly useful for categorization, then they will not be flexible enough to represent all objects that might be relevant for a new task. If the fixed features are small, subsymbolic fragments (such as pixels), then regularities at the level of the functional features required to accomplish categorizations will not be captured by these primitives. We present evidence of flexible perceptual changes arising from category learning and theoretical arguments for the importance of this flexibility. We describe conditions that promote feature creation and argue against interpreting them in terms of fixed features. Finally, we discuss the implications of functional features for object categorization, conceptual development, chunking, constructive induction, and formal models of dimensionality reduction.     

Object recognition and segmentation by a fragment-based hierarchy

How do we learn to recognize visual categories, such as dogs and cats? Somehow, the brain uses limited variable examples to extract the essential characteristics of new visual categories. Here, I describe an approach to category learning and recognition that is based on recent computational advances. In this approach, objects are represented by a hierarchy of fragments that are extracted during learning from observed examples. The fragments are class-specific features and are selected to deliver a high amount of information for categorization. The same fragments hierarchy is then used for general categorization, individual object recognition and object-parts identification. Recognition is also combined with object segmentation, using stored fragments, to provide a top-down process that delineates object boundaries in complex cluttered scenes. The approach is computationally effective and provides a possible framework for categorization, recognition and segmentation in human vision.     

Category learning with minimal prior knowledge

In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge.     

类别学习的阻碍效应与双机制理论

通过对类别学习中的阻碍效应进行系统研究,尝试性的提出了类别学习的双机制理论。三个实验分别考察：当样例特征随机呈现时;定义特征固定呈现在样例首位时;以及刺激材料为知觉图形材料时,类别学习中的阻碍效应。三个实验的研究结果都发现：在类别学习中存在一定程度的阻碍效应,支持类别学习同时存在联结学习机制和认知学习机制的双机制观点。     

A further investigation of category learning by inference

Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.