Children's category‐based inferences affect classification

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category‐based inferences during category learning influence category knowledge and thereby affect later classifications for 5‐ to 7‐year‐olds. The children learned to classify pictures of new types of creatures on the basis of a salient feature (colour) and then answered a question that required them to make an inference on the basis of other features. At test, children classified pictures that included only some features (without colour). Experiment 1 showed that the features relevant to the inference during learning led to better classification than did features irrelevant to the inference. Experiment 2 replicated this finding even when the relevant features were physically close to the irrelevant features. Experiments 3 and 4 found this effect even when the classification was learned prior to the inference task and even when no mention was made of the categories during inference learning. Taken together, these results show that making inferences during category learning can influence category knowledge and suggest a need to integrate the work on category learning and category‐based inferences.     

阻碍效应范式中主题知识对类别学习的影响

三个实验采用阻碍效应研究范式探讨主题知识对类别学习的影响。实验1探讨定义特征维度为机械特征时,主题知识对类别学习的影响;实验2和实验3,通过考察定义特征维度为知识特征时,类别学习中阻碍效应的大小,继续探讨主题知识对类别学习的影响。实验结果发现：（1）当定义特征维度为机械特征时,主题知识的存在没有促进类别学习。这可能因为定义特征维度为机械特征,主题知识难以发现所致;（2）当定义特征维度为知识特征时,类别学习中的阻碍效应消失,证明主题知识的存在促进了类别学习。研究结果再次支持了类别学习不仅仅受到减少归类错误驱动的观点。     

两种学习模式下类别学习的结果：原型和样例

利用“学习－迁移”的任务范式和单一特征类别判断技术,探讨了分类和推理两种类别学习模式的结果,比较了两种学习模式的效果和策略。研究表明：两种学习模式产生了不同的结果,分类学习的结果是样例,推理学习的结果是原型;在学习效果方面,分类学习比推理学习在达标比例上更高,但在进度上差异不显著;在策略运用方面,分类学习比推理学习更快地使用单维度策略,而在高水平策略的运用上,两者差异不显著     

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。     

Overconfidence effects in category learning: a comparison of connectionist and exemplar memory models

Exemplar and connectionist models were compared on their ability to predict overconfidence effects in category learning data. In the standard task, participants learned to classify hypothetical patients with particular symptom patterns into disease categories and reported confidence judgments in the form of probabilities. The connectionist model asserts that classifications and confidence are based on the strength of learned associations between symptoms and diseases. The exemplar retrieval model (ERM) proposes that people learn by storing examples and that their judgments are often based on the first example they happen to retrieve. Experiments 1 and 2 established that overconfidence increases when the classification step of the process is bypassed. Experiments 2 and 3 showed that a direct instruction to retrieve many exemplars reduces overconfidence. Only the ERM predicted the major qualitative phenomena exhibited in these experiments.     

学习条件和样例相似性对类别学习元认知监控的影响

采用“学习-迁移”范式 ,探讨了学习条件和样例相似性对类别学习元认知监控的影响。实验选取虚构动物材料,采用2（学习条件：规则、无规则）×3（样例相似性：低、中、高）×2（匹配类型：正向匹配、反向匹配）混合实验设计,结果显示,在规则条件下,高样例相似性组正向匹配新项目的分类正确率显著高于反向匹配新项目的分类正确率;在无规则条件下,样例相似性越高,正向匹配新项目的分类准确率越高,所有项目的信心值也越高。这表明,规则和样例相似性是类别学习元认知判断的线索;在同一任务中,分类会涉及基于规则和基于相似性两个过程。     

Learning categories by making predictions: An investigation of indirect category learning

Categories are learned in many ways, but studies of category learning have generally focused on classification learning. This focus may limit the understanding of categorization processes. Two experiments were conducted in which participants learned categories of animals by predicting how much food each animal would eat. We refer to this as indirect category learning, because the task andthe feedback were not directly related to category membership, yet category learning was necessary for good performance in the task. In the first experiment, we compared the performance of participants who learned the categories indirectly with the performance of participants who first learned to classify the objects. In the second experiment, we replicated the basic findings and examined attention to different features during the learning task. In both experiments, participants who learned in the prediction-only condition displayed a broader distribution of attention than participants who learned in the classification-and-prediction condition did. Some participants in the prediction-only group learned the family resemblance structure of the categories, even when a perfect criterial attribute was present. In contrast, participants who first learned to classify the objects tended to learn the criterial attribute.     

Paralinguistic correlates of conceptual structure

How is conceptual knowledge transmitted during conversation? When a speaker refers to an object, the name that the speaker chooses conveys information about categoryidentity. In addition, I propose that a speaker’s confidence in a classification can convey information about categorystructure. Because atypical instances of a category are more difficult to classify than typical instances, when speakers refer to these instances their lack of confidence will manifest itself “paralinguistically”—that is, in the form of hesitations, filled pauses, or rising prosody. These features can help listeners learn by enabling them to differentiate good from bad examples of a category. So that this hypothesis could be evaluated, in a category learning experiment participants learned a set of novel colors from a speaker. When the speaker’s paralinguistically expressed confidence was consistent with the underlying category structure, learners acquired the categories more rapidly and showed better category differentiation from the earliest moments of learning. These findings have important implications for theories of conversational coordination and language learning.     

Inference and classification learning of abstract coherent categories

Category learning research has primarily focused on how people learn to classify items using simple observable features. However, classification is only 1 way to learn categories. In addition, many concepts have an underlying coherence that explains the featural similarity among exemplars, such as abstract coherent concepts whose instances differ greatly on their observable features. In 3 experiments, the authors investigated how abstract coherent categories are acquired through 2 common means of category learning, classification and inference. Because inference promotes more focus on within-category information than does classification, they hypothesized that inference learning would lead to a better understanding of the underlying coherence of abstract coherent categories. All 3 experiments support this prediction.     

Knowledge-based classification of ill-defined categories

In this paper, I report an exploratory study which investigated the role that prior knowledge plays in influencing classification learning. Under neutral or knowledge-imposing instructions, subjects learned to classify exemplars into categories that either were or were not linearly separable. Linearly separable categories are those categories whose members can be correctly classified based on an additive summation of weighted attribute information. Following category learning, the subjects were given transfer tests. A major finding was that knowledge facilitated the learning of linearly separable categories but interfered with the learning of not linearly separable categories. Quantitative analyses revealed that the knowledge facilitated category learning of the linearly separable categories by influencing the storage and reliance on both prototypical and exemplar information.     

Problem structure and the use of base-rate information from experience

This article is concerned with the use of base-rate information that is derived from experience in classifying examples of a category. The basic task involved simulated medical decision making in which participants learned to diagnose hypothetical diseases on the basis of symptom information. Alternative diseases differed in their relative frequency or base rates of occurrence. In five experiments initial learning was followed by a series of transfer tests designed to index the use of base-rate information. On these tests, patterns of symptoms were presented that suggested more than one disease and were therefore ambiguous. The alternative or candidate diseases on such tests could differ in their relative frequency of occurrence during learning. For example, a symptom might be presented that had appeared with both a relatively common and a relatively rare disease. If participants are using base-rate information appropriately (according to Bayes' theorem), then they should be more likely to predict that the common disease is present than that the rare disease is present on such ambiguous tests. Current classification models differ in their predictions concerning the use of base-rate information. For example, most prototype models imply an insensitivity to base-rate information, whereas many exemplar-based classification models predict appropriate use of base-rate information. The results reveal a consistent but complex pattern. Depending on the category structure and the nature of the ambiguous tests, participants use base-rate information appropriately, ignore base-rate information, or use base-rate information inappropriately (predict that the rare disease is more likely to be present). To our knowledge, no current categorization model predicts this pattern of results. To account for these results, a new model is described incorporating the ideas of property or symptom competition and context-sensitive retrieval.     

Learning nonlinearly separable categories by inference and classification

Previous research suggests that learning categories by classifying new instances highlights information that is useful for discriminating between categories. In contrast, learning categories by making predictive inferences focuses learners on an abstract summary of each category (e.g., the prototype). To test this characterization of classification and inference learning further, the authors evaluated the two learning procedures with nonlinearly separable categories. In contrast to previous research involving cohesive, linearly separable categories, the authors found that it is more difficult to learn nonlinearly separable categories by making inferences about features than it is to learn them by classifying instances. This finding reflects that the prototype of a nonlinearly separable category does not provide a good summary of the category members. The results from this study suggest that having a cohesive category structure is more important for inference than it is for classification.     

Development of classification: The use of similarity and dimensional relations

Findings about perceptual development indicate that overall similarity is the primary perceptual relation by which young children compare complex objects. Traditional studies of classification, however, did not focus on children's organizational use of holistic relations but rather on their ability to classify by dimensions or criterial attributes. The results from such traditional studies suggest that young children are deficient classifiers. The present research investigated the possibility, contrary to the traditional view, that 4- to 6-year-old children are competent and systematic classifiers at least by overall similarity. In three experiments, preschoolers and kindergarteners classified various sets of multidimensional stimuli that could be organized into categories by overall similarity or by dimensional attributes. Consistent with the research in perceptual development, the children were highly attentive to overall similarity. However, the preschoolers in particular showed marked difficulty in using this relation to form categories of more than two objects. The children's difficulties were highly reminiscent of traditional claims about early classification. Analyses of the classification strategies used by the children, however, suggest that even the youngest children understood the purpose of a classification. The developmentla changes appear to be in the ability to execute a classification. Importantly, type of classification strategy was independent of type of category organization. Individual children used the same strategies both when classifying by overall similarity and by dimensional attributes. These results strongly suggest that it is the classification skills themselves, and not just the ability to classify by particular relations, that change with age.     

Working memory supports inference learning just like classification learning

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.     

What is learned in knowledge-related categories? Evidence from typicality and feature frequency judgments

When a category’s features are tied together by integrative knowledge, subjects learn the category faster than when the features are not directly related. What do subjects learn about the category in such circumstances? Some research has suggested that the subjects can use the knowledge itself in performing the category learning task and, thus, do not learn the details of the category’s features. Two experiments investigated this hypothesis by collecting feature frequency estimates after category learning. The results showed that integrative knowledge about a category did not decrease subjects’ sensitivity to feature frequency—if anything, knowledge improved it. A third experiment found that integrative knowledge did reduce sensitivity to feature frequency in typicality ratings. The results suggest that knowledge does not inhibit the learning of detailed category information, though it may replace its use in some tasks.     

特征概率影响多维和少维类别的分类学习和特征学习

探讨了特征概率对多维和少维类别的分类学习和特征学习的效果及策略的影响。结果表明高特征概率条件下,多维比少维类别的分类学习更容易,而且学到更多的特征知识,多维条件下人们更倾向于整体性加工策略,而少维条件下人们倾向于分析性加工策略。低特征概率条件下,多维比少维类别的分类学习和特征学习都困难,且两种条件下人们都倾向于采取分析性加工策略。     

Executive attention and task switching in category learning: Evidence for stimulus-dependent representation

One class of multiple-system models of category learning posits that within a single category-learning task people can learn to utilize different systems with different category representations to classify different stimuli. This is referred to as stimulus-dependent representation (SDR). The use of SDR implies that learners switch from subtask to subtask as trials demand. Thus, the use of SDR can be assessed via slowed response times, following a representation switch. Additionally, the use of SDR requires control of executive attention to keep inactive representations from interfering with the current response. Subjects were given a category learning task composed of one- and two-dimensional substructures. Control of executive attention was measured using a working memory capacity (WMC) task. Subjects most likely to be using SDR showed greater slowing of responses following a substructure switch and a greater correlation between learning performance and WMC. These results provide support for the principle of SDR in category learning and the reliance of SDR on executive attention.     

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.     

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.