因果关系及典型性程度对类别特征推理的影响

采用学习―测试二阶段实验范式,探讨了因果关系及典型性程度对类别特征推理的影响。研究结果表明:(1)因果关系影响类别特征推理任务;(2)典型性程度影响类别特征推理任务;(3)类别特征间存在因果关系的前提下,原因特征维度值与典型性程度间存在交互作用。典型性程度对不含原因特征的项目的特征推理影响是有限的。     

类别标签与典型性程度对类别特征推理的影响

本研究采用学习-测试二阶段实验范式,探讨了类别标签及典型性程度对类别特征推理任务的共同影响,结果表明,类别标签及典型性程度都会影响类别特征推理任务,而且,类别标签及典型性程度之间存在交互作用,典型性程度为高条件下类别标签对类别特征推理任务的影响要高于典型性程度为低条件,类别标签匹配条件下,典型性程度对类别特征推理任务的影响要高于类别标签不匹配条件。     

不同类别学习方式的对比研究

55名被试被随机分配到两个组,分别通过分类学习与推理学习来学习两个类别,之后,两组被试对新的测试项目进行典型性评定,考察类别学习方式对类别表征的影响。研究结果表明,分类学习者仅仅依据项目的诊断性程度来评定,而推理学习者主要依据项目的典型性程度来评定。所以,诊断性信息在分类学习者的类别表征中占有重要位置,典型性信息在推理学习者的类别表征中占有重要位置,即分类学习与推理学习导致的类别表征不同。     

基于原型特征因果关系的归纳推理研究

两个实验探讨新特征与类别原型特征之间存在因果关系时,因果关系对归纳推理的影响.实验1探讨不同典型性条件下,新特征与原型特征间的因果关系对归纳推理的影响.实验2探讨不同多样性条件下,新特征与原型特征间的因果关系对归纳推理的影响.研究结果表明,当新特征与原型特征之间存在因果关系时,因果关系会影响归纳推理的力度,但样例典型性和多样性仍是影响人们归纳推理的重要因素.     

类别标签在类别特征推理中的极化效应

采用学习—测试二阶段实验范式,对比了类别标签与类别特征在类别特征推理中的极化效应.研究结果表明,在类别标签组,标签匹配项目上的特征推理分数显著高于标签不匹配项目上的特征推理分数;在特征标签组,标签匹配项目上的特征推理分数与标签不匹配项目上的特征推理分数之间差异不显著.类别标签组的失匹配分数显著高于特征标签组.即在类别特征推理任务中,类别标签的极化效应显著高于类别特征的极化效应,因此,类别标签与类别特征存在本质差异,类别标签在类别特征推理中起主导作用.而且,本研究还发现,高前提概率条件下的特征推理分数都显著高于低前提概率条件下的特征推理分数,所以,前提概率也影响类别特征推理任务.     

类别学习中分类学习与推理学习的对比研究

本研究对类别学习中分类学习与推理学习进行了对比.实验中要求被试从成对呈现的项目中选择一个更典型的A,并且要求被试将典型的A和典型的B画出来.测试中所呈现项目诊断性程度或典型性程度不同.研究结果表明,分类学习者主要受诊断性信息的影响,而推理学习者主要受典型性信息的影响.即,分类学习者关注类别间信息,而推理学习者关注类别内信息.     

特征相似性、类别标签与因果关系在归纳推理中的作用

采用人工材料,比较类别标签、特征相似性与因果关系对归纳推理强度的影响。包括两个实验,实验1比较类别标签与特征相似性对归纳推理的影响,结果表明,当类别标签对归纳推理的影响显著强于特征相似性时,不能将类别标签等同于一个相似性特征。实验2进一步探讨类别标签与因果关系对归纳推理的作用,结果表明,因果关系作用明显强于类别标签的作用。综合两个实验的结果并整合前人相关研究,提出了不同关系影响归纳推理的强度假想。     

类别学习和分类运用的神经机制

从类别学习和分类运用(包括非人类对象分类和社会分类)两个方面阐述了分类的神经机制。类别学习主要与新皮层、内侧颞叶、基底神经节、中脑多巴胺能系统有关, 不同类别的学习会激活这些神经系统间不同的连接。对非人类对象分类时, 不同类型、级别、熟悉度及相似度类别分类的神经机制不同, 分类对象的清晰度、类别不确定性会影响分类的神经机制, 在分类进程的不同时段会出现对应的ERP指标。社会分类时个体先注意到外群体再加工内群体, 且对内群体的加工更深, P200和N200是对内、外群体区分的特异性波, 内外群体分类时, 内群体激活梭状回和扣带回后部, 外群体激活杏仁核。文章最后比较了人类和灵长类动物分类神经机制的异同, 并指出社会分类和非人类对象分类神经机制的整合以及人类和灵长类动物分类神经机制的比较是今后研究需要关注的问题。     

类别特征相似性与竞争性的线性变化对特征推理的影响

探讨在双类别情境中,新项目与类别成员的关系对特征推理的影响.共包括两个实验,被试是62名大一年级学生.实验1与实验2分别探讨在有无类别标签的情况下,诊断特征数量的增减对新项目预测特征推理的影响,即探讨类别特征相似性与竞争性的线性变化对特征推理的影响.结果表明:无论有无类别标签,诊断特征数量的增减对新项目预测特征的推理影响是相同的,即相似性与竞争性的线性变化对特征推理的影响是相同的.     

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。     

When similarity and causality compete in category-based property generalization

Five experiments were performed to investigate the category-based generalization of nonblank properties, properties that were novel but that were attributed to existing category features with causal explanations. Experiments 1-3 tested how such explanations interact with the well-known effects of similarity on such generalizations. The results showed that when the causal explanations were used, standard effects of typicality (Experiment 1), diversity (Experiment 2), or similarity itself (Experiment 3) were almost completely eliminated. Experiments 4 and 5 demonstrated that category-based generalizations exhibit some of the standard properties of causal reasoning; for example, an effect (i.e., a novel category property) is judged to be more prevalent when its cause (i.e., an existing category feature) is also prevalent. These findings suggest that category-based property generalization is often an instance of causal inference.     

Feature-based versus category-based induction with uncertain categories

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is known. The present experiments examined the conditions that drive feature-based and category-based strategies in induction under category uncertainty. Specifically, 2 experiments investigated whether reliance on feature-based inductive strategies is a product of the lack of coherence in the categories used in previous research or is due to the use of a decision-only induction procedure. Experiment 1 found that feature-based reasoning remained the preferred strategy even when categories with relatively high internal coherence were used. Experiment 2 found a shift toward category-based reasoning when participants were trained to classify category members prior to feature induction. Together, these results suggest that an appropriate conceptual representation must be formed through experience with a category before it is likely to be used as a basis for feature induction.     

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.     

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.     

How many processes underlie category-based induction? Effects of conclusion specificity and cognitive ability

Two studies investigated participants' sensitivity to the amount and diversity of the evidence when reasoning inductively about categories. Both showed that participants are more sensitive to characteristics of the evidence for arguments with general rather than specific conclusions. Both showed an association between cognitive ability and sensitivity to these evidence characteristics, particularly when the conclusion category was general. These results suggest that a simple associative process may not be sufficient to capture somekey phenomena of category-based induction. They also support the claim that the need to generate a superordinate category is a complicating factor in category-based reasoning and that adults' tendency to generate such categories while reasoning has been overestimated.     

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.     

The downside of categories

One of the primary uses of categories is to draw inferences about novel objects based on their category membership. In a recent study, Lagnado and Shanks show that people make different inferences about an object depending on whether they first categorize the object at a general or specific level. Indeed, their inference changes even though they have been given no information about the object. This finding reveals limitations of category-based induction.     

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     

Classification versus inference learning contrasted with real-world categories

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories—what each category is like—while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.