归纳推理中的属性中心性效应

通过三个实验研究了属性的中心性程度对归纳推理的影响,结果表明,在归纳推理中存在属性中心性效应,中心属性比其他属性（次中心属性、再次中心属性）更能影响归纳推理。但属性中心性效应的出现受到了前提对象和结论对象相似性程度的制约,当前提对象和结论对象的相似程度高时,中心属性比其他属性更能影响归纳推理;随着相似性水平的降低,中心属性的归纳推理力度越来越弱,而次中心属性、再次中心属性的归纳推理力度则会依次表现出越来越强的趋势,到最后当前提对象和结论对象的属性完全不相似时,最次中心属性的归纳推理力度达到最高水平,而中心属性的归纳推理力度则降低到最低水平。由于再次中心属性的归纳推理力度的最高水平没有显著高于随机水平,而中心属性的归纳推理力度的最低水平却显著低于随机水平,因此,研究者认为,在前提对象和结论对象完全不相似的情况下,被试将确信不能采用中心属性来进行归纳推理的力度判断,而采用猜测的方式选择了肯定可以排除的项目之外的其他项目,这体现了具有不确定的归纳推理活动的过程的典型特征     

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.     

类别型状态不确定独立否定句的加工机制：来自眼动实验的证据

探讨类别型状态不确定独立否定句(如“裙子不是蓝色的”)的动态表征过程。实验1和实验2分别仅给被试提供“与事件备择选项一致的图片” (红色裙子)和“含有否定标签的与事件否定状态一致的图片” (含有否定标签的蓝色裙子); 实验3同时提供以上两种选项。结果发现：3个实验中被试在早期均对“与事件否定状态一致的图片”注视概率更高, 在晚期阶段, 实验1仅对“与事件备择选项一致的图片” 注视概率更高, 实验2和实验3均仅对“含有否定标签的与事件否定状态一致的图片”注视概率更高, 并且选择为事件的最终状态; 实验4的评定结果表明, 当被试有充裕时间完成任务时, 37名评定者中有30名选择“与事件备择选项一致的图片”为事件的最终状态。4个实验的结果为锚激活与限制满足模型和抑制假设提供了进一步证据。     

P3b反映基于相似性的图形类别归纳决策过程(309)

先前关于归纳推理的研究没有探讨过图形类别归纳任务中决策过程的事件相关电位(ERPs)特征。该研究采用图形材料,通过记录和分析图形类别归纳推理任务的结论评估阶段的脑电信号,探讨了不同类别条件下图形归纳推理决策过程的ERP特征。实验结果显示,推理决策过程中,当前提为一些狗具有某种属性特征时,结论一致类别(狗类及马类)比结论不一致类别下(椅子类)的归纳推理诱发了更大的P3b的波幅; 而结论一致类别下,类别内(狗类)比类别外(马类)推理诱发更大的后部脑区的P3b的波幅,反映了相似性对类别归纳的贡献。该研究为理解类别归纳过程的决策阶段的动态加工过程提供了神经电生理学证据。研究结果进一步支持了类别归纳的相似性理论模型。     

类别学习语言标签效应的眼动研究

采用眼动技术,通过两个实验探讨人工语言标签与真实语言标签在类别学习中的作用。实验一对33名大学本科被试施测,探讨了人工语言标签对类别学习的影响。结果发现,在人工语言标签下,个体会基于标签的相似性进行分类。实验二对34名大学本科被试施测,探讨了真实语言标签对类别学习的影响。结果发现,在真实语言标签下,个体会基于标签的类别知识进行分类。类别学习中语言标签效应的熟悉程度影响分类方式,不熟悉的类别语言标签基于相似性分类,熟悉的类别语言标签基于知识分类;基于类别知识分类准确性更高,速度更快。     

知识对成人归纳推理标签效应的影响

通过对标签所承载的表层类属知识和深层概念知识进行区分,考察了知识对成人归纳推理中标签效应的影响。实验材料为自然标签和人工标签,任务为亲缘关系判断,结果发现在两种材料中都能观察到标签效应。在自然类别中,当标签承载深层知识时,标签承载的表层类属知识的作用会受其影响而减小;在人工类别中,标签作用的发挥会受到标签自身特征的影响,当标签中含有类别信息的汉字时,标签对归纳推理的促进作用更大。     

归纳推理多样性效应的发展及其争论

归纳推理多样性效应指的是在归纳论断中,前提项目数量越多、越多样,将能构成归纳力度越强的推理。已有研究表明成人的归纳推理明显受到前提项目多样性的影响,而幼儿的相关研究却存在着两种对立的观点：一是认为幼儿不能基于多样性进行推理,二是认为5岁左右的幼儿就能够基于多样性进行推理。出现此争论的原因可能与研究方法方面的差异有关,如实验范式与前提组间差异、实验材料类别与概念范畴、属性特征及呈现方式、推理形式;此外,儿童的知识经验也是一个影响因素。未来研究可以结合相关领域的研究范式;深入挖掘影响儿童表现出多样性效应的因素;将任务设计多样化;将实验任务的计分方式多样化;开展相关的应用研究。     

基于概率的归纳推理模型

归纳推理是指从个别前提推出一般结论的推理,它是人的一项基本认知能力,也是心理学研究需要解释的重要现象.在模拟归纳推理时,不能排除先验知识对归纳推理结论力度的影响,贝叶斯理论可以结合条件对先验概率进行修正,能够很好地将先验知识引入到归纳推理中来.文章重点介绍了基于记忆的贝叶斯模型,相似性概率模型和结构统计模型对归纳推理的解释.     

前提项目间差异对儿童归纳推理多样性效应的影响

通过两个实验深入探讨了前提项目间差异对儿童归纳推理多样性效应的表现水平的影响。结果发现:(1)多样性效应受多样组和非多样组之间差异(前提项目间差异)大小的影响,在减小两组差异的前提下,5～6岁儿童尚未表现出多样性效应;除个别任务外,8～9岁儿童表现出多样性效应(实验1)。(2)对前提图片的形状差异进行数量化处理后发现,儿童在归纳推理多样性效应任务上的归纳判断力度随前提项目间差异的增大而增强;在前提项目间差异很小的情况下,10～12岁儿童也没有运用多样性信息进行推理;当前提项目间差异很大时,被试表现出多样性效应(实验2)。     

对归纳推理非对称性现象的特征迁移解释

作者创新提出对归纳推理非对称性现象的特征迁移解释,认为根据由前提类别已知的特征集合构成的特征样本中迁移出现在结论类别中的特征的比例,能预测作为新特征的归纳特征由前提类别迁移到结论类别的可能性。以大学生为被试的实验结果支持对非对称性现象的特征迁移解释而不是原来的两种相似性解释。     

归纳推理的抽样理论

作者提出归纳推理的抽样理论,认为归纳推理实质是根据与归纳特征有关的抽样样本情况来推断结论类别具有归纳特征的可能性,其中的抽样可分为类别抽样和特征抽样两种。以大学生为被试的两个实验结果一致支持抽样理论而不是别的归纳推理理论。对于归纳推理的主要现象,抽样理论比其它的相似性解释具有更大的解释范围,特别是能够解释其它理论严格不能解释的归纳推理非对称性现象     

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.     

Leaping to Conclusions: Why Premise Relevance Affects Argument Strength

Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non‐monotonicity, in which adding premises to a category‐based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category‐based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.     

The role of similarity in deductive categorical inference

The category inclusion rule specifies that categories inherit the properties of their superordinates. For example, given thatall metals are pentavalent, it can be concluded thatall iron is pentavalent. Sloman (1998) showed that people do not fully endorse conclusions that follow from the category inclusion rule. He claims that people rely on the similarity between the premise and the conclusion categories (metals andiron), rather than applying the category inclusion rule. By allowing reasoners to rate their certainty for category relations (e.g.,iron is metal), as well as for conclusions, the present study shows that similarity has only an indirect effect on the certainty of conclusions: Reasoners are more certain that similar categories have a category inclusion relation, and this in turn affects the certainty of conclusions based on this relation.     

归纳推理的多样性效应及其机制探索

归纳推理结论范围大于前提范围的这一特点决定了归纳结论的不确定性,寻求前提的多样化是增强结论可靠度的重要途径。已有研究通过寻找证据、力度判断、特征扩展与归宿选择等研究方法发现前提类别覆盖范围、前提概率原则可以较好地解释多样性的内在机制。事实上,多样性效应的实质是大数原则在归纳推理中的运用,其产生的心理过程包括差异识别、大数信息抽取与大数信息运用三个步骤     

概念及概念结构研究新进展

近来有关归类和概念结构的研究与理论出现了两个方面的转变。第一个方面是从概念定义属性的假设 (经典观 )到概念表征属性的观点 (概率观 ) ;第二个方面是从概念以相似性组织的观点到概念围绕理论而组织的观点。本文综合概述了基于相似性和理论驱动的归类理论。     

Dissimilarity is used as evidence of category membership in multidimensional perceptual categorization: A test of the similarity–dissimilarity generalized context model

In exemplar models of categorization, the similarity between an exemplar and category members constitutes evidence that the exemplar belongs to the category. We test the possibility that the dissimilarity to members of competing categories also contributes to this evidence. Data were collected from two 2-dimensional perceptual categorization experiments, one with lines varying in orientation and length and the other with coloured patches varying in saturation and brightness. Model fits of the similarity–dissimilarity generalized context model were used to compare a model where only similarity was used with a model where both similarity and dissimilarity were used. For the majority of participants the similarity–dissimilarity model provided both a significantly better fit and better generalization, suggesting that people do also use dissimilarity as evidence.