义符促进类别归纳推理的ERP证据

为了明确义符对归纳推理影响,本研究使用有义符和无义符汉字为材料,使用ERP技术考查义符对不同层级类别概念归纳推理的影响。行为结果显示,有义符条件下反应速度更快,肯定判断率更高。脑电指标上,P3阶段义符主效应显著,有义符条件下P3波幅更大;N1、N400和P600阶段义符与层级交互作用显著,组内简单效应分析显示,层级主效应仅在无义符条件下显著;组间简单效应分析显示,义符主效在N400和P600阶段仅在下-上推理条件下显著,无义符条件下,下-上推理引发更大的N400和P600波幅。这些结果表明,义符促进了归纳推理过程,这种促进主要表现在对下-上推理的促进。     

动机与情境对不同自控水平儿童学业求助的影响

本实验以小学儿童为被试,研究了在不同动机取向和情境条件下的不同自我控制水平儿童的学业求助行为。结果表明：（１）单独无自尊压力的解难题情境比群体压力情境导致更多的求助行为。（２）低自控儿童比高自控儿童产生更多的执行性求助,但主要表现在单独无自尊压力的情境下。（３）在群体压力情境下,自我卷入的儿童比任务卷入的儿童表现出更多的求助行为。（４）在单独无自尊压力的情境下,任务卷入的儿童比自我卷入的儿童表现出     

音乐情境中自我参照加工的时间特征：来自ERPs的证据

采用事件相关电位技术, 考察在有情境条件下不同层面的自我参照加工的特性及其神经机制。实验发现,在两种音乐情境条件下个体自我参展刺激所激发的P300波幅比集体自我参照刺激和非自我刺激更大,而集体自我参照刺激所激发的P300波幅比非自我刺激更大;两种音乐情境条件下个体自我参照刺激所激发的P300波幅差异显著,而集体自我参照刺激所激发的P300波幅差异不显著。研究结果表明了在不同的情境中,个体自我参照加工的差异较大,表现出不稳定的特征;个体自我参照加工与集体自我参照加工的神经机制是不同的。这些结果还可能表明了相对于集体自我来讲,个体自我是自我的核心层面。     

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     

大学生的认知风格与逻辑推理的关系

以大学生为被试,探讨认知风格与逻辑推理的关系。结果表明：（1）在三段论推理中,场依存型学生和场独立型学生推理成绩不存在显著差异,学过和未学过逻辑学的学生推理成绩也不存在显著差异,但男生和女生推理成绩差异显著,女生比男生更易受“气氛”的影响。（2）在命题推理中,对抽象材料构成的命题,场独立型学生和场依存型学生的推理存在显著差异,场独立型学生表现出更强的证伪倾向,场依存型学生表现出更强的换位倾向;对具体材料构成的命题,场独立型学生和场依存型学生的推理不存在显著差异。     

工作记忆在归纳推理能力老化中的作用

目的：考查工作记忆系统中与目标保持密切相关的成分——工作记忆容量、转换和抑制功能在归纳推理能力老化中的作用。方法：招募年轻和老年被试共117名,接受图形推理测验、数字工作记忆广度测验、任务转换和Stroop测验。结果：工作记忆容量在归纳推理能力老化中起到显著的中介作用,而转换功能的中介作用则未达到显著。结论：增龄导致了工作记忆容量的衰退,进而造成了归纳推理能力的老化。     

自我-朋友冲突情境下智慧推理的文化差异及其机制

相对于面对自己的人际冲突, 以独立自我为主的西方人在面对朋友的人际冲突时表现更智慧(所罗门悖论)。在以互依自我为主的中国人身上所罗门悖论是否会有不同特点?研究1通过在线平台招募中美两国被试, 随机分配到自我与朋友冲突组考察其智慧推理水平, 并考察自我类型的影响。对齐法与方差分析的结果均显示美国文化下朋友组智慧推理显著高于自我组, 而中国文化下两组差异不显著, 进一步分析发现中国文化下独立自我与冲突类型的交互项显著预测智慧推理。在此基础上, 研究2~4在中国文化背景下, 通过启动不同自我类型, 考察个体在自我或朋友冲突组中的智慧推理差异, 结果显示启动独立自我的朋友组显著高于自我组, 而启动互依自我的两组差异不显著。研究表明所罗门悖论可能只存在独立自我高的人身上, 并不具有普适性。可见心理学研究除了关注样本多样性问题外, 更需关注使用单一样本却默认结论具有文化普适性的研究者。     

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)。     

卷入影响广告理性诉求信息加工效果的眼动研究

使用眼动技术,考察了卷入的两方面（个人卷入与产品卷入）共同对广告理性诉求信息加工效果的影响。研究发现：（1）随着个人卷入或产品卷入度的提高,被试获得的广告信息显著增加。（2）仅在产品卷入度高时,强说服证据理性诉求广告的品牌信息所获得的加工深度才明显优于弱说服证据者;同时,只有当个人卷入度高时,产品卷入度高的商品信息所获得的加工才明显比产品卷入度低者更为精细。这说明,广告理性诉求方式可能主要适宜于宣传产品卷入度高的商品,且只有当个人卷入度高时才可能更为有效     

正性情绪诱导下的自我参照加工：来自ERPs的证据

采用事件相关电位考察正性情绪对自我参照加工程度效应的影响。实验采用图片启动范式, 先呈现情绪图片, 然后再呈现自我参照刺激。实验发现, 在P2上, 中性情绪条件比正性情绪条件激发了更大的波幅, 高自我相关的刺激比其它刺激诱发了更短的P2潜伏期; 在N2上, 高自我相关名字比中等自我相关名字和非自我相关名字诱发了更小的N2的波幅和更长潜伏期; 在P3上, 高自我相关名字比中等自我相关名字和非自我相关名字诱发了更大的P3波幅, 中等自我相关名字比非自我相关名字诱发了更大的P3波幅。实验结果表明, 人类大脑对正性情绪刺激的加工可能是不敏感的。无论在正性情绪启动还是中性启动的影响下, 自我参照加工都能展现出稳定的特征, 而且高自我相关的刺激会得到更为深入和精细的加工, 表现出自我参照加工的程度效应。     

主题关系在4~5岁儿童不同属性归纳推理发展中的作用

为探讨主题关系在儿童归纳推理中的作用,采用经典的三角归纳范式,设计了主题关系和知觉相似冲突、主题关系和分类学关系冲突两种情形,来考察4~5岁儿童在内在属性和情境性属性任务中的归纳推理。结果显示：当主题关系和知觉相似冲突时,从4.5岁开始儿童主要基于主题关系对情境性属性进行归纳推理,而对于内在属性的推论,4~5岁儿童在基于主题关系和基于知觉相似上没有显著差异。当主题关系和分类学关系冲突时,从4.5岁开始儿童主要基于主题关系对情境性属性进行归纳推理,从5岁开始儿童主要基于分类学关系对内在属性进行归纳推理。两个实验结果均发现5岁和5.5岁儿童能够依据不同的属性使用不同的关系推理,表现出归纳灵活性     

Knowledge, expectations, and inductive reasoning within conceptual hierarchies

Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the “basic” level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hierarchical levels, expectations about conceptual coherence, and inductive inference. In Experiments 1 and 2, 5- and 8-year-olds and adults listed features of living kind (Experiments 1 and 2) and artifact (Experiment 2) concepts at different hierarchical levels (e.g. plant, tree, oak, desert oak), and also rated the strength of generalizations to the same concepts. For living kinds, the level that showed a relative advantage on these two tasks differed; the greatest increase in features listed tended to occur at the life-form level (e.g. tree), whereas the greatest increase in inductive strength tended to occur at the folk-generic level (e.g. oak). Knowledge and induction also showed different developmental trajectories. For artifact concepts, the levels at which the greatest gains in knowledge and induction occurred were more varied, and corresponded more closely across tasks. In Experiment 3, adults reported beliefs about within-category similarity for concepts at different levels of animal, plant and artifact hierarchies, and rated inductive strength as before. For living kind concepts, expectations about category coherence predicted patterns of inductions; knowledge did not. For artifact concepts, both knowledge and expectations predicted patterns of induction. Results suggest that beliefs about conceptual coherence play an important role in guiding inductive inference, that this role may be largely independent of specific knowledge of concepts, and that such beliefs are especially important in reasoning about living kinds.     

Deductive and inductive conditional inferences: Two modes of reasoning

A number of single- and dual-process theories provide competing explanations as to how reasoners evaluate conditional arguments. Some of these theories are typically linked to different instructions—namely deductive and inductive instructions. To assess whether responses under both instructions can be explained by a single process, or if they reflect two modes of conditional reasoning, we re-analysed four experiments that used both deductive and inductive instructions for conditional inference tasks. Our re-analysis provided evidence consistent with a single process. In two new experiments we established a double dissociation of deductive and inductive instructions when validity and plausibility of conditional problems were pitted against each other. This indicates that at least two processes contribute to conditional reasoning. We conclude that single-process theories of conditional reasoning cannot explain the observed results. Theories that postulate at least two processes are needed to account for our findings.     

Cognitive and AI models of reasoning

Although relatively few in number, cognitive neuroscience studies of reasoning have two general implications for cognitive theories of deduction. First, an important distinction among these theories is whether they focus on the effect of personally relevant content on the processes and representations underlying deductive reasoning. Evidence is reviewed indicating that there is a neuroanatomical basis for both content-independent and content-dependent theories of deduction. Clinical and neuroimaging studies appear to show that content-independent reasoning is mediated by the left hemisphere, whereas content-dependent reasoning is mediated by regions in the right hemisphere and the bilateral ventromedial frontal cortex. In normal subjects, reasoning is likely to be based on contributions from both hemispheres. Second, clinical evidence indicates that the visuospatial processes used in deductive reasoning are mediated by the posterior areas of the left hemisphere, and that verbal and visuospatial reasoning representations overlap at the neuroanatomical level. This finding weighs against the claims of mental-model theory that deduction involves a significant nonverbal component. Further investigation, particularly with contemporary neuroimaging methods, is needed to test these preliminary conclusions.     

类比归纳的相似性效应和概念效应比较

该文用两个实验比较了类比归纳中的相似性效应和概念效应。预备实验首先检验了实验所使用的人工概念符号的效度。结果显示,研究采用的人工符号能够按照实验要求准确控制被试对概念关系和相似性的估计。预备实验还显示,在只有相似性或概念信息的条件下,人们更加信任概念信息。在正式实验中,相似性信息和概念信息同时呈现,结果显示,在概念信息明确的时候,人会忽略相似性信息;但在相似性信息和明确的时候,人不会忽略概念信息,因此,文章得出结论：概念是类比归纳的基本构成要素。     

Relational integration in older adults

Reasoning requires making inferences based on information gleaned from a set of relations. The relational complexity of a problem increases with the number of relations that must be considered simultaneously to make a correct inference. Previous work (Viskontas, Morrison, Holyoak, Hummel, & Knowlton, ) has shown that older adults have difficulty integrating multiple relations during analogical reasoning, especially when required to inhibit irrelevant information. We report two experiments that examined the ability to integrate multiple relations in younger, middle-aged, and older adults performing two other reasoning tasks. These tasks systematically varied relational complexity, and required either inductive reasoning (a version of the Raven's Matrices Task) or transitive inference. Our results show that as people age they have increasing difficulty in solving problems that require them to integrate multiple relations. This difficulty may stem from a decrease in working memory capacity.     

A Logic For Inductive Probabilistic Reasoning

Inductive probabilistic reasoning is understood as the application of inference patterns that use statistical background information to assign (subjective) probabilities to single events. The simplest such inference pattern is direct inference: from “70% of As are Bs” and “a is an A” infer that a is a B with probability 0.7. Direct inference is generalized by Jeffrey’s rule and the principle of cross-entropy minimization. To adequately formalize inductive probabilistic reasoning is an interesting topic for artificial intelligence, as an autonomous system acting in a complex environment may have to base its actions on a probabilistic model of its environment, and the probabilities needed to form this model can often be obtained by combining statistical background information with particular observations made, i.e., by inductive probabilistic reasoning. In this paper a formal framework for inductive probabilistic reasoning is developed: syntactically it consists of an extension of the language of first-order predicate logic that allows to express statements about both statistical and subjective probabilities. Semantics for this representation language are developed that give rise to two distinct entailment relations: a relation ⊨ that models strict, probabilistically valid, inferences, and a relation that models inductive probabilistic inferences. The inductive entailment relation is obtained by implementing cross-entropy minimization in a preferred model semantics. A main objective of our approach is to ensure that for both entailment relations complete proof systems exist. This is achieved by allowing probability distributions in our semantic models that use non-standard probability values. A number of results are presented that show that in several important aspects the resulting logic behaves just like a logic based on real-valued probabilities alone.     

Re-framing the characteristics of concepts and their relation to learning and cognition in artificial agents

In this work, the problems of knowledge acquisition and information processing are explored in relation to the definitions of concepts and conceptual processing, and their implications for artificial agents.The discussion focuses on views of cognition as a dynamic property in which the world is actively represented in grounded mental states which only have meaning in the action context. Reasoning is understood as an emerging property consequence of actions-environment couplings achieved through experience, and concepts as situated and dynamic phenomena enabling behaviours.Re-framing the characteristics of concepts is considered crucial to overcoming settled beliefs and reinterpreting new understandings in artificial systems.The first part presents a review of concepts from cognitive sciences. Support is found for views on grounded and embodied cognition, describing concepts as dynamic, flexible, context-dependent, and distributedly coded.That is argued to contrast with many technical implementations assuming concepts as categories, whilst explains limitations when grounding amodal symbols, or in unifying learning, perception and reasoning.The characteristics of concepts are linked to methods of active inference, self-organization, and deep learning to address challenges posed and to reinterpret emerging techniques.In a second part, an architecture based on deep generative models is presented to illustrate arguments elaborated. It is evaluated in a navigation task, showing that sufficient representations are created regarding situated behaviours with no semantics imposed on data. Moreover, adequate behaviours are achieved through a dynamic integration of perception and action in a single representational domain and process.     

The role of relational reasoning in children's addition concepts

The study addresses the relational reasoning of different‐aged children and how addition reasoning is related to problem‐solving skills within addition and to reasoning skills outside addition. Ninety‐two 5‐ to 8‐year‐olds were asked to solve a series of conceptually related and unrelated addition problems, and the speed and accuracy of all self‐reported strategies were used to monitor their addition performance. Children were also given a series of general relational reasoning tasks to assess their ability to solve problems based on thematic, causal and visual relations. The results revealed that, while children were able to reason about commutativity relations, recognition of relations based on additive composition was rare. Furthermore, children's ability to reason about addition concepts increased with age and problem‐solving proficiency. Reasoning about addition concepts was related to performance on the thematic, causal and visual reasoning tasks for older children but not for younger children. Overall, the findings suggest that while children's early knowledge of addition relations is domain specific, as children develop in their broader reasoning abilities these developments enhance their addition reasoning.