因果模型在类比推理中的作用

通过操纵因果模型的特征维度及推理方向, 探讨因果模型在类比推理中的作用。实验一探讨了当结果特征未知时进行类比推理的情况, 发现在一果多因时, 被试采用因果模型进行类比推理, 而在一因多果时, 被试同时采用因果模型和计算模型进行类比推理。实验二探讨当原因特征未知时进行类比推理的情况, 发现在一果多因和一因多果时, 被试均采用因果模型进行类比推理。结果表明：(1)当结果特征未知时, 人们会建构因果模型进行类比推理。且当因果模型和计算模型处于冲突情境时, 人们会采用因果模型进行类比推理; 但当因果模型和计算模型处于非冲突情境时, 人们会同时采用因果模型和计算模型。(2)当原因特征未知时, 即按照因果模型推理的难度增加时, 人们仍会建构因果模型进行类比推理。     

高职生因果定向与学业表现的关系

为了考察高职生因果定向的特点及因果定向与学生学业表现(学业成绩和专业技能)的关系。该研究以修订的一般因果定向量表为工具,在济南和淄博的三所高职抽取505名学生作为被试进行调查。结果发现:1)修订后的一般因果定向量表具有良好的信效度;2)高职生在因果定向的三个维度上得分差异显著,由高到低依次为自主定向、控制定向和非个人定向;3)自主定向显著正向预测学业表现,控制定向显著负向预测学业表现,而非个人定向对学业表现的预测作用不显著。我国高职生与国外被试的因果定向特点基本一致,说明因果定向具有一定的文化普适性。同时,不同因果定向对学业表现具有不同的预测作用,教育者有必要对学生进行因果定向的教育和引导。     

其它可能原因对于因果共变信息作用的影响

研究使用相继呈现信息的方法控制了被试获得信息的顺序,将其它可能原因在因果判断过程中所起到的作用分离了出来,并按照其对不同共变信息的影响进行了分析。研究结果表明：(1)其它可能原因会影响到被试利用因果共变信息而进行的因果推断。(2)其它可能原因与待判断原因共同存在的程度在很大程度上影响着其它可能原因所起到的作用。(3)其它可能原因对于不同共变信息的影响有着明显的差别。     

因果信息在贝叶斯推理中的作用:问题情境的调节

以大学生为被试,考察因果信息在不同贝叶斯推理问题中的作用。结果表明:(1)问题情境的主效应显著(F(1,122)=16.33,p0.05,η~2=0.120),因果信息的主效应不显著((F(1,122)=1.161,p=0.283,η~2=0.01),但问题类型和因果信息的交互作用显著(F(1,122)=4.757,p0.05,η~2=0.038)。(2)不同类型的问题上因果信息的作用是不同的。具体表现为,在乳癌问题中,增加了因果信息,被试的正确率明显提高(χ~2(1)=6.759,p0.05,V=0.607),而在中彩问题中没有因果信息的推理成绩反而更好(χ~2(1)=1.864,p=0.221,V=0.167)。     

因果条件中儿童社会信息推理的发展

采用因果条件性逻辑推理研究的一般模式,研究了儿童社会信息因果推理发展状况。实验结果表明：(1)儿童社会信息推理能力呈年级性递增。(2)社会信息的内容会影响儿童社会信息推理的能力。(3)儿童四种逻辑形式推理能力之间的差异显著．AC、DA在年级水平上差异都十分显著,但MP和MT在年级水平上差异不显著,儿童在各个年级四种推理能力之间的差异都显著,确定型和非确定型逻辑推理能力之间的差异显著。(4)逻辑形式的难度、儿童的年龄影响儿童合理化解释的类型。     

因果信息和提示指导语对大学生贝叶斯推理的影响

基于三重加工心智模型,以大学生为被试,采用经典贝叶斯推理的文本范式,通过操纵自变量：因果信息（有或无）与提示指导语（提供或不提供）,试图探讨激发反省心智,消解理性障碍的情况下,因果贝叶斯框架的作用机制。估计正确率和准确性的结果都表明因果信息显著提高了贝叶斯推理成绩,准确性的结果也说明指导语可以提示被试放下既有观念,以无偏见的方式进行推理,从而有效促进了贝叶斯推理表现。而在提示条件下增加因果信息并没有促进作用,表明对于较高元认知的被试因果信息作用是有限的。     

因果力比较范式下对效力PC理论的检验

在用图形方式集中呈现信息的条件下,用因果力大小比较的实验范式检验效力PC理论。233名大学生被试对不同化学药物影响动物基因变异的能力做大小比较判断。结果发现,对单一因果关系因果力大小的比较判断具有以下3个特点：（1）不对称性：在预防原因条件下的因果力判断一般符合效力PC理论,而在产生原因条件下的因果力判断一般不符合效力PC理论;（2）在同时变化协变值DP和结果基率P(E|~C)的产生原因条件下,多数被试使用DP规则。这与概率对比模型的预测相一致,而不支持效力PC理论;（3）在固定协变值DP而只变化结果基率P(E|~C)的产生原因条件下,多数被试使用变异比RP规则。这是目前所有的因果推理理论都不能解释的现象。     

观察因果学习和自主探索对学前儿童因果推理的影响

本研究采取单因素完全随机实验设计,以94名学前末期儿童(66-74月龄)为被试,在控制证据顺序的条件下探究观察因果学习结果和自主探索结果对儿童因果推理的影响,结果发现:(1)在只获得观察学习结果或自主探索结果一种证据条件下,绝大多数儿童依据所获证据推断因果关系;(2)在获得观察学习结果和自主探索结果两种证据条件下,儿童能综合两类证据推断因果关系,其中自主探索结果对儿童因果推理的影响力大于观察学习结果。     

因果信息和提示指导语对大学生贝叶斯推理的影响

基于三重加工心智模型，以大学生为被试，采用经典贝叶斯推理的文本范式，通过操纵自变量：因果信息（有或无）与提示指导语（提供或不提供），试图探讨激发反省心智，消解理性障碍的情况下，因果贝叶斯框架的作用机制。估计正确率和准确性的结果都表明因果信息显著提高了贝叶斯推理成绩，准确性的结果也说明指导语可以提示被试放下既有观念，以无偏见的方式进行推理，从而有效促进了贝叶斯推理表现。而在提示条件下增加因果信息并没有促进作用，表明对于较高元认知的被试因果信息作用是有限的。     

儿童在因果知识领域内的选择性学习

儿童的选择性学习是目前认知发展领域的热点问题。儿童在因果知识领域内的选择性学习(即选择性因果学习)对于回答儿童如何获取知识这个经典问题具有重要意义。儿童的选择性因果学习表现在对他人解释的辨别、评估与采纳上。他们会主动向可靠的信息提供者寻求解释, 并在接收回答后表现出选择性跟进反应。对于他人的回答, 年幼儿童不仅能根据言语线索辨别出解释性陈述, 还能依据解释的结构特征选择更好的陈述加以采纳, 年长儿童甚至可以从不同模式的解释中灵活地学习更适宜的因果知识。未来研究应深入关注解释的其它特征在儿童选择性因果学习中的作用, 进一步探讨选择性因果学习的认知机制。     

Causal learning mechanisms in very young children: two-, three-, and four-year-olds infer causal relations from patterns of variation and covariation

Three studies investigated whether young children make accurate causal inferences on the basis of patterns of variation and covariation. Children were presented with a new causal relation by means of a machine called the "blicket detector." Some objects, but not others, made the machine light up and play music. In the first 2 experiments, children were told that "blickets make the machine go" and were then asked to identify which objects were "blickets." Two-, 3-, and 4-year-old children were shown various patterns of variation and covariation between two different objects and the activation of the machine. All 3 age groups took this information into account in their causal judgments about which objects were blickets. In a 3rd experiment, 3- and 4-year-old children used the information when they were asked to make the machine stop. These results are related to Bayes-net causal graphical models of causal learning.     

Causal learning across domains

Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence and independence to craft novel interventions across domains. In Experiments 4 and 5, children's sensitivity to patterns of dependence was pitted against their domain-specific knowledge. Children used conditional probabilities to make accurate causal inferences even when asked to violate domain boundaries.     

Mechanisms of theory formation in young children

Research suggests that by the age of five, children have extensive causal knowledge, in the form of intuitive theories. The crucial question for developmental cognitive science is how young children are able to learn causal structure from evidence. Recently, researchers in computer science and statistics have developed representations (causal Bayes nets) and learning algorithms to infer causal structure from evidence. Here we explore evidence suggesting that infants and children have the prerequisites for making causal inferences consistent with causal Bayes net learning algorithms. Specifically, we look at infants and children's ability to learn from evidence in the form of conditional probabilities, interventions and combinations of the two.     

Inferential reasoning by exclusion in children (Homo sapiens)

The cups task is the most widely adopted forced-choice paradigm for comparative studies of inferential reasoning by exclusion. In this task, subjects are presented with two cups, one of which has been surreptitiously baited. When the empty cup is shaken or its interior shown, it is possible to infer by exclusion that the alternative cup contains the reward. The present study extends the existing body of comparative work to include human children (Homo sapiens). Like chimpanzees (Pan troglodytes) that were tested with the same equipment and near-identical procedures, children aged three to five made apparent inferences using both visual and auditory information, although the youngest children showed the least-developed ability in the auditory modality. However, unlike chimpanzees, children of all ages used causally irrelevant information in a control test designed to examine the possibility that their apparent auditory inferences were the product of contingency learning (the duplicate cups test). Nevertheless, the children's ability to reason by exclusion was corroborated by their performance on a novel verbal disjunctive syllogism test, and we found preliminary evidence consistent with the suggestion that children used their causal-logical understanding to reason by exclusion in the cups task, but subsequently treated the duplicate cups information as symbolic or communicative, rather than causal. Implications for future comparative research are discussed.     

Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector,” a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4-year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base rate of certain events occurring. Four-year-olds, but not 3-year-olds were able to make this inference.     

5~6岁儿童传递推理能力的发展特点

通过有效的实验设计,在控制儿童记忆容量影响的前提下,考察了5~6岁儿童在长度传递推理和重量传递推理能力上的发展水平。结果表明:(1)5~6岁是儿童传递推理能力迅速发展的时期。在大概6岁左右,大多数儿童已经能够进行真正意义上的传递推理。(2)此年龄儿童在长度传递推理和重量传递推理能力上不具有显著差异。(3)条件排列的一致与不一致对于被试的推理成绩并不具有显著影响。     

推理方向与规则维度对儿童因果推理的影响

采用Frye和Zelazo等（1996）所设计的“二进二出”装置（斜面滚球装置）,设置了不同的推理方向、规则维度的因果推理任务,采用个别实验法,研究了60名3.5～4.5岁儿童因果推理能力的发展。结果发现：（1）儿童在不同方向的因果推理任务上成绩差异显著,因→果推理成绩要好于果→因推理;（2）不同维度下儿童的推理成绩有极显著的差异性,一维的因果推理更容易,三维合取规则的因果推理任务更难;（3）3.5～4岁左右是儿童因果推理能力发展的快速期。     

Children's beliefs about the fantasy/reality status of hypothesized machines

Four-year-olds, 6-year-olds, and adults were asked to make judgments about the reality status of four different types of machines: real machines that children and adults interact with on a daily basis, real machines that children and adults interact with rarely (if at all), and impossible machines that violated a real-world physical or biological causal law. Adults generally categorized all of the machines accurately. Both groups of children categorized familiar possible machines as real, but were agnostic as to the fantasy status of unfamiliar possible machines. Children generally responded that both kinds of impossible machines were make-believe, but 4-year-olds were more likely to make these accurate judgments for the physical than biological items, different from the older children and adults (whose responses were similar). These data suggest that children's judgments about the possibility of machines are not strictly limited by first-hand experience. Young children's domain-specific causal knowledge interacts with their understanding of the fantasy/reality distinction to constrain their inferences in a rational way.     

The influence of the number of relevant causes on the processing of covariation information in causal reasoning

Research on causal reasoning has focused on the influence of covariation between candidate causes and effects on causal judgments. We suggest that the type of covariation information to which people attend is affected by the task being performed. For this, we manipulated the test questions for the evaluation of contingency information and observed its influence on both contingency learning and subsequent causal selections. When people select one cause related to an effect, they focus on conditional contingencies assuming the absence of alternative causes. When people select two causes related to an effect, they focus on conditional contingencies assuming the presence of alternative causes. We demonstrated this use of contingency information in four experiments.