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。     

样例学习条件下的因果力估计

在逐个呈现因果样例的条件下,考察单一因果关系因果力估计的特点,同时检验联想解释,概率对比模型,权重DP模型,效力PC理论和pCI规则。实验让65名大学生被试估计不同化学药物影响动物基因变异的能力。实验结果表明：（1）对产生原因的因果力估计符合权重DP模型;（2）对预防原因的因果力估计较多符合效力PC理论;（3）因果力估计具有复杂多样性,难以用统一的模式加以描述和概括。     

用不同外部表征方式集中呈现信息条件下的因果力判断

在分别用文字陈述、表格和图形三种外部表征方式集中呈现因果信息的条件下,用直接估计因果力大小的实验范式考察单一因果关系因果力估计的特点,检验概率对比模型,效力PC理论和pCI规则。让287名大学生被试估计不同化学药物影响动物基因变异的能力。结果发现,对单一因果关系因果力估计具有以下4个特点：⑴不对称性：在预防原因条件下的因果力估计较多符合效力PC理论,而在产生原因条件下的因果力估计一般符合概率对比模型;⑵文字陈述、表格和图形三种信息外部表征方式,不影响产生原因条件下的因果力估计,但影响预防原因条件下的因果力估计。在预防原因条件下,与文字陈述和表格表征相比,图形表征会促使更多被试按效力PC理论来做因果力估计;⑶没有被试使用pCI规则;⑷被试估计因果力所使用的规则存在明显的个体差异。     

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。     

单一因果关系因果力判断研究述评

简单因果归纳包括自下而上的材料驱动的加工和自上而下的因果知识驱动加工两个方面。简单因果归纳的多数理论模型强调自下而上的材料驱动的加工,这些模型又存在联想解释与计算解释的基本区分。最后,概述了单一因果关系因果力判断研究的基本问题：因果力判断的主要影响因素和过程机制、研究的方法问题。     

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。     

取样大小对因果强度推理的影响研究

摘 要：使用纸笔测验探讨表格集中呈现信息条件下取样大小对单一因果关系强度推理的影响,并比较五种模型ΔP、效力PC、SS效力、Support和χ2的预测与实验数据的相关。结果显示：（1）取样大小对不同的问题有不同的影响：高取样在ΔP=0时导致了低估计值,在0<|ΔP|=PPC时没有效果,在|ΔP|相似文献   

因果模型构建的特点和新思路

因果模型构建作为心理学研究的一项中心任务,主要包括因果关系理论模型的构建和验证、因果关系数据模型的构建和验证,理论模型和数据模型之间进行匹配的因果关系推论过程,文章讨论了因果模型构建的基本条件。因果模型构建时所面临的问题及解决的策略,包括如何确定因果模型的类型,如何在不同层次进行建模、如何进行因果模型的验证。另外,文章还讨论了一种因果建模的新思路及发展趋势。     

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。     

Causal Models Drive Preference between Drugs that Treat a Focal versus Multiple Symptoms

This research examines the effects of causal beliefs on drug preference. In three studies, 374 undergraduate participants imagined that they suffered from a focal symptom and then indicated their preference between a drug claiming to treat only the focal symptom (single treatment) and a drug claiming to treat the focal symptom and a nonfocal symptom (dual treatment) they thought resulted from a common‐cause or from a different cause. Participants who thought that the symptoms resulted from different causes significantly preferred the single treatment drug more and the dual treatment drug less than participants who thought the symptoms resulted from a common‐cause. Process analysis yielded a significant mediation effect. Specifically, an individual's causal model determines preference by affecting the estimates of the potential gain and loss associated with using each drug. Participants who held a common‐cause model concerning the source of the symptoms thought they would be more likely to need the nonfocal treatment provided by the dual treatment drug and less likely to suffer from side effects when taking this drug, than those with a different‐cause model. The results demonstrate the influence of causal structure on judgment and choice. © 2017 The Authors Journal of Behavioral Decision Making Published by John Wiley & Sons Ltd.     

经验信息与共变信息对判断两原因共同作用的影响

本研究考察了被试在使用经验信息和共变信息进行两原因共同作用因果判断时所具有的特点。研究结果表明:(1)两原因的性质对于被试的选择有着显著影响。当两原因的可信度相等时,更多被试认为两原因都可以引起结果。(2)两原因都不出现时结果出现的概率P(e/~i~j)对于被试的选择没有显著影响。(3)两原因单独出现时结果出现的概率对于被试的选择有着显著的影响,但是这一变量是与两原因的可信程度共同产生影响的。     

其它可能原因的呈现方式在因果判断中的作用

研究使用不同方式呈现了其它可能原因,研究了这种不同的呈现方式对被试使用共变信息和经验信息进行因果判断的影响。研究结果表明:(1)其它可能原因的呈现方式对于因果判断有着一定的影响。当这一信息在待判断原因与结果的共变中出现、且永远存在时,被试会排除这一其它可能原因的作用,从而增强判断的信心;(2)当其它可能原因出现于待判断原因与结果的共变中、且永远存在时,被试会在一定程度上忽视经验信息的作用,更多地依赖共变信息做出判断。     

Judging the Importance of Constant and Variable Candidate Causes: A Test of the Power PC Theory

In two causal induction experiments subjects rated the importance of pairs of candidate causes in the production of a target effect; one candidate was present on every trial (constant cause), whereas the other was present on only some trials (variable cause). The design of both experiments consisted of a factorial combination of two values of the variable cause's covariation with the effect and three levels of the base rate of the effect. Judgements of the constant cause were inversely proportional to the level of covariation of the variable cause but were proportional to the base rate of the effect. The judgements were consistent with the predictions derived from the Rescorla-Wagner (1972) model of associative learning and with the predictions of the causal power theory of the probabilistic contrast model (Cheng, 1997) or 'power PC theory'. However, judgements of the importance of the variable candidate cause were proportional to the base rate of the effect, a phenomenon that is in some cases anticipated by the power PC theory. An alternative associative model, Pearce's (1987) similaritybased generalization model, predicts the influence of the base rate of the effect on the estimates of both the constant and the variable cause.     

Is causal induction based on causal power? Critique of Cheng (1997)

The authors empirically evaluate P. W. Cheng's (1997) power PC theory of causal induction. They reanalyze some published data taken to support the theory and show instead that the data are at variance with it. Then, they report 6 experiments in which participants evaluated the causal relationship between a fictitious chemical and DNA mutations. The power PC theory assumes that participants' estimates are based on the causal power p of a potential cause, where p is the contingency between the cause and the effect normalized by the base rate of the effect. Three of the experiments used a procedure in which causal information was presented trial by trial. For these experiments, the power PC theory was contrasted with the predictions of the probabilistic contrast model and the Rescorla-Wagner theory. For the remaining 3 experiments, a summary presentation format was employed to which only the probabilistic contrast model and the power PC theory are applicable. The power PC theory was unequivocally contradicted by the results obtained in these experiments, whereas the other 2 theories proved to be satisfactory.     

Causal Induction in the Presence of a Perfect Negative Cue: Contrasting Predictions from Associative and Statistical Models

Two experiments on human causal induction with multiple candidate causes are reported. Experiment 1 investigated the influence of a perfect preventive cause on the ratings of a less contingent cause. Whereas the Rescorla-Wagner model (RWM) and Cheng's probabilistic contrast model predict that the less contingent cause should be completely discounted, the Pearce model predicts, in most cases, an enhancement of that cause's perceived importance. Results corresponded more closely tothe predictions of the Pearce model.The predictions of both the RWM and the Pearce model rely on a constant context cue acquiring associative strength, yet no such cue was explicitly identified in the task scenario employed in Experiment 1. Experiment 2 replicated a number of key conditions of Experiment 1 with a task scenario that afforded ratings of the causal importance of the context in which the effectiveness of the discrete candidate causes was evaluated. In addition, the number of trials was increased to test the possibility that the ratings in Experiment 1 were the product of incomplete learning. The results of the first experiment were replicated and the ratings of the effectiveness of the context cue were anticipated by both the RWM and the Pearce model. Overall, the Pearce model offers a more comprehensive account of the causal inferences recorded in this study.     

Tests of the power PC theory of causal induction with negative contingencies

The power PC theory of causal induction (Cheng, 1997) proposes that causal estimates are based on the power p of a potential cause, where p is the contingency between the cause and effect normalized by the base rate of the effect. Previous tests of this theory have concentrated on generative causes that have positive contingencies with their associated outcomes. Here we empirically test this theory in two experiments using preventive causes that have negative contingencies for their outcomes. Contrary to the power PC theory, the results show that causal judgments vary with contingency across conditions of constant power p. This pattern is consistent, however, with several alternative accounts of causal judgment.