有关条件推理中概率效应的实验研究

通过预备实验选取了四种不同条件概率的条件规则和四种不同前后件概率组合的条件规则作为实验材料,以大学生为被试,考察了两种概率因素(条件概率和前后件概率)对条件推理的演绎形式(MP、DA、AC、MT)以及变通形式(四卡问题)的影响。结果表明,两种概率因素对四种条件推理的影响都非常显著,研究进一步证实了人们对四种推理的认可程度主要与范畴前提的概率成正比的结论;但概率因素对四卡问题的解决影响不明显。     

论述理解中逻辑有效与逻辑无效条件推理的研究

句法理论和心理模型理论是解释论述理解中逻辑有效 (如MP)与逻辑无效 (如AC)条件推理机制的两种不同观点。本研究通过两项实验旨在对这两种理论加以检测。实验要求被试阅读遵循MP或AC形式的故事后对故事结论的正确性做出判断。结果表明 ,在论述理解中 ,条件前提的语义联系强度影响MP推理 ;深思熟虑对MP和AC推理均产生影响。研究结果支持心理模型理论。     

左右半球在语篇理解中的协同作用

通过2例一侧颞叶损伤患者研究了一侧颞叶损伤及两侧半球的协同作用对语篇理解的影响,并探讨了发生此种影响的可能机制。结果表明一侧颞叶的损伤将导致患者语篇水平的理解障碍及语言推理能力的下降;提示语篇理解需双侧半球的协同作用,而推理能力下降可能是引发语篇理解障碍的原因之一。     

"The End of all our Exploring" in Science and Theology

Abstract. The present malaise of religion—and of theology, its intellectual formulation—in Western society is analyzed, with some personal references, especially with respect to its history in the United Kingdom and the United States. The need for a more open theology that takes account of scientific perspectives is urged. An indication of the understandings of God and of God's relation to the world which result from an exploration starting from scientific perspectives is expounded together with their fruitful relation to some traditional themes. The implications of this for the future of theology are suggested, not least in relation to the new phase, beginning in 2003, of the development of the Zygon Center for Religion and Science. In a concluding reflection the hope is expressed that the shared global experience and perspectives generated by the sciences might form a more common and acceptable starting point than hitherto for the exploration towards God of the seekers of many religious traditions and of none.     

有关条件推理认知机制的概率理论

条件推理主要有两种研究范式：演绎形式(MP、DA、AC、MT)和变通形式(四卡问题)。已往研究主要探讨了内容因素、情境因素以及元认知因素等对条件推理的影响,形成了若干理论模型和观点。近来、Oaksford等人研究了概率因素对条件推理的影响．提出了条件概率模型。本文对这种新的研究取向作了详细的介绍和评价,并对未来研究进行了展望。     

不同语言理解能力中学生的语篇连贯推理

该研究使用移动视窗技术考察了初二和高一年级中的高低语言理解能力被试语篇阅读过程中将当前信息与背景信息之间建立联系,建构语篇连贯表征的连贯推理加工特点。结果表明：(1)不同语言理解能力个体对语篇中明确提及信息的记忆无差异,但在阅读过程中进行连贯推理上存在差异。(2)个体进行连贯推理的速度和准确度会随年龄增加有所提高。     

Can unpredicted outcomes be intended? The role of outcome-beliefs in children's judgments of intention

An adult-like concept of intention includes a deliberate action to achieve a goal and a belief that one's action (if successful) will cause the desired outcome. For example, good outcomes caused by accident or by chance are not believed to be caused intentionally. In two experiments, we asked whether children understand this connection between intentions and outcomes. Children played two games in which actions could produce unintended outcomes (i.e., causes were unplanned). Children sometimes received a desirable reward independent of intention. In Experiment 1, 4- and 5-year-olds mistakenly claimed they had intended the desirable outcome even when it was unexpected. Four-year-olds judged that they had not intended a deliberate action if it did not yield a rewarding outcome. Experiment 2 demonstrates that 6-year-olds seldom make these errors. The results suggest that 4- and 5-year-old children have not yet attained an adult-like concept of intention. Their inaccurate judgments regarding their intentions, given a rewarding yet unexpected outcome, can be explained by a positivity bias.     

预期推理在长时记忆中的表征形式

该研究采用移动窗口技术,通过两个实验研究探讨在高低不同竞争条件下预期推理结果在长时记忆中的表征形式。实验结果表明,预期推理在长时记忆中的表征形式依赖于文本中预期推理的引发语境,在不同竞争强度的预期语境中,预期推理的结果在长时记忆会以不同的形式进行表征。在低竞争条件下,预期推理的结果以确定的形式表征;只有在高竞争条件下,预期推理的结果才会以假定的形式进行表征。     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.