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.     

共同方法变异的影响及其统计控制途径的模型分析

共同方法变异(common method variance, CMV)指两个变量之间变异的重叠是因为使用同类测量工具而导致, 而不是代表潜在构念之间的真实关系。虽然以往研究显示CMV不一定导致研究结果的偏差, 在实际研究中应当加以考量。特别是在使用测量方法的研究中, 如果数据来源越单一, 测量方法越类似, CMV效应使研究结果产生偏差的可能性越大。CMV效应的控制方法包括过程控制法和统计控制法。在统计控制法的选择和使用上, 需要重点考虑该方法是否分离了三大变异(特质变异、方法变异和误差变异), CMV效应是在测量构念层面还是题目层面, CMV效应是加法效应还是乘法效应。控制潜在方法因子途径是统计控制方法中最重要的一类方法, 理解其模型是正确使用这类方法的前提。未来研究应当关注多个研究的CMV效应和侧重评估某个理论研究中CMV所引起的潜在的效度威胁。     

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。     

条件推理中的二元效应初探

通过预备实验选取前件(或后件)是二元(或多元)的5种条件命题的4种推理形式作为实验材料,以大学本科生为被试,考察了五种不同条件命题类型对4种推理结果的影响.结果表明命题类型、推理规则以及两者的交互作用都非常显著.这个结果进一步验证了条件推理的集合映射模型.     

文本阅读中回指推理发生的整合性因素

探讨回指距离、干扰词的精细描述、干扰词的典型性 对回指推理（先行词通达）的影响。包括2个实验,被试是华南师范大学一二年级本科生164名。采用移动窗口技术,要求被试阅读一定篇数（如,实验1a每个被试阅读15篇）的文章,对不同条件的探测词的反应时进行统计。实验1探讨干扰词精细描述类型（精细描述高和精细描述低）对回指推理的影响。结果表明,干扰的精细描述并非是影响回指推理的主要因素。实验2探讨干扰词和先行词的典型性类型的相对变化对回指推理的影响。结果表明高典型干扰在不同回指距离条件下都会对回指推理产生影响。本研究结果初步证明在影响回指推理的干扰典型性和干扰精细描述的二个因素中,起主要作用的是干扰的典型性     

A multilevel framework for understanding relationships among traits,states, situations and behaviours

A conceptual and analytic framework for understanding relationships among traits, states, situations, and behaviours is presented. The framework assumes that such relationships can be understood in terms of four questions. (1) What are the relationships between trait and state level constructs, which include psychological states, the situations people experience and behaviour? (2) What are the relationships between psychological states, between states and situations and between states and behaviours? (3) How do such state level relationships vary as a function of trait level individual differences? (4) How do the relationships that are the focus of questions 1, 2, and 3 change across time? This article describes how to use multilevel random coefficient modelling (MRCM) to examine such relationships. The framework can accommodate different definitions of traits and dispositions (Allportian, processing styles, profiles, etc.) and different ways of conceptualising relationships between states and traits (aggregationist, interactionist, etc.). Copyright © 2007 John Wiley & Sons, Ltd.     

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。