青少年偏差行为的同伴选择和影响效应:基于纵向社会网络的元分析

以22篇纵向社会网络分析研究文献(30项独立样本研究,27746个被试)为对象,使用元分析技术探讨青少年偏差行为的同伴选择和同伴影响效应假设。结果发现:(1)同伴选择和同伴影响效应都显著,但同伴影响效应显著大于同伴选择效应;(2)青少年发展阶段在同伴选择和同伴影响过程中存在显著的调节效应。在同伴选择过程中,青少年早期阶段不显著,而中、晚期阶段显著;在同伴影响过程中,青少年早、中期显著,晚期阶段不显著。(3)偏差行为类型在同伴选择和同伴影响效应中的调节效应不显著。(4)网络数量在同伴影响过程中存在显著的调节效应,随着网络数量增加同伴影响效应越可能成立。结论,中晚期青少年会基于偏差行为相似性选择朋友,早中期青少年会受朋友的影响而产生偏差行为。     

Personalization in Mother–Child Emotion Talk Across Three Contexts

An unexplored aspect of contextual variation in emotion talk is the extent to which the emotions mothers and children discuss relate to the child, mother, or another self. To establish the extent to which mothers and children personalize the emotions they discuss, we examined the emotion talk of 40 American mother–child dyads in three conversational contexts: reminiscing, book reading, and play. We found that both mothers and children talked about emotions directly relevant to the child mostly in the reminiscing context and about emotions relating to another self mostly in the book context. Interestingly, the discussion of personalized emotions did not significantly differ between the book and play contexts, but did for the independent emotions. The discussion of mothers' own emotions occurred with almost the same frequency in all three contexts. In addition, within‐context analyses revealed that while mothers discussed the child's and their own emotions in the book and play contexts at about the same rate, children focused on their own emotions significantly more than on mothers' emotions in all three contexts. We contextualize the significance of the results in light of intervention programmes and future research. Copyright © 2013 John Wiley & Sons, Ltd.     

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。     

Childhood fantasy play relates to adult socio-emotional competence

Childhood fantasy play and creation of imaginary companions are thought to confer socio-emotional benefits in children, but little is known about how they relate to socio-emotional competence in adulthood. A total of 341 adults (81 males) aged 18 and above (M = 31.47, SD = 12.62) completed an online survey examining their fantasy play as a child, their childhood imaginary companion status, and their adult socio-emotional competence. Adults who reported higher levels of childhood fantasy play were found to be significantly more prosocial, empathetic, and emotionally intelligent than their counterparts after controlling for demographic factors. Recall of a childhood imaginary companion, however, was significantly related only to higher scores for perspective-taking and did not explain unique variance in any adult competence measure. Findings suggest that engagement in fantasy play during childhood may be a precursor to later socio-emotional competence, while benefits previously associated with imaginary companions specifically may not extend into adulthood.     

学前儿童在游戏中社交和认知类型发展的研究──中美跨文化比较

采用时间取样观察法,以录像对中美１５０名学前儿童在游戏中的社交和认知类型进行比较研究,结果表明：①学前儿童社会交往发展水平、认知发展水平、游戏难度随年龄的增长而提高;②学前儿童较高水平的认知游戏行为与较高水平的社会技能的发展相关联,社交水平与认知水平的发展相互影响;③中美学前儿童社交和认知能力发展进程具有一致性,游戏行为发展模式具有一致性;④中美学前儿童游戏的方式和内容、社交和认知水平存在显著差异,表明不同文化背景的制约性。     

职业选拔情境下人格测验作假研究

在职业选拔情境下被试很容易对人格测验作假,从而制约了人格测验在企业中的应用。许多研究者在努力解决作假问题,分别就应聘者是否会作假,作假给人格测验带来的负面影响、应聘者如何作假以及如果应对作假等问题进行了深入的探讨。经过几十年的发展,该研究领域已经形成了包括实验诱导设计、已知群体设计和量表设计等几种特定的研究范式。研究结果显示,大多数应聘者会作假,但其负面影响并不严重;作假不同于社会称许性反应,它是一种工作称许性反应。目前的几种应对作假的方法尚存在一些问题,其有效性有待提高。总之,人格测验的作假作用明显,其研究难度较大,有待革新性理论和方法的出现     

亲子互动中游戏参与方式、情感交流和儿童心理理论的关系

本研究通过亲子互动游戏观察来考查亲子游戏参与方式、情感交流与儿童心理理论发展的关系。实验结果如下:第一,控制了年龄因素后,儿童的亲子游戏参与方式中的主动参与和建议游戏两种方式与儿童心理理论发展之间存在显著的相关;而对于母亲来说,指导游戏的参与方式和儿童心理理论发展存在显著相关。第二,控制了年龄因素之后,儿童情感交流方面的分享积极情感和心理理论发展之间存在显著相关。而母亲这方面的指标和儿童心理理论发展没有显著相关。文章对上述结果进行了分析讨论。     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.     

On the current status of the evolutionary theory of behavior dynamics

The evolutionary theory of behavior dynamics is a complexity theory that instantiates the Darwinian principles of selection, reproduction, and mutation in a genetic algorithm. The algorithm is used to animate artificial organisms that behave continuously in time and can be placed in any experimental environment. The present paper is an update on the status of the theory. It includes a summary of the evidence supporting the theory, a list of the theory's untested predictions, and a discussion of how the algorithmic operations of the theory may correspond to material reality. Based on the evidence reviewed here, the evolutionary theory appears to be a strong candidate for a comprehensive theory of adaptive behavior.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.