Right Hemispheric Dominance of Creative Insight: An Event-Related Potential Study

The purpose of this study was to investigate the hemispheric effect of creative insight. This study used high-density ERPs to record participants' brain activity while they performed an insight task. Results showed that both insight solutions and incomprehension solutions elicited a more negative ERP deflection (N320～550) than noninsight solutions in the time window of 320～550 msec. Scalp ERPs and topographical maps revealed that the insight N320～550 exhibited a relative RH dominance, whereas the incomprehension N320～550 exhibited a relative midline dominance. Dipole source analysis showed that the generator of N320～550 elicited by insight and incomprehension solutions was localized in the right middle frontal gyrus and the right parahippocampus gyrus, respectively. These distinct spatiotemporal patterns involved in insight processing and incomprehension processing suggest that the observed N320～550 might reflect the processing of set-shift and the formation of novel associations. Moreover, the spatiotemporal pattern of brain activation associated with insight solutions supported the idea that the right hemispheric dominance theory of creative thinking also applies to creative insight.     

On the Model-Based Bootstrap With Missing Data: Obtaining a P-Value for a Test of Exact Fit

Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) Beran, R. and Srivastava, M. S. 1985. Bootstrap tests and confidence regions for functions of a covariance matrix. The Annals of Statistics, 13: 95–115. [Crossref], [Web of Science ®] , [Google Scholar] for general covariance structure models and applied to structural equation modeling by Bollen and Stine (1992) Bollen, K. A. and Stine, R. A. 1992. Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research, 21: 205–229. [Crossref], [Web of Science ®] , [Google Scholar]. An extension of this transformation to missing data was presented by Enders (2002) Enders, C. K. 2002. Applying the Bollen-Stine bootstrap for goodness-of-fit measures to structural equation models with missing data. Multivariate Behavioral Research, 37: 359–377. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], but it is an approximate and not an exact solution, with the degree of approximation unknown. In this article, we provide several approaches to obtaining an exact solution. First, an explicit solution for the special case when the sample covariance matrix within each missing data pattern is invertible is given. Second, 2 iterative algorithms are described for obtaining an exact solution in the general case. We evaluate the rejection rates of the bootstrapped likelihood ratio statistic obtained via the new procedures in a Monte Carlo study. Our main finding is that model-based bootstrap with incomplete data performs quite well across a variety of distributional conditions, missing data mechanisms, and proportions of missing data. We illustrate our new procedures using empirical data on 26 cognitive ability measures in junior high students, published in Holzinger and Swineford (1939) Holzinger, K. J. and Swineford, F. 1939. A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs, 48: 1–91.  [Google Scholar].     

Abstract: Local Influence and Robust Methods for Mediation Models

Mediation analysis investigates how certain variables mediate the effect of predictors on outcome variables. Existing studies of mediation models have been limited to normal theory maximum likelihood (ML) or least squares with normally distributed data. Because real data in the social and behavioral sciences are seldom normally distributed and often contain outliers, classical methods can result in biased and inefficient estimates, which lead to inaccurate or unreliable test of the meditated effect. The authors propose two approaches for better mediation analysis. One is to identify cases that strongly affect test results of mediation using local influence methods and robust methods. The other is to use robust methods for parameter estimation, and then test the mediated effect based on the robust estimates. Analytic details of both local influence and robust methods particular for mediation models were provided and one real data example was given. We first used local influence and robust methods to identify influential cases. Then, for the original data and the data with the identified influential cases removed, the mediated effect was tested using two estimation methods: normal theory ML and the robust method, crossing two tests of mediation: the Sobel (1982) Sobel, M. E. 1982. “Asymptotic confidence intervals for indirect effects in structural equation models”. In Sociological methodology, Edited by: Leinhardt, S. 290–312. Washington, DC: American Sociological Association. [Crossref] , [Google Scholar] test using information-based standard error (z _I ) and sandwich-type standard error (z _SW ). Results show that local influence and robust methods rank the influence of cases similarly, while the robust method is more objective. The widely used z _I statistic is inflated when the distribution is heavy-tailed. Compared to normal theory ML, the robust method provides estimates with smaller standard errors and more reliable test.     

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood Versus Robust Methods

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data, in practice, can make the ML estimator (MLE) biased and inefficient. In addition to ML, robust methods have also been developed, which are designed to minimize the effects of outlying cases. But the properties of robust estimates and their standard errors (SEs) have never been systematically studied. This article studies two robust methods and compares them against the ML method with respect to bias and efficiency using a confirmatory factor model. Simulation results show that robust methods lead to results comparable with ML when data are normally distributed. When data have heavy tails or outlying cases, robust methods lead to less biased and more efficient estimators than MLEs. A formula to obtain consistent SEs for one of the robust methods is also developed. The formula-based SEs for both robust estimators match the empirical SEs very well with medium-size samples. A sample of the Cross Racial Identity Scale with a 6-factor model is used for illustration. Results also confirm conclusions of the simulation study.     

A Class of Population Covariance Matrices in the Bootstrap Approach to Covariance Structure Analysis

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given sample size, also provides more accurate results than those based on standard asymptotics. But the procedure needs a matrix to play the role of the population covariance matrix. The closer the matrix is to the true population covariance matrix, the more valid the bootstrap inference is. The current paper proposes a class of covariance matrices by combining theory and data. Thus, a proper matrix from this class is closer to the true population covariance matrix than those constructed by any existing methods. Each of the covariance matrices is easy to generate and also satisfies several desired properties. An example with nine cognitive variables and a confirmatory factor model illustrates the details for creating population covariance matrices with different misspecifications. When evaluating the substantive model, bootstrap or simulation procedures based on these matrices will lead to more accurate conclusion than that based on artificial covariance matrices.     

干扰任务对说谎影响的研究

摘要:本文将干扰任务对记忆的影响同测谎结合起来,试图运用这一方法来对求职者的真实学历和虚假学历进行区分。实验采用模拟面试的方式,对被试在有或无干扰任务条件下进行两次提问,结果发现,四种任务条件对被试真实经历和虚假经历都具有区分度,其中双双条件的区分度是最高的。并且,被试在双双任务条件下虚假经历的得分显著低于其他三种任务条件。     

幼儿教师职业承诺与情绪耗竭:情绪劳动的中介作用

以218名幼儿教师为被试,采用问卷调查法,探讨了职业承诺对情绪耗竭的影响,以及情绪劳动在职业承诺对情绪耗竭影响中的中介效应。结果表明:(1)职业承诺与自然行为、深层行为呈显著正相关,而与表层行为、情绪耗竭呈显著负相关。(2)自然行为、深层行为与情绪耗竭均呈显著负相关,而表层行为与情绪耗竭呈显著正相关。(3)深层行为和表层行为在职业承诺与情绪耗竭关系中起部分中介作用,自然行为在职业承诺与情绪耗竭关系间中介效应不显著。     

组织成员地位的形成及影响机制

组织成员地位通常是指组织内个体受组织其他成员尊重和钦佩的程度,是近些年备受关注的一个心理学构念。虽然组织成员地位与权力、阶层、面子、基于组织的自尊等概念具有一定的关联性,但它们之间存在着明显的界限。组织成员地位主要受到外在显性因素(形体特征、人口统计特征)、内在心理因素(人格、认知、情绪、行为)以及神经生理因素(睾酮激素、皮质醇激素)的影响,并能够对组织成员的认知、情绪、行为和绩效产生显著的影响。未来研究应当进一步区分组织成员地位的维度,探索组织成员地位的生物学基础和文化差异,关注组织成员地位的动态演化及其后果,并检验团队和组织层面地位分布的作用机制。     

不同胜任力水平下大学生自谦归因对他人评价的影响

从人际吸引力和胜任力评价两个维度探讨大学生自谦归因对他人评价的影响。以120名大学生为样本,采用2*2的组间情景实验设计,让被试观测情景材料中不同胜任力水平个体的自谦归因后作出评价,研究发现,不同胜任力水平下大学生自谦归因对人际吸引力和胜任力评价上存在交互效应,当材料中大学生胜任力水平高时,自谦归因能提高大学生的人际吸引力。然而,当材料中大学生胜任力水平低时,自谦归因不仅不能提高人际吸引力,反而减少了他人对大学生的胜任力评价。