多元动机网格测验（MMG-S）中文版的修订报告

对Sokolowski等人编制的简版多元动机网格测验（MMG-S）作了中文版的修订。MMG-S的验证性因素分析表明：中文修订版MMG-S仍保持英文版的六因子结构,修订后六因子的α系数在0.70~0.84之间。中文修订版MMG-S具有良好的心理学测量指标,可用于测量企业管理人员的三种社会性动机。     

程序公正作用机制中的调节变量

程序公正是指用于决定分配的过程是否公正。回顾近年来程序公正作用机制的相关理论以及实证研究结果发现, 对程序公正的效果起调节作用的主要有四类因素, 分别为情景因素、个体特征因素、分配结果因素以及领导者因素。今后该主题的研究应进一步关注发言权效应的跨文化验证、探索本土化程序公正原则、进一步考察情景变量的调节效应、加强领导者因素的研究、结合分配公正进行研究, 并应加强程序公正的应用性研究。     

内隐记忆中的神经机制研究方法

知觉启动与语义启动神经机制的研究是内隐记忆神经机制研究的两条重要途径。本文介绍了内隐记忆神经机制的研究途径之一：知觉启动神经机制的研究进展情况,并对研究中存在的问题及解决方法提出建议。     

从山东地名解读齐鲁文化

地名是一种特殊的文化现象,可以反映特定地域的自然和人文条件。山东地名与齐鲁地理文化、齐鲁历史文化、齐鲁居民生活、齐鲁方言文化等方面密切相关。本文从这些方面解读山东地名所反映的齐鲁文化。     

Why best cannot last: Cultural differences in predicting regression toward the mean

Four studies were conducted to investigate cultural differences in predicting and understanding regression toward the mean. We demonstrated, with tasks in such domains as athletic competition, health and weather, that Chinese are more likely than Canadians to make predictions that are consistent with regression toward the mean. In addition, Chinese are more likely than Canadians to choose a regression‐consistent explanation to account for regression toward the mean. The findings are consistent with cultural differences in lay theories about how people, objects and events develop over time.     

人类视觉系统获取颜色恒常性的认知操作特性

以光度测量为基础,采用心理物理学方法,探讨人类视觉系统获取颜色常性的认知特性。共进行了两个实验。实验一,探讨颜色对比对获取颜色常性的认知操作过程的作用。结果发现,视觉系统可根据已适应了的对比参照作用,而校正（或补偿）由于外界照明变化而引起的物理色度变化。实验二,探讨视觉经验对人类视觉系统获取颜色常性的作用。结果发现,颜色常性不仅是一种视觉低层次、自动完成的补偿,而且涉及高层次的意识活动。     

The Thorny Relation Between Measurement Quality and Fit Index Cutoffs in Latent Variable Models

Latent variable modeling is a popular and flexible statistical framework. Concomitant with fitting latent variable models is assessment of how well the theoretical model fits the observed data. Although firm cutoffs for these fit indexes are often cited, recent statistical proofs and simulations have shown that these fit indexes are highly susceptible to measurement quality. For instance, a root mean square error of approximation (RMSEA) value of 0.06 (conventionally thought to indicate good fit) can actually indicate poor fit with poor measurement quality (e.g., standardized factors loadings of around 0.40). Conversely, an RMSEA value of 0.20 (conventionally thought to indicate very poor fit) can indicate acceptable fit with very high measurement quality (standardized factor loadings around 0.90). Despite the wide-ranging effect on applications of latent variable models, the high level of technical detail involved with this phenomenon has curtailed the exposure of these important findings to empirical researchers who are employing these methods. This article briefly reviews these methodological studies in minimal technical detail and provides a demonstration to easily quantify the large influence measurement quality has on fit index values and how greatly the cutoffs would change if they were derived under an alternative level of measurement quality. Recommendations for best practice are also discussed.     

Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.