应激对风险和社会决策的影响

应激是有机体在受到真实或者潜在的威胁刺激时所表现出来的全身性非特异性反应, 伴随着紧张和焦虑的心理体验, 交感神经系统的兴奋, 糖皮质激素分泌的增多以及脑干−边缘系统−前额叶神经环路的改变。应激对个体在风险决策行为中的风险寻求和风险回避倾向, 社会决策行为中的利己和利他倾向都会产生重要影响。策略使用异常、习惯化和自动化反应增强、反馈学习过程以及奖惩敏感性的改变是应激影响决策行为的认知基础; 应激激素的分泌, 及杏仁核、前额叶等在决策过程发挥重要作用的脑区活动的改变则为应激作用于决策行为的神经基础。未来研究应重点关注：应激的个体差异与应激对决策影响效应多样性的关系; 综合多种指标对应激进行测量; 考察应激的时序效应; 揭示个体的最佳应激水平; 加强对慢性应激影响决策以及应激对决策影响效应可逆性的研究; 揭示应激影响决策的神经机制。     

认知诊断模型资料拟合检验方法和统计量

认知诊断模型界定了测验题目和所考察属性之间的关系, 通过被试的作答反应获取被试对属性或知识技能的掌握情况。认知诊断模型资料拟合检验可以从项目拟合、模型绝对拟合、模型相对拟合和个人拟合方等方面进行。通过对认知诊断拟合检验方法和统计量的详细介绍和评价, 可为认知诊断实践提供借鉴和参考。未来研究可在更丰富的研究条件下对各统计量的性能进行评价和对比, 完善已有的拟合检验方法, 提出新的拟合统计量。     

中学生物理力学问题的分类及表征研究

采用自由分类和标签分类法,以16道中学物理力学应用题为实验材料,探讨不同学业水平中学生对物理问题的分类及表征差异,以及题目陈述方式(有无示意图)对分类的影响.结果发现: (1)自由分类实验中,学优生侧重于按问题的深层特征(物理原理表征)进行分类,学困生侧重于问题的表面特征(文字表征);题目陈述方式对优困生的分类时间和分类表征也有影响. (2)标签分类实验中,学优生的正确分类题目数量显著高于学困生,有示意图下学优生的正确分类题目数量显著高于无示意图下.     

5HT antagonists attenuate MK801-impaired radial arm maze performance in rats

Glutamatergic hypofunction occurs in Alzheimer's disease (AD). MK801, a noncompetitive blocker of glutamate N-methyl-D-aspartate receptors, was used to disrupt the cognitive performance of rats trained on a delayed nonmatching to sample radial maze task. Drugs which act by blocking serotonin (5-HT) receptors were evaluated for their ability to reduce the cognitive impairment produced by MK801. Specifically, WAY-100635, a selective 5-HT1A receptor antagonist, buspirone, a 5-HT1A partial agonist, ritanserin, a 5-HT2 antagonist, and ondansetron, a 5-HT3 antagonist, were assessed. In addition, the muscarinic agonist arecoline was evaluated for its potential cognitive benefit in this model. It was found that WAY-100635 significantly reduced the cognitive impairment induced by MK801. Treatment with single doses of ritanserin, ondansetron, or arecoline in combination with MK801 did not result in a cognitive impairment, indicating that these drugs attenuated the MK801 impairment. The combination of buspirone and MK801 resulted in an inability of the animals to complete the task. These results suggest that interactions between 5-HT and glutamate may mediate the beneficial effects of reducing cognitive impairment and that 5-HT antagonists, especially selective 5-HT1A antagonists, may be useful in treating AD. Further, it is indicated that the MK801 model of cognitive impairment may add to the armamentarium of tools available to predict treatment efficacy in AD.     

Rank‐based permutation approaches for non‐parametric factorial designs

Inference methods for null hypotheses formulated in terms of distribution functions in general non‐parametric factorial designs are studied. The methods can be applied to continuous, ordinal or even ordered categorical data in a unified way, and are based only on ranks. In this set‐up Wald‐type statistics and ANOVA‐type statistics are the current state of the art. The first method is asymptotically exact but a rather liberal statistical testing procedure for small to moderate sample size, while the latter is only an approximation which does not possess the correct asymptotic α level under the null. To bridge these gaps, a novel permutation approach is proposed which can be seen as a flexible generalization of the Kruskal–Wallis test to all kinds of factorial designs with independent observations. It is proven that the permutation principle is asymptotically correct while keeping its finite exactness property when data are exchangeable. The results of extensive simulation studies foster these theoretical findings. A real data set exemplifies its applicability.     

More efficient parameter estimates for factor analysis of ordinal variables by ridge generalized least squares

Data in psychology are often collected using Likert‐type scales, and it has been shown that factor analysis of Likert‐type data is better performed on the polychoric correlation matrix than on the product‐moment covariance matrix, especially when the distributions of the observed variables are skewed. In theory, factor analysis of the polychoric correlation matrix is best conducted using generalized least squares with an asymptotically correct weight matrix (AGLS). However, simulation studies showed that both least squares (LS) and diagonally weighted least squares (DWLS) perform better than AGLS, and thus LS or DWLS is routinely used in practice. In either LS or DWLS, the associations among the polychoric correlation coefficients are completely ignored. To mend such a gap between statistical theory and empirical work, this paper proposes new methods, called ridge GLS, for factor analysis of ordinal data. Monte Carlo results show that, for a wide range of sample sizes, ridge GLS methods yield uniformly more accurate parameter estimates than existing methods (LS, DWLS, AGLS). A real‐data example indicates that estimates by ridge GLS are 9–20% more efficient than those by existing methods. Rescaled and adjusted test statistics as well as sandwich‐type standard errors following the ridge GLS methods also perform reasonably well.     

Empirically Corrected Rescaled Statistics for SEM with Small N and Large p

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. With typical nonnormally distributed data in practice, a rescaled statistic T_rml proposed by Satorra and Bentler was recommended in the literature of SEM. However, T_rml has been shown to be problematic when the sample size N is small and/or the number of variables p is large. There does not exist a reliable test statistic for SEM with small N or large p, especially with nonnormally distributed data. Following the principle of Bartlett correction, this article develops empirical corrections to T_rml so that the mean of the empirically corrected statistics approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics control type I errors reasonably well even when N is smaller than 2p, where T_rml may reject the correct model 100% even for normally distributed data. The application of the empirically corrected statistics is illustrated via a real data example.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

生态化微表情识别测验EMERT的建立——对JACBART微表情识别测验的改进与发展

经典JACBART微表情识别测验只考察平静表情背景下微表情识别,生态效度不高。本研究创建生态化微表情识别测验,考察所有7种基本表情背景下的6种微表情识别特征。结果发现:(1)该测验具有良好的重测信度、校标效度和生态效度,能够稳定有效地测量生态化微表情识别。(2)信效度检验揭示了生态化微表情识别特征。某些生态化微表情识别存在训练效应。生态化微表情与经典微表情或普通表情普遍相关。恐惧、悲伤、厌恶、愤怒微表情背景主效应显著;惊讶和愉快微表情背景主效应不显著,成对比较发现各背景下惊讶/愉快微表情差异不显著,但是与普通表情有广泛的显著差异。用不同背景下微表情识别正确率的标准差定义生态化微表情识别波动,发现生态化微表情识别具有稳定的波动性。     

Implicit health associations across the adult lifespan

Objective: Explicit reports of one’s health self-concept (e.g. rate your overall health) are commonly used in research and clinical practice. These measures predict important health outcomes, but rely on conscious introspection so may not fully capture the different components of the health self-concept (e.g. more automatic components) that relate to actual health. This study examined the health-implicit association test (health-IAT), and how it may add to our prediction of health from self-reports.

Design: 1004 participants (ages 18–85) completed this web-based study with the health-IAT (assessing self-healthy implicit associations) and explicit assessments of health.

Main outcome measures: Self-reported measures of physical functioning.

Results: The health-IAT was valid and reliable. Older age was correlated with stronger self-healthy implicit associations. Although the health-IAT did not incrementally predict self-reported markers of physical functioning when only controlling for explicit health self-concept, it was an incremental predictor once age was entered for all four models tested.

Conclusions: The health-IAT appears to be a valid and reliable new measure that assesses implicit self-concept relating to physical health. Results reveal the potential value of assessing implicit health self-concept in both research and practice, especially when taking into account age.