辛苦所得与意外所得的非理性消费偏差：基于IAT检测和ERP证据

不同来源的财富因其在人们心理的权重差异会导致对其消费态度与方式的不同。前人运用一系列行为实验已发现意外所得更容易消费、辛苦所得则不容易消费。基于心理账户和内隐社会认知等理论, 运用IAT和ERP考察财富的意外所得与辛苦所得在人们心理引起的内在消费偏差, 进而对内隐消费态度和脑加工机制进行间接检测。IAT研究结果表明, 意外所得与容易消费的联结更加紧密, 辛苦所得与不易消费的联结更加紧密, 从内隐层面验证了前人的行为研究结论; ERP研究结果发现辛苦所得与意外所得这两个不同收入来源可能建立了不同脑加工机制, 且在P3和LPC成分得到反映, P3成分可能是反映辛苦所得组偏好难消费型消费方式的ERP成分, LPC反映了意外所得收入条件下个体偏好易消费型消费方式的特点。     

跨期选择单维占优模型的过程检验

跨期选择需要决策者在眼前与未来的损益之间做出权衡与取舍。主流跨期选择理论认为, 跨期选择是把未来价值折扣到现在, 根据折扣后的价值进行选择的过程; 而单维占优模型则认为决策者把“结果”维度上的差异和“延迟”维度上的差异进行比较(维度间差异比较), 然后根据差异更大的维度(即占优势的维度)进行决策。跨期选择众理论之争无果的原因之一或是研究者未能找到揭示其心理过程的令人信服的证据。为此, 本研究采用过程检验而非结果拟合的方法, 首创了“直观模拟天平”任务, 对维度间差异比较的大小进行测量, 从而为验证单维占优模型提供了支持性的证据。实验1A证明了维度间差异大小中介了选择结果, 被试认为“结果”维度上的差异相对于“延迟”维度上的差异越大, 越倾向于选择延迟长、结果大的选项; 反之, 被试认为“延迟”维度上的差异相对于“结果”维度上的差异越大, 越倾向于选择延迟短、结果小的选项。实验1B证明了维度间差异比较是在选择时实时(而不是在选择后)进行的。实验1C通过调整实验程序消减了共同方法偏差对结果的影响。实验2进一步证明维度间差异比较可以解释众多的跨期选择异象。通过4个实验, 本研究揭示了维度间差异比较构成了跨期选择的重要(尽管可能不是唯一)决策过程, 为支持单维占优模型提供了重要的过程验证证据。     

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。     

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.     

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。     

On Being Found: How Habitual Patterns of Thought Influence Creative Interest,Behavior, and Ability

This study on 138 undergraduate students used path analysis to investigate the relationship between creativity (interest, measured by a creative activities survey; and ability: fluency, originality, and elaboration) and different aspects of thought patterns presumed to influence the preparation and illumination phase of the creative process: habitual patterns of thought (ruminative brooding, ruminative self-reflection), thought suppression, thought intrusion, mind wandering, and associative ability. Such relationship was hinted at in Wallas’s classical model of the creative sequence, but is rarely investigated. We found that creative behavior/interest was driven by self-reflection, thought intrusion, and the lack of a need for thought suppression; creative ability was fueled mainly by associative ability. The only variable that influenced both aspects of creativity was the lack of resistance to thought suppression; this distinguished the creativity variables from dysphoria, which was associated with a desire for thought suppression. The results suggest that what drives the need to create is not creative ability per se, but rather self-focused attention, as well as the feeling or experience of being found by thought, rather than finding it. That is, the need to create is associated with having thoughts that interrupt one’s ordinary stream of consciousness and that are seen as welcome rather than interfering.