面孔审美加工的神经机制及个体差异

外显和内隐的面孔审美加工的脑成像研究表明,美的面孔比不美的面孔导致眶额皮层、伏隔核、杏仁核等奖赏脑区更大的激活。脑电研究则发现了与面孔审美加工有关的早期负波和晚期正成分。面孔审美加工有关的脑区活动也受到性别、生理周期等个体因素的调节。未来的研究需要比较美的面孔与其它奖赏刺激加工的神经机制,探讨面孔审美加工的不同阶段及时间进程,在面孔知觉的框架下形成合理的面孔审美加工的神经模型。     

青少年核心信念挑战对创伤后成长的影响：反刍与社会支持的作用

采用核心信念问卷、反刍问卷、社会支持问卷和创伤后成长问卷, 以汶川地震4年半后的354名中学生为被试, 考察其核心信念挑战、侵入性反刍、主动反刍、社会支持与创伤后成长之间的关系。结果发现, 地震4年半后青少年创伤后成长的整体水平较高, 其中女生高于男生, 初二学生相对低于高年级学生。结构方程模型的结果显示, 侵入性反刍和主动反刍在核心信念挑战与创伤后成长之间起部分中介作用, 核心信念挑战可以直接正向预测创伤后成长, 也可以通过侵入性反刍对创伤后成长起负向预测作用、通过主动反刍对创伤后成长起正向预测作用、通过侵入性反刍经主动反刍正向预测创伤后成长; 社会支持在侵入性反刍影响创伤后成长的直接路径上不起调节作用, 但在侵入性反刍经主动反刍对创伤后成长的间接路径上起调节作用, 具体表现为侵入性反刍对主动反刍的正向预测作用随着社会支持的增加而降低。     

音乐诱发情绪测量及其影响因素*

使用音乐诱发情绪是有效且特殊的诱发方式。本文对音乐诱发情绪的自我报告测量、心理生理测量和功能神经成像测量进行了系统的梳理,并阐述了一种基于音乐特定模型的情绪测量方法。文章从音乐刺激的选择、听者特征以及情境特征三方面对影响音乐情绪测量的因素进行了总结。并为保证测量的准确性进一步提出确定理论模型一致性、结合多元方法、利用有效刺激材料、认真考评情境和参与者特征等解决思路,并阐明了此领域的研究趋势。     

比较基因组学研究与应用中的辩证思维

比较基因组学是人类基因组计划的重要组成部分.它利用人类基因组与模式生物基因组之间编码顺序上和组织结构上的同源性,发现和克隆人类基因,揭示基因功能,从而阐明物种的进化关系及基因组的内在结构.比较基因组学的创立、内容及应用都充分体现着辩证思维的哲学观点.     

经学视野下的儒学三形态

文章认为经学的发展历程对儒学的发展影响极大,政治儒学、心性儒学和世俗化儒学三种形态都与经学历史之间存在着紧密的联系,它们都以儒家经典作为自己最高的文本依据,以经典解释学的方式表现自身,经学的转型也意味着儒学发展的新方向,经学的发展史与三种形态儒学的发展之间存在着互动。     

褚伯玉与《太平经》

本文通过对有关<太平经>在南齐流传的一条资料进行考辨,旨在说明<上清道类事相>和<南岳总胜集>关于南朝隐士褚伯玉曾读过<太平经>的记载是不可信的.因此,一些以此为据来说明<太平经>在南齐时流传情况的论著也就是站不住脚的.但是,从文献的蛛丝马迹中,似乎又让人觉得褚伯玉与<太平经>有很多关联.文章以文献梳理为线索,试图展示其中细微之处,为确定<太平经>的成书年代和了解南朝<太平经>流传情况提供一些帮助.     

大学生孤独感结构特点的初步研究

采用自编的大学生孤独感结构问卷,对大学生孤独感结构的特点进行了初步研究,结果表明：大学生孤独感与其自我同一性发展有关系;大学生在孤独感结构问卷的各维度和总分上的得分存在性别、生源、学科门类、学历层次、年级、学校性质等人口学变量上的差异,但差异的表现是复杂多样的;大学生孤独感并不总是消极的,更多的大学生认为适度的孤独感可以为其提供动力,因而也具有积极意义。     

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.     

Ambiguity Attitudes over Time

Previous research demonstrates that individuals exhibit a stronger level of ambiguity aversion for high probabilities than for low probabilities. Given that risky and ambiguous prospects are often unresolved until a future date (e.g., investment decisions, new product launches, and medical interventions), this study examines the impact of time on ambiguity preferences at different probability levels. Our experimental results indicate that although ambiguity preferences for low‐probability events remain constant, a robust effect of time occurs for high‐probability events. More specifically, temporal distance mitigates ambiguity aversion. This effect is consistent for different elicitation methods (preference rating and probability–ambiguity trade‐off tasks). We propose a dual‐process model (affective versus cognitive processing styles) to explain our results. Affective processing for high‐probability lotteries resolved in the current period increases ambiguity aversion, while cognitive processing leads to less ambiguity‐averse choices for future lotteries. Copyright © 2015 John Wiley & Sons, Ltd.