Familias Unidas’ Crossover Effects on Suicidal Behaviors among Hispanic Adolescents: Results from an Effectiveness Trial

The long‐term impact of Familias Unidas on suicidal behaviors among Hispanic 8th graders (N = 746) was examined along with parent–adolescent communication as a moderator of intervention effectiveness. At baseline, 9.2% (95% CI = 7.3%–11.6%) of adolescents reported suicide ideation and 5.7% (95% CI = 4.1%–7.7%) reported a past year suicide attempt. There were no significant intervention effects on suicidal behaviors; however, parent–adolescent communication was a moderator of suicide attempts in the past year, across the intervention (b = ?.01, p = .01). Results suggest that Familias Unidas reduces suicidal behaviors among Hispanic adolescents with low levels of parent–adolescent communication despite no suicide‐specific intervention content. Implications of these findings are discussed.     

数目及其呈现方式在不同范围内对时间知觉的影响

采用时间比较法,考察数目及其呈现方式对时间知觉的影响。任务中,首先呈现标准时距,此时变化刺激的数目或呈现方式,然后呈现比较时距,被试的任务是判断比较时距比标准时距长还是短。实验1和实验2分别采用1秒内和1秒以上的时间,结果发现： 1秒内的时间加工中,刺激一个一个连续呈现时,小数引起标准时距的低估,大数导致标准时距的高估,而同时呈现时,刺激的数目对知觉到的时间无影响;数目及其呈现方式影响计时的准确性,而对计时的变异性和敏感性无影响。时间在1秒以上,同时呈现和连续呈现条件下,刺激的数目影响知觉到的时间;计时的     

论儒家的自强人格及其培养

从心理学角度看,儒学主要体现了一种自强的人格心理学思想。仁爱人格、弘毅人格和革新人格是儒家自强人格的主要内容,其终极目标则是养成健全人格。儒家的自强人格既具有个人取向,也具有社会取向。它以个人取向为基础和出发点,以社会取向为最终目标,从个人取向逐渐扩展至社会取向。实践学习、克服偏私、正确处理理情关系和人际关系是培养儒家自强人格的4个主要途径     

不同时距条件下面孔表情知觉的时间整合效应

通过把面孔表情分割成三部分,按照不同的时间间隔以及不同的呈现时间相继呈现,考察了被试对面孔表情的时间整合效果,以此探讨时间整合的加工过程和影响因素。结果发现：（1）面孔表情的时间整合效果受时间结构和刺激材料的影响。（2）分离呈现的面孔表情能否进行时间整合与SOA的大小有关。（3）面孔表情的时间整合存在类型差异。（4）面孔表情的时间整合是在一个有限的视觉缓冲器内进行的,图像记忆和长时记忆与面孔表情的时间整合过程关系密切。     

小学四、五年级数学诊断性测验的编制——基于规则空间模型的方法

基于规则空间模型, 以小学四、五年级数学诊断性测验的编制为例, 探索了认知诊断理论背景下诊断性测验的编制方法。研究发现, 基于规则空间模型编制的诊断性测验具备优良的信效度, 尤其在结构效度上具有突出优势。应用该测验对1059名四、五年级学生进行诊断测验的结果显示:在整体上, 学生对整数、初级运算与应用掌握得较为巩固, 对量、统计、规律、高级运算掌握较差;在发展趋势上, 量、统计、规律、高级运算是四、五年级之间进步最快的属性。     

爬行经验对婴儿迂回行为发展的影响

该研究旨在探讨个体早期第一种自主位移动作－爬行的经验对迂回行为这一重要空间认知能力发展的和用及其特点。     

A penalized likelihood method for multi-group structural equation modelling

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.     

Which types of income matter most for well‐being in China: Absolute,relative or income aspirations?

China has experienced remarkable economic growth, but that has not been associated with increases in individual well‐being, implying that well‐being may not be sensitive to increases in absolute income (AI). Thus, the relationship between well‐being and other types of income is worth investigating. This study employed a national sample of 5471 respondents from 31 provinces of China (excluding Hong Kong, Macao and Taiwan) to compare the effects of AI, relative income (RI) and income aspirations (IA) on individual well‐being in China. The results of a hierarchical regression analysis found that, compared to AI and RI, IA explained more variation in well‐being. These findings highlight the importance of IA for well‐being among the Chinese.     

Altered oscillatory brain dynamics of emotional processing in young binge drinkers

Heavy episodic drinking, also termed binge drinking, is commonly practiced by young adults. It is accompanied by a range of cognitive, affective, and social problems, but the neural dynamics underlying changes in emotional functions is poorly understood. To investigate the behavioral and brain indices of affective processing as a function of binge drinking, young, healthy participants (23.3 ± 3.3 years) were assigned to two groups (n = 32 each) based on their drinking habits. Binge drinking (BD) participants reported drinking heavily with at least five binge episodes in the last 6 months, whereas light drinkers (LD) reported no more than one binge episode in the last 6 months. Participants provided subjective ratings of emotionally evocative images with negative, positive, erotic, and neutral themes mostly selected from the International Affective Picture System (IAPS). Electroencephalography (EEG) signal was recorded with a 64-channel system and analyzed in theta frequency band (4–7 Hz) with Morlet wavelets. Subjective ratings of the IAPS pictures were equivalent across both groups. However, affective modulation of event-related theta power both during early appraisal and later integrative processing stages was attenuated in BD, particularly those engaging in high-intensity drinking. These findings suggest that binge drinking is associated with altered neurophysiological indices of affective functions that are reflected in lower theta responsivity to emotions. The blunted long-range cortico-cortical and corticolimbic integration is consistent with compromised affective functions in alcohol use disorder. These findings may have implications for diagnostic and intervention strategies in heavy alcohol users.