工作记忆中有意抑制能力的发展研究

使用Reed设计的有意遗忘研究范式,以194名小学二、三、四、五年级学生和大学生为被试,考察不同年龄阶段被试工作记忆中有意抑制能力的差异,探索抑制机制随年龄增长的发展趋势及儿童工作记忆中有意抑制能力发展的敏感期。结果表明：1）大学生被试和儿童被试之间的有意抑制能力存在显著性差异;2）随年龄的增长,工作记忆中有意抑制能力呈递增趋势;3）儿童的有意抑制能力发展在三年级,9、10岁可能存在一个敏感期。     

Sense of home buffers threats to the self

We hypothesised that sense of home serves as a buffer against self‐threat. In three experiments, we induced sense of home (vs. control) and assessed its buffering function to threat. Sense of home augmented openness to stereotype threat information (Experiment 1), increased performance on a spatial rotation ability test after stereotype threat (Experiment 2), and reduced the desire for high‐status products following mortality salience (Experiment 3). This “proof of concept” research establishes the buffering potential of sense of home and raises theoretical and practical implications.     

The Effect of Conflict Goals on Avoidance Strategies: What Does Not Communicating Communicate?

Avoidance is proposed to be a goal‐directed behavior rather than a behavior that reflects passivity or inaction. To evaluate this proposition, a typology of conflict goals and a typology of conflict avoidance strategies are created, and the relationship between nonavoidance strategies and the elements of these 2 typologies are evaluated within a structural equation model (N = 352). Findings show that, except for pretending and withdrawal, conflict goals successfully predict avoidance strategies, thereby affirming their strategic importance. The article concludes by addressing areas for future research, including modifications and refinement of the typologies and proposing other strategies and goals that may be theoretically significant.     

High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm

A Metropolis–Hastings Robbins–Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed. The sequence of estimates from the MH-RM algorithm converges with probability one to the maximum likelihood solution. Details on the computer implementation of this algorithm are provided. The accuracy of the proposed algorithm is demonstrated with simulations. As an illustration, the proposed algorithm is applied to explore the factor structure underlying a new quality of life scale for children. It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm. Extensions of the algorithm to other modeling frameworks are discussed.     

集体所有权的层次及其法律政策选择

集体和集体所有权是一个问题的两个方面,明确了集体的含义也就明确了集体所有权的含义。应当区分集体所有权在宪法、物权法与企业法上的不同含义。宪法意义上的集体所有权构成集体法律制度设计的强制性限制条件。物权法意义的集体所有权应当进行法人所有权的法律构成。集体土地可以通过多种多样的用益物权实现民事主体的独立性和民事权利的有效流转。企业法意义的集体所有权则应当给当事人的意思自治留下充分的空间。     

四川凉山彝族的"尼"与我国北方萨满的传承比较研究

凉山彝族的宗教和我国北方萨满教都属于原始宗教,凉山彝族的宗教职业者之一尼与北方萨满在传承上有许多相同之处,但由于各自社会发展的差异性,因此也带来了各自的区别与特色.     

Gain or non‐loss: The message matching effect of regulatory focus on moral judgements of other‐orientation lies

Previous studies found that individuals with promotion focus are more likely to be persuaded by messages framed in terms of gain‐related words; individuals with prevention focus are more likely to be persuaded by messages framed in terms of loss‐related words. This is known as the message matching effect of regulatory focus. The present study extended this effect into the field of moral judgement of other‐orientation lies. Two experiments were conducted, revealing that (a) individuals with promotion focus judged gain‐framed other‐orientation lies to be more moral, while individuals with prevention focus judged non‐loss‐framed other‐orientation lies to be more moral; and (b) the subjective processing fluency had a partial mediating role in the message matching effect. Theoretical implications and future research directions were discussed.     

Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data

Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities. However, the existence of outliers or influential points may misidentify the hidden states and distort the associated inference. In this study, we develop a Bayesian local influence procedure for HMMs with latent variables in the presence of missing data. The proposed model enables us to investigate the dynamic heterogeneity of multivariate longitudinal data, reveal how the interrelationships among latent variables change from one state to another, and simultaneously conduct statistical diagnosis for the given data, model assumptions, and prior inputs. We apply the proposed procedure to analyze a dataset collected by the UCLA center for advancing longitudinal drug abuse research. Several outliers or influential points that seriously influence estimation results are identified and removed. The proposed procedure also discovers the effects of treatment and individuals’ psychological problems on cocaine use behavior and delineates their dynamic changes across the cocaine-addiction states.