Gating范式：探讨听觉词汇加工的途径

传统的反应时任务只能为听觉词汇加工提供定量数据,而不能提供定性信息,Gating范式的优势在于可以提供以上两种信息,使研究者能够全面、细致地了解听觉词汇加工的即时过程。而且它的适用群体广泛,可被用于研究不同年龄段的发展。文章较详细地介绍了Gating范式的基本特点及其在汉语研究中的应用,期待它被更多的研究者所认识和使用。     

Chinese Internet addiction inventory: developing a measure of problematic Internet use for Chinese college students.

The current study developed a psychometrically sound multidimensional measure of Internet addiction: the Chinese Internet Addiction Inventory (CIAI). Data were collected from 1,029 Chinese undergraduate students from 14 universities and colleges. The initial sample was split randomly into two samples (N1=516; N2=513). An exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) were conducted on the two samples respectively. Findings from the EFA suggest that this measure assesses three dimensions of Internet addiction: conflicts, mood modification, and dependence. Items in each dimension showed high internal consistency and acceptable test-retest reliability. Findings from the CFA further confirmed the three-factor measurement structure of CIAI. Test of criterion-related validity also showed good abilities for all three CIAI subscales to discriminate between an Internet addictive group and non-Internet addictive group. The theoretical and clinical implications of CIAI and its limitations are discussed.     

多面体的一面:第二语言学习的社会化过程

第二语言学习过程是社会互动情景中的言语活动,"热"的社会化过程是第二语言学习过程的有机组成部分;形成第二语言情景下的文化心理参照系认知能力和文化适应能力是第二语言的言语能力重要内容;第二语言学习过程中的言语变异现象部分源于学习者的社会心理结构特征。     

基于原型特征因果关系的归纳推理研究

两个实验探讨新特征与类别原型特征之间存在因果关系时,因果关系对归纳推理的影响.实验1探讨不同典型性条件下,新特征与原型特征间的因果关系对归纳推理的影响.实验2探讨不同多样性条件下,新特征与原型特征间的因果关系对归纳推理的影响.研究结果表明,当新特征与原型特征之间存在因果关系时,因果关系会影响归纳推理的力度,但样例典型性和多样性仍是影响人们归纳推理的重要因素.     

小学4～6年级儿童数学应用题表征模型的使用研究

该研究考察了小学4～6年级学生在数学应用题中理解阶段和执行阶段使用表征模型的情况,研究运用实验法对某普通小学的158名4～6年级学生进行了应用题改写任务和缩写任务.结果表明：（1）学生在理解阶段更多地选择情境条件,使用情境模型来表征问题;在执行阶段较少选择情境条件,转而使用问题模型来表征问题;（2）随着年级的升高,相对于优等生来说,其他学生在缩写任务中对情境条件的选择数量越来越少,而优等生在这方面一直都做得很好.差等生识别多余条件的能力并没有随着年级的升高而发生改变,而优等生却在不断增强.     

Magnetic-field dependence of nucleation undercoolings in non-magnetic metallic melts

The nucleation undercoolings of non-magnetic metals like paramagnetic aluminium in high magnetic fields were measured by the differential thermal analysis technique. It was shown that the nucleation undercooling of pure aluminium increased with increasing the magnetic field, while its melting temperature was hardly changed. Based on the model of magnetic dipoles at the interface, it is proposed that the magnetic-field-induced interfacial energy mainly contributes to the increase in undercooling. The change in undercooling in the magnetic field is calculated theoretically, which is in comparison with experimental data. Additionally, the inhibition of atom diffusion in the magnetic field plays a role in the change of undercooling.     

Is the Lateralized Categorical Perception of Color a Situational Effect of Language on Color Perception?

This study investigated whether and how a person's varied series of lexical categories corresponding to different discriminatory characteristics of the same colors affect his or her perception of colors. In three experiments, Chinese participants were primed to categorize four graduated colors—specifically dark green, light green, light blue, and dark blue—into green and blue; light color and dark color; and dark green, light green, light blue, and dark blue. The participants were then required to complete a visual search task. Reaction times in the visual search task indicated that different lateralized categorical perceptions (CPs) of color corresponded to the various priming situations. These results suggest that all of the lexical categories corresponding to different discriminatory characteristics of the same colors can influence people's perceptions of colors and that color perceptions can be influenced differently by distinct types of lexical categories depending on the context.     

SenseCam reminiscence and action recall in memory-unimpaired people

Case studies of memory-impaired individuals consistently show that reminiscing with SenseCam images enhances event recall. This exploratory study examined whether a similar benefit would occur for the consolidation of memories in memory-unimpaired people. We tested delayed recall for atypical actions observed on a lengthy walk. Participants used SenseCam, a diary, or no external memory aid while walking, followed by reminiscence with SenseCam images, diary entries, or no aid, either alone (self-reminiscence) or with the experimenter (social reminiscence). One week later, when tested without SenseCam images or diary entries, prior social reminiscence produced greater recall than self-reminiscence, but there were no differences between memory aid conditions for action free recall or action order recall. When methodological variables were controlled, there was no recall advantage for SenseCam reminiscence with memory-unimpaired participants. The case studies and present study differ in multiple ways, making direct comparisons problematic. SenseCam is a valuable aid to the memory impaired, but its mnemonic value for non-clinical populations remains to be determined.     

Bias and Efficiency in Structural Equation Modeling: Maximum Likelihood Versus Robust Methods

In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data, in practice, can make the ML estimator (MLE) biased and inefficient. In addition to ML, robust methods have also been developed, which are designed to minimize the effects of outlying cases. But the properties of robust estimates and their standard errors (SEs) have never been systematically studied. This article studies two robust methods and compares them against the ML method with respect to bias and efficiency using a confirmatory factor model. Simulation results show that robust methods lead to results comparable with ML when data are normally distributed. When data have heavy tails or outlying cases, robust methods lead to less biased and more efficient estimators than MLEs. A formula to obtain consistent SEs for one of the robust methods is also developed. The formula-based SEs for both robust estimators match the empirical SEs very well with medium-size samples. A sample of the Cross Racial Identity Scale with a 6-factor model is used for illustration. Results also confirm conclusions of the simulation study.