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。     

试论《牟子理惑论》之真伪

《高僧传》、《祐录》等佛教文献记载佛法初传历史,依据的是《牟子理惑论》。《牟子理惑论》甚至影响了在南方翻译的佛经。汉末交州,社会安定,儒学盛行,学术自由,与海外交往密切,从而为佛教传播提供了条件,但也抑制了佛教传播范围和程度的进一步扩大,这是《牟子理惑论》创作的历史背景。《牟子理惑论》首先译Buddha为“佛”,并在中国南方介绍了佛“相好”制。     

《道德经》和谐思想的四个进阶

和谐,是中国文化中的重要理念。古代的思想家们对这一理念多有发明,形成了源远流长的和谐思想,深深渗透进了中国人的观念与生活中。《道德经》对和谐的阐发别开生面,处处流露着     

从"平均"到"公正"的嬗变——中国公正观的价值维度转换

历史上的每一种公正观,都有它的核心价值指向,它们构成了公正思想的价值维度,引导着社会的基本治理方向和利益分配方式.中国社会的公正观经历了一个从"平均"到"公正"的发展过程.长期以来,它是以平均主义为核心价值取向而展开的,并因此而深刻地影响着中国社会.面对当今中国日趋扩大的利益分配差距,一种以公平正义为价值指向的公正观应运而生.公平正义的公正观是具有现实合理性和价值优越性的新型公正观.     

贫困与反贫困：心理学的研究

局部繁荣下的贫困问题已成为当今世界面临的最大挑战,理解贫困以及与贫困有关的心理模型和决策过程、并促进人类的幸福与公正,是心理学家的一项重要使命。文章旨在阐明心理学在理解贫困和减贫工作中的角色,并对贫困心理学的研究思路、贫困归因的测量方法及其影响因素作一介绍。最后,在总结成就与问题的基础上,建议未来的贫困心理学应对特殊的目标群体做更多关注,并发展更可靠的测量方法,挖掘更多影响贫困心理的人格、社会因素     

关于学习环模式的研究综述

学习环模式是一种有效的科学学习与教学模式。西方科学教育工作者对此做了大量研究,取得了丰硕成果。本文综述了学习环模式的理论基础、基本结构与发展。这一模式对于我国当前中小学科学课程设计和探究性学习的开展有一定的借鉴意义。     

合作数学问题解决与心理模型建构关系研究

合作学习研究者很早就开始关注合作互动中解释的作用。Chi关于自我解释的研究表明解释是一种非常有效果的学习现象,能够起到修补和建立学习者的心理模型的作用。本研究以28名高一学生为被试,采取了两种程序的合作学习任务,研究中虽然没有发现两种程序在问题解决结果上的显著差异,但是却表现出在心理模型建构方面互动效果上的差异,或表现为同等效果下的互动次数有显著性差异。     

Causal attribution for interfirm contract violation: a comparative study of Chinese and American commercial arbitrators

In this study, the authors examined differences between Chinese and American commercial arbitrators. They predicted and found that Chinese arbitrators make higher awards for interfirm contract violations than Americans. This difference is partially explained by differences in attributions. Prior theory suggests, and the authors found, that the Chinese tend to have more internal attributions for events when observing group actions. When evidence provided to arbitrators is mixed (evidence is provided for both internal and external attributions), Chinese-American differences in awards become even stronger.     

A tutoring package to teach pronunciation of Mandarin Chinese characters

We examined the effects of a tutoring package (verbal modeling, prompts, and contingent praise/ Chinese conversations with the tutor) on the performance of a college student's Mandarin Chinese pronunciation. The effects of the tutoring package were analyzed using a multiple baseline design across two sets of 50 Chinese characters. The tutoring package produced improvement in the student's correct pronunciation of Chinese characters from 48% (pretutoring) to 90% (posttutoring). Results suggested that the tutoring package produced mastery pronunciation of targeted Mandarin Chinese vocalizations by a nonnative speaker.     

Feature extraction via KPCA for classification of gait patterns

Automated recognition of gait pattern change is important in medical diagnostics as well as in the early identification of at-risk gait in the elderly. We evaluated the use of Kernel-based Principal Component Analysis (KPCA) to extract more gait features (i.e., to obtain more significant amounts of information about human movement) and thus to improve the classification of gait patterns. 3D gait data of 24 young and 24 elderly participants were acquired using an OPTOTRAK 3020 motion analysis system during normal walking, and a total of 36 gait spatio-temporal and kinematic variables were extracted from the recorded data. KPCA was used first for nonlinear feature extraction to then evaluate its effect on a subsequent classification in combination with learning algorithms such as support vector machines (SVMs). Cross-validation test results indicated that the proposed technique could allow spreading the information about the gait's kinematic structure into more nonlinear principal components, thus providing additional discriminatory information for the improvement of gait classification performance. The feature extraction ability of KPCA was affected slightly with different kernel functions as polynomial and radial basis function. The combination of KPCA and SVM could identify young-elderly gait patterns with 91% accuracy, resulting in a markedly improved performance compared to the combination of PCA and SVM. These results suggest that nonlinear feature extraction by KPCA improves the classification of young-elderly gait patterns, and holds considerable potential for future applications in direct dimensionality reduction and interpretation of multiple gait signals.