智力和元认知技能在发现学习中的作用:任务复杂性的影响

采用自编的计算机模拟发现学习任务,以49名大学生为被试,探讨了在简单和复杂发现学习任务中,智力、元认知技能和学习成绩三者之间的关系。结果发现:在简单任务中,三者关系基本符合混合模型;进行过多的元认知活动,并不能提高学习效果。在复杂任务中,智力对学习成绩起主要预测作用。随着任务复杂性的增加,智力对学习的影响也随之变化,符合难度阈限理论的假设。任务复杂性不同时,智力、元认知技能和发现学习成绩之间的关系也不同,在一定程度上验证了难度阈限扩展理论。     

Hand dominance influences the processing of observed bodies

In motor tasks, subgroups of lefthanders have been shown to differ in the distribution of attention about their own bodies. The present experiment examined whether similar attentional biases also apply when processing observed bodies. Sixteen right handers (RHs), 22 consistent left handers (CLHs) and 11 relatively ambidextrous inconsistent left handers (ILHs) performed an own body transformation task in which they were instructed to make speeded left–right judgements about a schematic human figure. Attentional biases associated with handedness were found to extend to observed bodies: CLHs’ judgements were faster to the figure’s left side, while ILHs, like RHs, showed facilitated performance to the figure’s right side. These results demonstrate a novel embodiment effect whereby the processing of a static schematic human figure is modulated by an individual’s personal motor capabilities. This finding suggests that motor simulation may contribute to whole body perception in the absence of actual or implied actions.     

计算机化自适应测验中原始题项目参数的估计

计算机化自适应测验(Computerized Adaptive Testing, 简称CAT)其安全性面临着新的挑战, 小题库的安全更受威胁。如何建设一个大型、优质的题库成为CAT研究中一个非常重要的课题。目前CAT题库的建设存在一些问题, 如成本高且保密性较差。尤其是等值技术较复杂且锚题重复使用容易造成泄露。如能在实施CAT过程中插入未经过参数估计的项目(原始题), 同时对原始题项目参数进行估计, 这对建设大型、优质的CAT题库来说其意义是不言而喻的。本文基于1PLM和2PLM对此进行研究, 提出了原始题在线估计的新方法以及推导出了求区分度参数a迭代初值的计算公式。研究结果表明：无论是模拟研究还是实证研究, 原始题被作答的次数对项目参数估计结果都会产生不同的影响, 并且原始题作答人数越多项目参数估计精度也越高。     

时间隐喻在时刻水平上的表盘模拟表征

探讨时间隐喻在时刻水平上的表盘模拟表征。包括两个实验,实验1考察静态的时刻表征是否依赖表盘模拟,对时针和分针的表征是否会有差异;实验2通过加工不同的时刻变化及对旋转图形的判断,考察动态时刻的加工是否产生表盘指针的旋转模拟。结果表明,具体时刻的时间隐喻模式表现为表盘模拟,不同于年、月、日等时间的直线型的时间隐喻;加工变化的时刻是以心理旋转的方式进行的,这种加工方式会影响空间知觉。     

语言理解中情绪的动态模拟

采用混合实验设计, 句子-图片匹配范式和动态过程再现范式, 以包含静态情绪和动态变化情绪信息的句子为实验材料, 以被试对图片的反应时间为因变量指标, 通过不同时间间隔的设置以及不同的实验程序, 探讨了语言理解中静态情绪和动态情绪的心理模拟及其时间进程。结果表明：(1)被试对情绪的心理模拟与句中的语言描述一致, 情绪语境能够激活被试的经验模拟。(2)被试在句子理解加工的初期就能迅速、即时地模拟静态情绪信息, 且这种模拟具有一定的稳定性和持久性。(3)被试可以对动态变化的情绪进行动态的心理模拟, 不过这种模拟所需的时间要长, 是在句子理解加工的后期完成的。被试对不同情绪变化方向的句子模拟受到原情绪状态的影响, 当阅读由消极情绪向积极情绪变化的句子时, 由于被试固着于原来的消极情绪而发生了模拟积极情绪的困难。     

语言理解中的动作知觉：基于具身认知的视角

具身认知理论是目前认知科学领域中最新的研究范式和取向, 它认为人的认知过程依赖于感知和动作系统, 强调身体在认知中发挥重要作用。具身语言理解则认为, 身体、动作和知觉系统在语言认知中也起着不可或缺的作用。文章在简要回顾具身语言理解的相关理论如索引假设、浸入式经历者框架、语言神经理论的基础上, 重点从语言理解的四个层面的实证研究, 即音位、单词、句子、语篇, 证实具身语言理解的观点。未来的研究应该着眼于用具身认知观点来解释更高级的抽象语言表征, 具身单词、句子、语篇的具体表征形式, 并利用具身语言认知的观点进行认知的本土化研究。     

AN ANALYTIC FORM FOR THE INTERRESPONSE TIME ANALYSIS OF SHULL, GAYNOR, AND GRIMES WITH APPLICATIONS AND EXTENSIONS

Shull, Gaynor and Grimes advanced a model for interresponse time distribution using probabilistic cycling between a higher-rate and a lower-rate response process. Both response processes are assumed to be random in time with a constant rate. The cycling between the two processes is assumed to have a constant transition probability that is independent of bout length. This report develops an analytic form of the model which has a natural parametrization for a higher-rate within-bout responding and a lower-rate visit-initiation responding. The analytic form provides a convenient basis for both a nonlinear least-squares data reduction technique to estimate the model's parameters and Monte Carlo simulations of the model. In addition, the analytic formulation is extended to both a refractory period for the rats' behavior and, separately, the strongly-banded behavior seen with pigeons.     

Bayesian Hierarchical Classes Analysis

Hierarchical classes models are models for N-way N-mode data that represent the association among the N modes and simultaneously yield, for each mode, a hierarchical classification of its elements. In this paper we present a stochastic extension of the hierarchical classes model for two-way two-mode binary data. In line with the original model, the new probabilistic extension still represents both the association among the two modes and the hierarchical classifications. A fully Bayesian method for fitting the new model is presented and evaluated in a simulation study. Furthermore, we propose tools for model selection and model checking based on Bayes factors and posterior predictive checks. We illustrate the advantages of the new approach with applications in the domain of the psychology of choice and psychiatric diagnosis. Iwin Leenen is now at the Instituto Mexicano de Investigación de Familia y Población (IMIFAP), Mexico. The research reported in this paper was partially supported by the Spanish Ministerio de Educación y Ciencia (programa Ramón y Cajal) and by the Research Council of K.U.Leuven (PDM/99/037, GOA/2000/02, and GOA/2005/04). The authors are grateful to Johannes Berkhof for fruitful discussions.     

人工神经网络模型在心理学研究中的运用

人工神经网络是对真实神经元网络的粗略模拟,具有容错性、抗干扰性、高效性等特征,具有学习、记忆等强大的认知功能,因此适宜于心理学许多领域的模拟研究。借助神经网络模型不仅可以模拟和验证以往的实验室研究;还能深入探讨感知、联想、学习和记忆等认知过程的具体发生机理,把握其内在机制;它对探讨内隐记忆、内隐学习等无意识认知过程有极大的启发作用。     

检验导出的等值新方法及其表现探讨

该文受Berkson将检验方法用于估计未知参数的启发,根据三个拟合优度统计量导出三种新的求取等值系数的方法,即:平方根等值方法(Square Root criterion,SQRTcrit)、对称相对熵等值方法(Symmetric Relative Entropy criterion,SREcrit)、加权等值方法(Weighted criterion,Wcrit),即Haebara准则的加权式。虽然在被检验的两个分布列很接近时,这三个多项拟合优度检验方法是渐近等价的,然而用它们求取等值系数时,Monte-Carlo模拟结果表明这三种新等值方法的行为表现存在差异。它们之间的差异和随机误差的大小有密切关系,即与项目参数估计的精度有关;还与等值系数A的范围有关。