试论荀子哲学的特质及其对儒家道统之意义

荀子哲学作为一个经验论的形态,为儒学的发展开启了一个新的哲学范式。就儒学而言,荀学经验论所带来的并不是对“道”的离弃和背叛,而是学问进路与方法的创新。从“道统”的角度说,荀学与孟学的关系是:“道”同而“术”不同。     

A new rotational method in factor analysis

A new rotational method, particularly appropriate when a positive manifold can be assumed, is presented in theory and in computational detail. It is applied to a five-dimensional factorial matrix in which a simple configuration is known to exist. The new technique is much more efficient in yielding the simple configuration than are the methods formerly used. Landahl's transformations can be combined with this method to advantage.     

人脑的奥秘能否由人脑自身揭示--浅谈神经科学现代研究方法

神经科学研究是学科、多层次的。神经科学的每一次飞跃几乎都与某种研究方法的出现有关。从形态学的束路追踪法、组织化学法、原位杂交法、其聚焦激光扫描显微镜,到生理学的脑内微透析术、脑片、微电泳及行为学方法;从电生理的电压钳、膜片和脑电波,到生物化学的层析法、放射免疫法、免疫印迹法等;从种类繁多的分子生物学方法到各种脑成像技术。每种新方法的出现都打开了一扇门,引入一个新领域。实践证明,人脑即使不能彻底了解人脑的奥秘,也可无限地逼近。     

A blueprint of ELI: A new method for eliciting subjective probability distributions

A blueprint of a new method for eliciting uncertain knowledge about continuous quantities is presented. The direct realization of a proper scoring rule in a graphically oriented interactive computer program is one of the central features of the new elicitation methodology. Uncertain knowledge is internally represented through subjective probability distributions. However, in its interaction with assessors, the elicitation method uses a score representation. A proper scoring rule is applied to transform probability density functions into score functions. In order to study its merits, central ideas for the new method were implemented in an experimental version of the elicitation technique ELI. The results were promising and encouraged further development of the technique.     

Refining distraction potential testing guidelines by considering differences in glancing behavior

Driver distraction is a recognized cause of traffic accidents. Although the well-known guidelines for measuring distraction of secondary in-car tasks were published by the United States National Highway Traffic Safety Administration (NHTSA) in 2013, studies have raised concerns on the accuracy of the method defined in the guidelines, namely criticizing them for basing the diversity of the driver sample on driver age, and for inconsistent between-group results. In fact, it was recently discovered that the NHTSA driving simulator test is susceptible to rather fortuitous results when the participant sample is randomized. This suggests that the results of said test are highly dependent on the selected participants, rather than on the phenomenon being studied, for example, the effects of touch screen size on driver distraction. As an attempt to refine the current guidelines, we set out to study whether a previously proposed new testing method is as susceptible to the effects of participant randomization as the NHTSA method. This new testing method differs from the NHTSA method by two major accounts. First, the new method considers occlusion distance (i.e., how far a driver can drive with their vision covered) rather than age, and second, the new method considers driving in a more complex, and arguably, a more realistic environment than proposed in the NHTSA guidelines. Our results imply that the new method is less susceptible to sample randomization, and that occlusion distance appears a more robust criterion for driver sampling than merely driver age. Our results are applicable in further developing driver distraction guidelines and provide empirical evidence on the effect of individual differences in drivers’ glancing behavior.     

A synthetic validity approach to testing differential prediction hypotheses

A new method is presented for conducting differential prediction analyses that makes it possible to test differential prediction hypotheses with adequate statistical power even when the sample size within a job or a job family is very small. This method, called synthetic differential prediction analysis, represents an application of the logic of synthetic validation to differential prediction analyses. The authors explain this new method and describe its application in a selection-system validation study conducted in a large organization.     

A matching method of successive approximations and its instrumentation

The present paper reports a method of successive approximations. The technique is optimal for matching stimuli with respect to time and all Ss are forced to use the same strategy. The S’s answer to the question of which is more intense, a reference or a matching stimulus, initiates a new matching stimulus according to an algorithm that bisects intervals up or down in successive trials and gives a new matching stimulus to judge. A circuitry that governs the method of successive approximations is reported. The method is exemplified by an experimental set-up for odor intensity matching.     

IRT中最小化χ2/EM参数估计方法

对IRT的双参数Logistic模型（2PLM）中未知参数估计问题,给出了一个新的估计方法――最小化χ2/EM估计。新方法在充分考虑项目反应理论(IRT)与经典测量理论(CTT)之间的差异的前提下,从统计计算的角度改进了Berkson的最小化χ2估计,取消了Berkson实施最小化χ2估计时需要已知能力参数的不合实际的前提,扩大了应用范围。实验结果表明新方法能力参数的估计结果与BILOG相比,精确度要高,且当样本容量超过2000时,项目参数的估计结果也优于BILOG。实验还表明新方法稳健性好     

A dental roll technique for the measurement of salivary activity

A discussion of the development of a method for the analysis of salivary activity is presented. Results gained using this new method are compared with those obtained using a whole-mouth spitting technique. The application of the new method in real-world situations is indicated. Norms are presented for measures of salivary flow, pH, and salivary ion concentration based on experimental studies of a female working population.     

Relating tests given to different samples

Suppose a collection of standard tests is given to all subjects in a random sample, but a different new test is given to each group of subjects in nonoverlapping subsamples. A simple method is developed for displaying the information that the data set contains about the correlational structure of the new tests. This is possible to some extent, even though each subject takes only one new test. The method uses plausible values of the partial correlations among the new tests given the standard tests in order to generate plausible simple correlations among the new tests and plausible multiple correlations between composites of the new tests and the standard tests. The real data example included suggests that the method can be useful in practical problems.     

Item Response Theory with Estimation of the Latent Population Distribution Using Spline-Based Densities

The purpose of this paper is to introduce a new method for fitting item response theory models with the latent population distribution estimated from the data using splines. A spline-based density estimation system provides a flexible alternative to existing procedures that use a normal distribution, or a different functional form, for the population distribution. A simulation study shows that the new procedure is feasible in practice, and that when the latent distribution is not well approximated as normal, two-parameter logistic (2PL) item parameter estimates and expected a posteriori scores (EAPs) can be improved over what they would be with the normal model. An example with real data compares the new method and the extant empirical histogram approach.     

A modified conjugate gradient-based Elman neural network

Elman recurrent network is a representative model with feedback mechanism. Although gradient descent method has been widely used to train Elman network, it frequently leads to slow convergence. According to optimization theory, conjugate gradient method is an alternative strategy in searching the descent direction during training. In this paper, an efficient conjugate gradient method has been presented to reach the optimal solution in two ways: (1) constructing a more effective conjugate coefficient, (2) determining adaptive learning rates in terms of the generalized Armijo search method. Experiments show that the performance of the new algorithm is superior to traditional algorithms, such as gradient descent method and conjugate gradient method. In particular, the new algorithm has better performance than the evolutionary algorithm. Finally, we prove the weak and strong convergence of the presented algorithm, i.e., the gradient norm of the error function with respect to the weight vectors converges to zero and the weight sequence approaches a fixed optimal point.     

Quasi-Maximum Likelihood Estimation of Structural Equation Models With Multiple Interaction and Quadratic Effects

In this article, a nonlinear structural equation model is introduced and a quasi-maximum likelihood method for simultaneous estimation and testing of multiple nonlinear effects is developed. The focus of the new methodology lies on efficiency, robustness, and computational practicability. Monte-Carlo studies indicate that the method is highly efficient and that the likelihood ratio test of nonlinear effects is robust and outperforms alternative testing procedures. The new method is applied to empirical data of middle-aged men, where a latent interaction between physical fitness and flexibility in goal adjustment on complaint level is hypothesized. A model with 5 simultaneous nonlinear effects is analyzed, and the hypothesized interaction is quantified and tested positively against an additive model with quadratic and linear effects.     

An approximation method for obtaining a maximized multiple criterion

A new approximation method for obtaining a maximized multiple criterion, based on the formula of Edgerton and Kolbe (2), is presented. By applications to examples from the literature (1, 2), the new method is evaluated in comparison with the Horst approximation (1), a suggested revision of the Horst procedure, and the more exact but more laborious iterative method for the principal axis solution of Hotelling.     

A Recursive Partitioning Method for the Prediction of Preference Rankings Based Upon Kemeny Distances

Preference rankings usually depend on the characteristics of both the individuals judging a set of objects and the objects being judged. This topic has been handled in the literature with log-linear representations of the generalized Bradley-Terry model and, recently, with distance-based tree models for rankings. A limitation of these approaches is that they only work with full rankings or with a pre-specified pattern governing the presence of ties, and/or they are based on quite strict distributional assumptions. To overcome these limitations, we propose a new prediction tree method for ranking data that is totally distribution-free. It combines Kemeny’s axiomatic approach to define a unique distance between rankings with the CART approach to find a stable prediction tree. Furthermore, our method is not limited by any particular design of the pattern of ties. The method is evaluated in an extensive full-factorial Monte Carlo study with a new simulation design.     

Robust principal components: A generalized variance perspective

This article compares several methods for performing robust principal component analysis, two of which have not been considered in previous articles. The criterion here, unlike that of extant articles aimed at comparing methods, is how well a method maximizes a robust version of the generalized variance of the projected data. This is in contrast to maximizing some measure of scatter associated with the marginal distributions of the projected scores, which does not take into account the overall structure of the projected data. Included are comparisons in which distributions are not elliptically symmetric. One of the new methods simply removes outliers using a projection-type multivariate outlier detection method that has been found to perform well relative to other outlier detection methods that have been proposed. The other new method belongs to the class of projection pursuit techniques and differs from other projection pursuit methods in terms of the function it tries to maximize. The comparisons include the method derived by Maronna (2005), the spherical method derived by Locantore et al. (1999), as well as a method proposed by Hubert, Rousseeuw, and Vanden Branden (2005). From the perspective used, the method by Hubert et al. (2005), the spherical method, and one of the new methods dominate the method derived by Maronna.     

四种新的基于属性平衡的CD-CAT选题策略开发研究

基于属性平衡的CD-CAT选题策略能够保证每个认知属性被相当数量的题目测量,从而提高被试属性判准率,传统的基于属性平衡的选题策略包括MMGDI法和MGCDI法。本文针对传统的基于属性测量次数平衡选题策略进行改进,提出4种新的基于属性平衡的选题策略：RMGDI、RMCDI、SE-RMGDI、SE-RMCDI,前两种为基于属性测量次数平衡,后两种为基于属性测量精度平衡的选题策略。模拟研究表明：（1）定长CD-CAT条件下,短测验中,MMGDI表现最好,而长测验中,SE-RMGDI和SE-RMCDI的表现优于传统的属性平衡选题策略。（2）不定长CD-CAT条件下,RMGDI在判准率指标上表现优于传统的属性平衡选题策略,4种新的属性平衡策略在测量效率和综合指标上的表现均优于传统的选题策略。     

Development and testing of a rapid method for measuring shoal size discrimination

The shoal-choice test is a popular method to investigate quantity discrimination in social fish based on their spontaneous preference for the larger of two shoals. The shoal-choice test usually requires a long observation time (20–30 min), mainly because fish switch between the two shoals with low frequency, thus reducing the possibilities of comparison. This duration limits the use of the shoal-choice test for large-scale screenings. Here, we developed a new version of the shoal-choice test in which the subject was confined in a large transparent cylinder in the middle of the tank throughout the experiment to bound the minimum distance from the stimulus shoals and favour switching. We tested the new method by observing guppies (Poecilia reticulata) in a 4 versus 6 fish discrimination (experiment 1). The new method allowed for a faster assessment of the preference for the larger shoal (<5 min), resulting in potential application for large population screenings. Guppies switched five times more frequently between the two shoals and remained close to the first chosen shoal ten times less compared to experiments with the old method. In experiment 2, we found that with the new method guppies were able to discriminate up to 5 versus 6 fish, a discrimination that was not achieved with the classical method. This last result indicates that minor methodological modifications can lead to very different findings in the same species and suggests that caution should be exercised when interpreting inter-specific differences in quantitative abilities.     

Multiple imputation for item scores when test data are factorially complex

Multiple imputation under a two‐way model with error is a simple and effective method that has been used to handle missing item scores in unidimensional test and questionnaire data. Extensions of this method to multidimensional data are proposed. A simulation study is used to investigate whether these extensions produce biased estimates of important statistics in multidimensional data, and to compare them with lower benchmark listwise deletion, two‐way with error and multivariate normal imputation. The new methods produce smaller bias in several psychometrically interesting statistics than the existing methods of two‐way with error and multivariate normal imputation. One of these new methods clearly is preferable for handling missing item scores in multidimensional test data.     

a‐Stratified CAT design with content blocking

Content balancing is often required in the development and implementation of computerized adaptive tests (CATs). In the current study, we propose a modified a‐stratified method, the a‐stratified method with content blocking. As a further refinement of a‐stratified CAT designs, the new method incorporates content specifications into item pool stratification. Simulation studies were conducted to compare the new method with three previous item selection methods: the a‐stratified method; the a‐stratified with b‐blocking method; and the maximum Fisher information method with Sympson‐Hetter exposure control. The results indicated that the refined a‐stratified design performed well in reducing item overexposure rates, balancing item usage within the pool, and maintaining measurement precision, in a situation where all four procedures were forced to balance content.