Increased activation in superior temporal gyri as a function of increment in phonetic features

A common assumption is that phonetic sounds initiate unique processing in the superior temporal gyri and sulci (STG/STS). The anatomical areas subserving these processes are also implicated in the processing of non-phonetic stimuli such as music instrument sounds. The differential processing of phonetic and non-phonetic sounds was investigated in this study by applying a “sound-morphing” paradigm, where the presence of phonetic features were parametrically varied, creating a step-wise transition from a non-phonetic sound into a phonetic sound. The stimuli were presented in an event-related fMRI design. The fMRI-BOLD data were analysed using parametric contrasts. The results showed a higher sensitivity for sounds containing phonetic features compared to non-phonetic sounds in the middle part of STG, and in the anterior part of the planum temporale (PT) bilaterally. Although the same areas were involved in the processing of non-phonetic sounds, a difference in activation was evident in the STG, with an increase in activation related to increment of phonetic features in the sounds. The results indicate a stimulus-driven, bottom-up process that utilizes general auditory resources in the secondary auditory cortex, depending on specific phonetic features in the sounds.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.     

Nonparametric item response theory axioms and properties under nonlinearity and their exemplification with knowledge space theory

This paper investigates the dichotomous Mokken nonparametric item response theory (IRT) axioms and properties under incomparabilities among latent trait values and items. Generalized equivalents of the unidimensional nonparametric IRT axioms and properties are formulated for nonlinear (quasi-ordered) person and indicator spaces. It is shown that monotone likelihood ratio (MLR) for the total score variable and nonlinear latent trait implies stochastic ordering (SO) of the total score variable, but may fail to imply SO of the nonlinear latent trait. The reason for this and conditions under which the implication holds are specified, based on a new, simpler proof of the fact that in the unidimensional case MLR implies SO. The approach is applied in knowledge space theory (KST), a combinatorial test theory. This leads to a (tentative) Mokken-type nonparametric axiomatization in the currently parametric theory of knowledge spaces. The nonparametric axiomatization is compared with the assumptions of the parametric basic local independence model which is fundamental in KST. It is concluded that this paper may provide a first step toward a basis for a possible fusion of the two split directions of psychological test theories IRT and KST.     

bootstrap法在合成分数信度区间估计中的应用

在介绍bootstrap法原理的基础上,本文以一个同质测量模式的模拟数据为例,对结构方程模型下使用bootstrap法对合成分数信度的区间估计的应用中进行了演示。     

Interval estimation of gamma for an<Emphasis Type="Italic">R</Emphasis>×<Emphasis Type="Italic">S</Emphasis> table

When the underlying responses are on an ordinal scale, gamma is one of the most frequently used indices to measure the strength of association between two ordered variables. However, except for a brief mention on the use of the traditional interval estimator based on Wald's statistic, discussion of interval estimation of the gamma is limited. Because it is well known that an interval estimator using Wald's statistic is generally not likely to perform well especially when the sample size is small, the goal of this paper is to find ways to improve the finite-sample performance of this estimator. This paper develops five asymptotic interval estimators of the gamma by employing various methods that are commonly used to improve the normal approximation of the maximum likelihood estimator (MLE). Using Monte Carlo simulation, this paper notes that the coverage probability of the interval estimator using Wald's statistic can be much less than the desired confidence level, especially when the underlying gamma is large. Further, except for the extreme case, in which the underlying gamma is large and the sample size is small, the interval estimator using a logarithmic transformation together with a monotonic function proposed here not only performs well with respect to the coverage probability, but is also more efficient than all the other estimators considered here. Finally, this paper notes that applying an ad hoc adjustment procedure—whenever any observed frequency equals 0, we add 0.5 to all cells in calculation of the cell proportions—can substantially improve the traditional interval estimator. This paper includes two examples to illustrate the practical use of interval estimators considered here.The authors wish to thank the Associate Editor and the two referees for many valuable comments and suggestions to improve the contents and clarity of this paper. The authors also want to thank Dr. C. D. Lin for his graphic assistance.