Australian Norms and Retest Data for the Rey Auditory and Verbal Learning Test

This article presents normative data for the Rey Auditory and Verbal Learning Test (RAVLT). A representative sample of 390 healthy young adults aged between 18 and 34 living within the Sydney metropolitan area, Australia, completed Form AB of the RAVLT as part of the Macquarie University Neurological Normative Study. Retest data were collected from a subsample of 98 participants after an interval of 1 year. Norms were derived for commonly used measures of the RAVLT and are presented for the whole sample as well as separately for males and females with different levels of education. Age was not found to impact significantly on test performance for this group of young adults, and therefore age‐adjusted norms are not provided. An excel program to calculate RAVLT standard scores (mean of 10 and standard deviation of 3) can be downloaded from http://www.psy.mq.edu.au/RAVLT . Poor test–retest reliability raises concerns about the use of the RAVLT in clinical diagnosis.     

单维项目因素分析：CCFA与IRT估计方法的比较

当观测指标变量为二分分类数据时,传统的因素分析方法不再适用。作者简要回顾了SEM框架下的分类数据因素分析模型和IRT框架下的测验题目和潜在能力的关系模型,并对两种框架下主要采用的参数估计方法进行了总结。通过两个模拟研究,比较了SEM框架下GLSc和MGLSc估计方法与IRT框架下MML/EM估计方法的差异。研究结果表明：（1）三种方法中,GLSc得到参数估计的偏差最大,MGLSc和MML/EM估计方法相差不大;（2）随着样本量增大,各种项目参数估计的精度均提高;（3）项目因素载荷和难度估计的精度受测验长度的影响;（4）项目因素载荷和区分度估计的精度受总体因素载荷（区分度）高低的影响;（5）测验项目中阈值的分布会影响参数估计的精度,其中受影响最大的是项目区分度。（6）总体来看,SEM框架下的项目参数估计精度较IRT框架下项目参数估计的精度高。此外,文章还将两种方法在实际应用中应该注意的问题提供了一些建议。     

长时间相处是亲子关系的保护性因素吗？——疫情背景下基于微博大数据的质性分析

目的：采用大数据技术与质性分析相结合的方式,探索疫情期间青少年长时间与父母相处的条件下,亲子冲突的演变特征。方法：利用Python3.8编写程序,以“亲子冲突、初中生、高中生、青少年、冲突、父母、与父母冲突”等为关键词在微博平台上搜索个人发布的内容,经数据整理,选择286条微博文本纳入文本分析,用NVivo12质性分析软件辅助编码。结果：亲子冲突的表达包含了冲突诱因、冲突类型、冲突领域,和冲突管理策略等四个方面的特征。结论：研究建构了冲突诱因、冲突领域、冲突类型和冲突应对策略之间的关系模型。亲子冲突的发生是在社会文化背景下,由于家庭功能失调引发的亲子互动方式异常。亲子冲突发生以后,取积极应对策略有助于家庭功能的良性调节。     

Geometric representation of association between categories

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.     

The bilingual effects of linguistic distances on episodic memory and verbal fluency

The impact of linguistic distance or the relatedness between two languages, on bilinguals’ episodic memory performance and verbal fluency is an understudied area. Thus, the purpose of this study was to determine if differences in linguistic distances have differential effects on these abilities. Measures of episodic recognition, categorical fluency, and global cognitive functioning were also considered in the analyses. Two matched samples with participants living and educated in Sweden were drawn from the Betula Prospective Cohort Study. Results showed that bilinguals who speak linguistically similar languages (Swedish and English), performed significantly better than monolinguals on both episodic memory recall and letter fluency, while bilinguals who speak two languages that are more distant (Swedish and Finnish), showed no advantages compared to their monolingual counterparts. For both tasks, however, a linear trend was observed indicative of better performance for the Swedish-English group compared to the Finnish-Swedish group, and for the Swedish-Finnish group compared to the monolinguals group. As expected, no differences between groups were found in any of the other cognitive tasks. Overall, results suggest that the impact of linguistic distances should be explored in more detail in the future.     

Colour perception changes with basic colour word comprehension

Recent work has investigated the origin of infant colour categories, showing pre-linguistic infants categorise colour even in the absence of colour words. These infant categories are similar but not identical to adult categories, giving rise to an important question about how infant colour perception changes with the learning of colour words. Here we present two novel paradigms in which 12- and 19-month-old participants learning English as their first language were assessed on their perception of colour, while data on their colour word comprehension were also collected. Results indicate that participants' perception of colours close to the colour category boundaries dramatically change after colour word learning. The results highlight the shift made from infant colour categories to adult-like linguistically mediated colour categories that accompanies colour word learning.

Research Highlights

• We aimed to test whether colour perception is linguistically mediated in infants.
• We used novel eye-tracking and pupillometry paradigms to test infant colour perception either side of learning colour words.
• Infants' discrimination of colour changes after learning colour words, suggesting a shift due to colour word learning.
• A shift from pre-linguistic colour representation to linguistically mediated colour representation is discussed.

     

A latent topic model with Markov transition for process data

We propose a latent topic model with a Markov transition for process data, which consists of time-stamped events recorded in a log file. Such data are becoming more widely available in computer-based educational assessment with complex problem-solving items. The proposed model can be viewed as an extension of the hierarchical Bayesian topic model with a hidden Markov structure to accommodate the underlying evolution of an examinee's latent state. Using topic transition probabilities along with response times enables us to capture examinees' learning trajectories, making clustering/classification more efficient. A forward-backward variational expectation-maximization (FB-VEM) algorithm is developed to tackle the challenging computational problem. Useful theoretical properties are established under certain asymptotic regimes. The proposed method is applied to a complex problem-solving item in the 2012 version of the Programme for International Student Assessment (PISA).     

The Performance of Multivariate Methods for Two-Group Comparisons with Small Samples and Incomplete Data

Abstract

In intervention studies having multiple outcomes, researchers often use a series of univariate tests (e.g., ANOVAs) to assess group mean differences. Previous research found that this approach properly controls Type I error and generally provides greater power compared to MANOVA, especially under realistic effect size and correlation combinations. However, when group differences are assessed for a specific outcome, these procedures are strictly univariate and do not consider the outcome correlations, which may be problematic with missing outcome data. Linear mixed or multivariate multilevel models (MVMMs), implemented with maximum likelihood estimation, present an alternative analysis option where outcome correlations are taken into account when specific group mean differences are estimated. In this study, we use simulation methods to compare the performance of separate independent samples t tests estimated with ordinary least squares and analogous t tests from MVMMs to assess two-group mean differences with multiple outcomes under small sample and missingness conditions. Study results indicated that a MVMM implemented with restricted maximum likelihood estimation combined with the Kenward–Roger correction had the best performance. Therefore, for intervention studies with small N and normally distributed multivariate outcomes, the Kenward–Roger procedure is recommended over traditional methods and conventional MVMM analyses, particularly with incomplete data.     

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

GoalTo apply signal processing and machine learning skills and knowledge in processing the EEG and MEG signal and further localize and evaluate the source of the finger stimulation.MethodsCognitive control is usually applied in information processing and behavioral response. In the preprocessing, baseline correction is implemented to analyze the pre-stimuli, combining ERP to mark the event related potential, studying the time-locked only behavior. Z-score transform, coherence and spec trum are calculated and analyzed in the functional connectivity analysis.In addition to the functional analysis, Bayes Optimizer evaluates the neuro imaging according to the hierarchical Bayes. The introduction of the application is described from both user and developer’s prospects. Results: Introduction of both user and developers aspects, on its modules from pre-processing, functional analysis and results visualization and evaluation is conducted with one specific clinical data case, including the correlation is higher especially on gamma band and the MVAR coherence on the whole source space depicting the relation between different regions, especially on somatosensory (compared by thalamus) when stimulated by finger activity, phase-lock property of the E/MEG signal and etc. Compared to a manual selection, the scaling parameter prediction can be improved with support vector machine (SVM). The evaluation results with Bayes Optimization, location prediction is superior in the somatosensory area and in the thalamus, the total reconstructed source space is larger, one of the realization of cognitive system comparing different kernels and classifiers. The SVM and discriminant classifier gives similar results evaluating the dipole localization and the parameter choice related as well to the shape parameter, noise level, hyperprior and etc.ConclusionApproaches of Brain Q are found to be suitable for pre-processing for the EEG and MEG data. The system is capable of functional analysis including coherence and spectral related computation. Machine learning techniques are conducted as well to analyze and evaluate the result of the dipole reconstruction and help to predict the better model parameters and the localization of the origin dipoles. A case on finger stimulation clinical data is conducted and the results of the analysis temporarily and spatially manifests its functionality for users and potential extensions for developers.     

Religion and state entanglement and interstate armed conflict initiation, 1990–2010

ABSTRACT

The relationship of religion to armed conflict initiation, as opposed to occurrence, remains understudied and undertheorised. The same is true for the relationship of religion to armed conflicts between states, cf. intra-state or extra-state conflict. This contribution adds to the understanding of both of these conflict outcomes. It examines the relationship to interstate armed conflict initiation of state religion, operationalised as government involvement in religion in the Religion and State dataset of Jonathan Fox. We find sufficient evidence to conclude that religion–state entanglement does raise states’ propensities towards first use of military force. Official religion and official support are both strongly correlated with that outcome. Legislative support to the majority religion is also well correlated with it. State-level discrimination against minority religions is weakly but positively correlated, but heavy regulation of majority religions is not correlated. We further find evidence that minority discrimination, majority regulation and legislative support wield their effects in tandem more strongly than they do individually. The foregoing supports the theory that official religious preference does make states more militant.