Age trends in stimulus overselectivity

Stimulus overselectivity refers to the phenomenon whereby stimulus control over behavior is exerted only by a limited subset of the total number of stimuli present during discrimination learning. It often is displayed by individuals with autistic spectrum disorders or learning disabilities, but is not exclusive to those groups. The present studies investigated the impact of aging on stimulus control and overselectivity. Three age groups--18-22, 47-55, and 70-80 year olds-were studied in two experiments. All participants were trained on a simple discrimination task, randomly assigned to one of two conditions (either with or without a distractor task), and then tested for the emergence of overselectivity (Experiment 1). In Experiment 2 responding controlled by the overselected stimulus elements was reduced by introducing a verbal punisher. In subsequent tests, control of behavior by the previously underselected elements from Experiment 1 was enhanced across the two younger age groups but not the oldest group of participants. The results are discussed in relation to the attention-deficit and overshadowing accounts of overselectivity.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

A note on ROC analysis and non-parametric estimate of sensitivity

In the signal detection paradigm, the non-parametric index of sensitivity A′, as first introduced by Pollack and Norman (1964), is a popular alternative to the more traditional d′ measure of sensitivity. Smith (1995) clarified a confusion about the interpretation of A′ in relation to the area beneath proper receiver operating characteristic (ROC) curves, and provided a formula (which he called A′′) for this commonly held interpretation. However, he made an error in his calculations. Here, we rectify this error by providing the correct formula (which we call A) and compare the discrepancy that would have resulted. The corresponding measure for bias b is also provided. Since all such calculations apply to “proper” ROC curves with non-decreasing slopes, we also prove, as a separate result, the slope-monotonicity of ROC curves generated by likelihood-ratio criterion.This revised article was published online in August 2005 with the PDF paginated correctly.     

Extracting a maximum of useful information from statistical research data

The practice of statistical inference in psychological research is critically reviewed. Particular emphasis is put on the fast pace of change from the sole reliance on null hypothesis significance testing (NHST) to the inclusion of effect size estimates, confidence intervals, and an interest in the Bayesian approach. We conclude that these developments are helpful for psychologists seeking to extract a maximum of useful information from statistical research data, and that seven decades of criticism against NHST is finally having an effect.     

The Impact of Early Loss on Depression

Abstract

This article reviews psychodynamic theoretical claims linking childhood losses to depression later in life and explores contemporary empirical studies that are congruent with those claims. Biologically mediated responses to grief, empirically supported pathways that link loss to depression, and factors that distinguish normal from pathological grief are identified. Trajectories that may lead to the development of psychotic-like, instead of neurotic-like, symptoms of depression are also explored, and empirically supported internal and external vulnerability factors that may lead to depression are reported. Several clinical vignettes are included to exemplify theoretical claims. Implications for assessment and treatment are discussed in light of these findings.     

可用性测试中用户皮肤电水平变化分析——基于EEMD的方法

根据可用性测试过程中用户皮肤电水平（skin conductance level,SCL）的变化,提出了基于总体平均经验模式分解（ensemble empirical mode deeomposition,EEMD）技术的用户SCL信号趋势和变化特征提取方法。首先,应用EEMD技术对SCL信号进行分解,得到若干独立的基本模式分量及趋势项;然后,对基本模式分量进行显著性检验,进而实现信号的去噪去趋势重构,得到用户SCL的变化特征。实验结果表明：界面的可用性问题使得用户SCL单调趋势变得不明显;当用户遇到可用性问题时,SCL去噪去趋势重构信号波动幅度明显变大,Wilcoxon符号秩检验表明不同阶段用户SCL去噪去趋势重构信号功率差异显著。因此,基于EEMD的信号处理方法能有效支撑可用性测试过程中用户SCL变化的分析。     

Bayes Factors Have Frequency Properties—This Should Not Be Ignored: A Rejoinder to Morey,Wagenmakers, and Rouder

Hoijtink, van Kooten, and Hulsker (2016 Hoijtink, H., van Kooten, P., &; Hulsker, K. (2016). Why Bayesian psychologists should change the way they used the Bayes factor. Multivariate Behavioral Research, 51, 1--9. doi: 10.1080/00273171.2014.969364.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) outline a research agenda for Bayesian psychologists: evaluate and use the frequency properties of Bayes factors. Morey, Wagenmakers, and Rouder (2016 Morey, R. D., Wagenmakers, E. -J., &; Rouder, J. N. (2016). Calibrated Bayes factors should not be used: A reply to Hoijtink, van Kooten, and Hulsker. Multivariate Behavioral Research, 51, 10--17. doi: 10.1080/00273171.2015.1052710.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) respond that Bayes factors calibrated using frequency properties should not be used. This paper contains the response of Hoijtink, van Kooten, and Hulsker to the criticism of Morey, Wagenmakers, and Rouder (2016 Morey, R. D., Wagenmakers, E. -J., &; Rouder, J. N. (2016). Calibrated Bayes factors should not be used: A reply to Hoijtink, van Kooten, and Hulsker. Multivariate Behavioral Research, 51, 10--17. doi: 10.1080/00273171.2015.1052710.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]).     

水资源态度与节水行为关系研究现状及发展趋势

文章综述了相关水资源态度与节水行为的概念,介绍了近些年来有影响力的模型。如理性行为理论、计划行为理论,价值—态度—行为层次理论,并简述了水资源态度与节水行为关系的相关研究,最后提出了对未来相关研究的的几点启示。     

Using MCMC chain outputs to efficiently estimate Bayes factors

One of the most important methodological problems in psychological research is assessing the reasonableness of null models, which typically constrain a parameter to a specific value such as zero. Bayes factor has been recently advocated in the statistical and psychological literature as a principled means of measuring the evidence in data for various models, including those where parameters are set to specific values. Yet, it is rarely adopted in substantive research, perhaps because of the difficulties in computation. Fortunately, for this problem, the Savage–Dickey density ratio (Dickey & Lientz, 1970) provides a conceptually simple approach to computing Bayes factor. Here, we review methods for computing the Savage–Dickey density ratio, and highlight an improved method, originally suggested by Gelfand and Smith (1990) and advocated by Chib (1995), that outperforms those currently discussed in the psychological literature. The improved method is based on conditional quantities, which may be integrated by Markov chain Monte Carlo sampling to estimate Bayes factors. These conditional quantities efficiently utilize all the information in the MCMC chains, leading to accurate estimation of Bayes factors. We demonstrate the method by computing Bayes factors in one-sample and one-way designs, and show how it may be implemented in WinBUGS.