The covariation of independent and dependant variables in neurofeedback: A proposal framework to identify cognitive processes and brain activity variables

This methodological article proposes a framework for analysing the relationship between cognitive processes and brain activity using variables measured by neurofeedback (NF) carried out by functional Magnetic Resonance Imagery (fMRI NF). Cognitive processes and brain activity variables can be analysed as either the dependant variable or the independent variable. Firstly, we propose two traditional approaches, defined in the article as the “neuropsychological” approach (NP) and the “psychophysiology” approach (PP), to extract dependent and independent variables in NF protocols. Secondly, we suggest that NF can be inspired by the style of inquiry used in neurophenomenology. fMRI NF allows participants to experiment with his or her own cognitive processes and their effects on brain region of interest (ROI) activations simultaneously. Thus, we suggest that fMRI NF could be improved by implementing “the elicitation interview method”, which allows the investigator to gather relevant verbatim from participants’ introspection on subjective experiences.     

潜变量交互效应标准化估计：方法比较与选用策略

标准化估计对模型的解释和效应大小的比较有重要作用。虽然潜变量交互效应的恰当标准化估计公式已经面世超过10年, 国内外都在使用和引用, 但至今未见到关于不同估计方法得到的恰当标准化估计的系统比较。通过模拟实验, 比较了乘积指标法、潜调节结构方程(LMS)、无先验信息和有先验信息的贝叶斯法的潜变量交互效应标准化估计在不同条件下的表现。结果发现, 在正态条件下, LMS和有信息贝叶斯法表现较好; 而在非正态条件下, 乘积指标法比较稳健, 但需要较大的样本(不小于500), 小样本且外生潜变量之间相关很低时可使用无信息贝叶斯法。     

国内调节效应的方法学研究

在心理学和其他社科研究领域, 大量实证文章建立调节效应模型, 以分析自变量对因变量的影响是如何随着调节变量的变化而改变。过去10多年, 调节效应分析成了方法学研究的一个热点。从显变量的调节效应、潜变量的调节效应、多层数据的调节效应、基于两层回归模型的单层调节分析、纵向数据的调节效应、调节和中介的整合模型六个主题系统地总结了国内调节效应分析的方法学研究的发展历程。最后对调节效应的未来研究方向做了讨论和拓展。     

基于结构方程模型的多重中介效应分析

多重中介模型是指存在多个中介变量的模型。多重中介模型可以分析特定中介效应、总的中介效应和对比中介效应。指出了目前多重中介模型分析普遍存在的问题,包括分析不完整、使用Sobel检验带来的局限。建议通过增加辅助变量的方法进行完整的多重中介效应分析,使用偏差校正的Bootstrap方法进行中介检验。总结出一个多重中介SEM分析流程,并有示例和相应的MPLUS程序。随后展望了辅助变量和中介效应检验方法的发展方向。     

Deconstructing the construct: A network perspective on psychological phenomena

In psychological measurement, two interpretations of measurement systems have been developed: the reflective interpretation, in which the measured attribute is conceptualized as the common cause of the observables, and the formative interpretation, in which the measured attribute is seen as the common effect of the observables. We advocate a third interpretation, in which attributes are conceptualized as systems of causally coupled (observable) variables. In such a view, a construct like ’depression’ is not seen as a latent variable that underlies symptoms like ’lack of sleep’ or ’fatigue’, and neither as a composite constructed out of these symptoms, but as a system of causal relations between the symptoms themselves (e.g., lack of sleep → fatigue, etc.). We discuss methodological strategies to investigate such systems as well as theoretical consequences that bear on the question in which sense such a construct could be interpreted as real.     

Influence analysis of structural equation models with polytomous variables

The estimation of model parameters in structural equation models with polytomous variables can be handled by several computationally efficient procedures. However, sensitivity or influence analysis of the model is not well studied. We demonstrate that the existing influence analysis methods for contingency tables or for normal theory structural equation models cannot be applied directly to structural equation models with polytomous variables; and we develop appropriate procedures based on the local influence approach of Cook (1986). The proposed procedures are computationally efficient, the necessary bits of the proposed diagnostic measures are readily available following an usual fit of the model. We consider the influence of an individual cell frequency with respect to three cases: when all parameters in an unstructured model are of interest, when the unstructured polychoric correlations are of interest, and when the structural parameters are of interest. We also consider the sensitivity of the parameters estimates. Two examples based on real data are presented for illustration.The work described in this paper was partially supported by a Chinese University of Hong Kong Postdoctoral Fellows Scheme and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (RGC Ref. No. CUHK4186/98P). We are indebted to P.M. Bentler and M.D. Newcomb for providing the data set, Michael Leung for his assistance, and the Editor and the referees for some very valuable comments.     

实验设计:一种非模式的方法

实验设计通常被当作一种种设计模式．这种方法可称之为实验设计的模式方法。但本文认为,这种常用的方法事实上存在着不少缺点。于此．本文尝试提出了一种新的实验设计方法——实验设计的变量方法。实验设计的变量方法的基本思想是．从实验研究的问题出发,根据实验中变量的性质或特点确定实验设计的具体内容。本文最后讨论了实验设计变量方法的优越性。     

潜变量交互效应分析方法

简要回顾了分析显变量交互效应的常用方法。详细讨论了目前分析潜变量交互效应的主要方法,包括用潜变量的因子得分做回归分析、分组线性结构方程模型分析、加入乘积项的结构方程模型分析和两步最小二乘回归分析,并比较和评价了这些方法的优缺点。最后归纳了潜变量交互效应分析方法的研究趋势,并介绍了新近进展(包括LMS方法和GAPI方法)。     

近十年国外罗夏测验攻击变量研究综述

用罗夏测验测量攻击有助于了解个体的攻击性冲动 ,也有助于外显攻击行为的预测和控制。近十年国外文献中有关研究者主要有Holt、Exner、Gacono和Meloy。综述结果表明 ,他们提出的大多数攻击变量都有明确的定义 ,其实证研究也获得了一些支持。但也可以看到 ,目前的研究依然存在不少问题 :(1 )变量的定义有重复、内容有交叉 ;(2 )变量的分类标准不唯一 ;(3 )某些变量的使用和操作欠方便。因而 ,这些变量的定义、分类和使用都还有待于进一步研究支持。     

Concurrent second-order schedules of reinforcement

Responses on one key (the main key) of a two-key chamber produced food according to a second-order variable-interval schedule with fixed-interval schedule components. A response on a second key (the changeover key) alternated colors on the main key and provided a second independent second-order variable-interval schedule with fixed-interval components. The fixed-interval component on one variable-interval schedule was held constant at 8 sec, while the fixed interval on the other variable-interval schedule was varied from 0 to 32 sec. Under some conditions, a brief stimulus terminated each fixed interval and generated fixed-interval patterns; in other conditions, the brief stimulus was omitted. Relative response rate and relative time deviated substantially from scheduled relative reinforcement rate and, to a lesser extent, from obtained relative reinforcement rate under both brief-stimulus and no-stimulus conditions. Matching was observed with equal components on both schedules; with unequal components, increasingly greater proportions of time and responses than the matching relation would predict were spent on the variable-interval schedule containing the shorter component. Preference for the shorter fixed interval was typically more extreme under brief-stimulus than under no-stimulus schedules. The results limit the extension of the matching relation typically observed under simple concurrent variable-interval schedules to concurrent second-order variable-interval schedules.