定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

MPT模型在事件性前瞻记忆研究中的应用

前瞻性记忆是指对将要进行的活动或事件的记忆。前瞻记忆中,包含了前瞻成分和回溯成分。前人研究中,缺乏对前瞻成分和回溯成分有效的分离手段,使得对前瞻记忆机制的探讨缺乏深入挖掘。本文将MPT模型与Cohen等人的研究范式对比,分析了该模型在事件性前瞻记忆研究中的优势。文章对模型的理论基础,模型的主要内容,模型的数据计算方法,模型的效度以及模型的应用等几个方面进行了介绍,同时对模型使用中需要注意的问题进行了讨论。     

Pentheus rather than Oedipus: On perversion,survival and analytic ‘presencing’1

Following an introductory review of the main developments in the psychoanalytic thinking on perversion, the author focuses on her own understanding of perversion and its treatment, based on the psychoanalytic treatment of patients with severe sexual perversions. This paper uses the term ‘autotomy’ (borrowed from the fi eld of biology) to describe perversion formation as an ‘autotomous’ defence solution involving massive dissociative splitting in the service of psychic survival within a violent, traumatic early childhood situation; thus, a compulsively enacted ‘desire for ritualised trauma’ ensues. The specifi c nature of the perverse scenario embodies the specifi c experiential core quality of the traumatic situation. It is an actual repetition in the present of the imprint of a past destructive experience which is pre‐arranged and stage‐managed; it thus encounters haunting scenes of dread or psychic annihilation while, at the same time, controlling, sanitising and disavowing them. Hence, the world of severe perversion is no longer oedipal, but rather the world of Pentheus, Euripides's most tragic hero‐a world dominated by a mixture of a mother's madness, devourment, destruction and rituals of desire. According to this view, the (diffi cult) psychoanalytic treatment of perversion focuses on patient‐analyst interconnectedness‐brought about by the analyst's ‘givenness to being present’ or ‘presencing’‐at a deep, primary level of contact and impact (the emphasis being on the ontological dimension of experience). This evolving therapeutic entity creates and actualises a new, alternative experiential‐emotional reality within the pervert's alienated world, eventually generating a change in the perverse essence. The author illustrate this approach with three clinical vignettes.     

Early trauma and narcissism—autism bipolarity1

The analyst makes a series of considerations taken a posteriori from the analysis of a small number of patients. These patients have saved themselves from an early narcissistic catastrophe by developing precocious mental processes, while affective relationships rudimentarily repeat the impact with the original trauma. Primitive defences, essentially denial and vertical splitting, dissociate the tear in the psyche and structure a narcissism–autism bipolarity, revealed in aspects of the character which oblige the patient to automatically repeat a single matrix of experience. In therapy, it is necessary to construct a rst time of the trauma, by pending and linking threads of the primary relationship and strengthening them in the analytic relationship. This reconstruction of the background, a screen to project what had originally been rejected, is the prerequisite for coming out, in deferred action, from the hold of the pathological identifications. The author dedicates particular attention to the undifferentiated background, the nature‐environment torn by the trauma, and to the need to reconstruct this fabric of experience in the analytical relationship, as a fundamental element to the recomposition of the dissociated nuclei. In the clinical case, the analyst describes in particular how the analyst's words encounter an unbridgeable gap, a failure in the capacity for representation when opening the autistic nucleus. Through a regression lasting for about a year, a patient was able to live the experience of primitive agonies and that of an unbearable helplessness and, at the same time, was able to feel how the analyst supported her sense of existence. Subsequently, the patient was able to give shape, through visual images, to deep states of being and start the process of metabolising and symbolising the trauma.     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Order‐constrained linear optimization

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order‐constrained linear least‐squares (OCLO) optimization algorithm that maximizes the linear least‐squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out‐of‐sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat‐tailed distributions), OCLO shows less bias and variance, and substantially better out‐of‐sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data.     

AFFECT,SYMPTOM, FANTASY,DREAM: CLINICAL AND THEORETICAL CONSIDERATIONS

A symptom being studied in the process of analysis can be seen as not unlike the unconscious affect it sprang from. The author presents a case in which a symptom, premature ejaculation, was analogous to the unconscious affect of guilt, which itself seemed to be a premature defensive transformation of a deeper current of anger. Guilt was interpreted as if it were a psychic premature ejaculation, a defensive derailment of anger. Fantasy and dream seemed to be engaged in similar transformations, with a fantasy of “premature incarceration” not unlike the symptom itself in its analogous functioning. Analysis of affect, symptom, fantasy, and dream in complex, integrative analytic process led not only to resolution of the symptom itself, but also to a deeper understanding of the mind's complex functioning in general.     

Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models

Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the homoscedasticity assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated assumptions is discussed.     

Modeling Information Content Via Dirichlet-Multinomial Regression Analysis

Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.     

新颖语义联结形成的右半球优势效应

新颖语义联结形成是创造性思维的重要加工过程。采用谜题型歇后语为材料,直接操控语义联结的新颖性,通过两个实验考察汉语新颖语义联结形成的神经机制。实验1采用fMRI记录谜题型歇后语阅读理解过程的神经活动,结果显示相对于寻常语义关联条件,新颖语义关联条件更强地激活了右侧颞上回,该脑区可能与新颖语义信息的激活有关。实验2采用ERP技术在语义关联性判断任务中考察新颖语义联结形成神经加工的时空特征,结果显示相对于寻常语义关联条件,新颖语义关联条件在右侧颞区和右侧额区诱发了更正的晚期正成分,可能分别反映了新颖语义信息的激活以及选择与整合。研究结果支持了新颖语义联结形成的右半球加工优势效应。