Client as expert: A Delphi poll of clients’ subjective experience of therapeutic alliance formation variables

This study extends prior research that identified client-derived variables believed to contribute to therapeutic alliance development. Forty-two clients participated in a three-round-modified Delphi poll in which the subjective importance of 74 client-derived, common alliance formation variables was rated using a six-point scale. Participants consensually identified 23 variables as highly important, five as moderately important and three as low in importance to the formation of a strong alliance. Variables related to validation and asking about parts of the client’s life other than the presenting concern were considered as most important by at least one-third of the sample. Limitations of the study are presented and implications for theory, measurement, practice and future research are briefly discussed.     

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。     

Problematic,absent and stigmatizing diagnoses in current mental disorders classifications: Results from the WHO-WPA and WHO-IUPsyS Global Surveys

This study examined English- and Spanish-speaking psychologists’ and psychiatrists’ opinions regarding problematic, absent and stigmatizing diagnoses in current mental disorders classifications (ICD-10 and DSM-IV), and their perceived need for a national classification of mental disorders. Answers to open-ended questions included in WHO-WPA and WHO-IUPsyS surveys were examined using an inductive content-analysis method. A total of 3,222 participants from 35 countries were included. The most problematic diagnostic group was personality disorders, especially among psychiatrists, because of poor validity and lack of specificity. Complex posttraumatic stress disorder was the most frequent diagnosis suggested for inclusion, mainly by psychologists, to better account for the distinct processes and consequences of complex trauma. Schizophrenia was the diagnosis most frequently identified as stigmatizing, particularly by psychiatrists, due to lack of public understanding or knowledge about the diagnosis. Of the 14.4% of participants who perceived a need for a national classification system, two-thirds were from Africa or Latin America. The rationales provided were that mental disorders classifications should consider cultural and socio-historical diversity in the expression of psychopathology, differences in the perception of what is and is not pathological in different nations, and the existence of culture-bound syndromes. Implications for ICD-11 development and dissemination are discussed.     

Proactive control of proactive interference using the method of loci

Proactive interferencebuilds up with exposure to multiple lists of similar items with a resulting reduction in recall. This study examined the effectiveness of using a proactive strategy of the method of loci to reduce proactive interference in a list recall paradigm of categorically similar words. While all participants reported using some form of strategy to recall list words, this study demonstrated that young adults were able to proactively use the method of loci after 25 min of instruction to reduce proactive interference as compared with other personal spontaneous strategies. The implications of this study are that top-down proactive strategies such as the method of loci can significantly reduce proactive interference, and that the use of image and sequence or location are especially useful in this regard.     

Object representations are biased toward each other through statistical learning

The visual system is remarkably efficient at extracting regularities from the environment through statistical learning. While such extraction has extensive consequences on cognition, it is unclear how statistical learning shapes the representations of the individual objects that comprise the regularities. Here we examine how statistical learning alters object representations. In three experiments, participants were exposed to either random arrays containing objects in a random order, or structured arrays containing object pairs where two objects appeared next to each other in fixed spatial or temporal configurations. After exposure, one object in each pair was briefly presented and participants judged the location or the orientation of the object without seeing the other object in the pair. We found that when an object reliably appeared next to another object in space, it was judged as being closer to the other object in space even though the other object was never presented (Experiments 1 and 2). Likewise, when an object reliably preceded another object in time, its orientation was biased toward the orientation of the other object even though the other object was never presented (Experiment 3). These results demonstrated that statistical learning fundamentally shapes how individual objects are represented in visual memory, by biasing the representation of one object toward its co-occurring partner. Importantly, participants in all experiments were not explicitly aware of the regularities. Thus, the bias in object representations was implicit. The current study reveals a novel impact of statistical learning on object representation: spatially co-occurring objects are represented as being closer in space, and temporally co-occurring objects are represented as having more similar features.     

Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques

Autocorrelation and partial autocorrelation, which provide a mathematical tool to understand repeating patterns in time series data, are often used to facilitate the identification of model orders of time series models (e.g., moving average and autoregressive models). Asymptotic methods for testing autocorrelation and partial autocorrelation such as the 1/T approximation method and the Bartlett's formula method may fail in finite samples and are vulnerable to non-normality. Resampling techniques such as the moving block bootstrap and the surrogate data method are competitive alternatives. In this study, we use a Monte Carlo simulation study and a real data example to compare asymptotic methods with the aforementioned resampling techniques. For each resampling technique, we consider both the percentile method and the bias-corrected and accelerated method for interval construction. Simulation results show that the surrogate data method with percentile intervals yields better performance than the other methods. An R package pautocorr is used to carry out tests evaluated in this study.     

The everyday dynamics of rumination and worry: precipitant events and affective consequences

ABSTRACT

Rumination and worry are two perseverative, negatively valenced thought processes that characterise depressive and anxiety disorders. Despite significant research interest, little is known about the everyday precipitants and consequences of rumination and worry. Using an experience sampling methodology, we examined and compared rumination and worry with respect to their relations to daily events and affective experience. Participants diagnosed with Major Depressive Disorder (MDD), Generalized Anxiety Disorder (GAD), co-occurring MDD–GAD, or no diagnosis carried an electronic device for one week and reported on rumination, worry, significant events, positive affect (PA), and negative affect (NA). Across the clinical groups, occurrences of everyday events predicted subsequent increases in rumination, but not worry. Further, higher momentary levels of rumination, but not worry, predicted subsequent decreases in PA and increases in NA. Thus, in these clinical groups, rumination was more susceptible to daily events and produced stronger affective changes over time. We discuss implications for theory and clinical intervention.     

Who’s afraid of the big bad wolf? The interweaving of values and science

The role of values in the scientific process is widely debated. But evidence and values cannot be neatly separated. Instead, values infuse the entire scientific process, starting with the choice of research questions. Research avenues are selected based on prior beliefs about the workings of the world. In fact, informally assigned prior probabilities and normalizing constants play an essential role in distinguishing causes from correlations and ignoring irrelevant associations that would otherwise be suggested by raw data. But since these initial probabilities often have a heterogeneous pedigree – often not all that went into their formation is known or knowable – it becomes difficult to cleanly separate evidence from values. At the same time, this reality does not undermine science’s unique status in generating knowledge. This is because it is not the evidence per se generated by science, but rather the method by which science generates it, that justifies science’s privileged role.     

Learning General Phonological Rules From Distributional Information: A Computational Model

Phonological rules create alternations in the phonetic realizations of related words. These rules must be learned by infants in order to identify the phonological inventory, the morphological structure, and the lexicon of a language. Recent work proposes a computational model for the learning of one kind of phonological alternation, allophony (Peperkamp, Le Calvez, Nadal, & Dupoux, 2006). This paper extends the model to account for learning of a broader set of phonological alternations and the formalization of these alternations as general rules. In Experiment 1, we apply the original model to new data in Dutch and demonstrate its limitations in learning nonallophonic rules. In Experiment 2, we extend the model to allow it to learn general rules for alternations that apply to a class of segments. In Experiment 3, the model is further extended to allow for generalization by context; we argue that this generalization must be constrained by linguistic principles.     

A narratological methodology for identifying archetypal story patterns in autobiographical narratives

Based on Jung's definition of archetype the concept 'archetypal story pattern' is developed as well as a research method drawing on narrative analysis and biographical research to identify these archetypal story patterns in life stories. Jung pointed out that personal myths, archetypal patterns found, e.g., in mythology, can govern the life course of individuals unconsciously. In the Theory of Narrative Identity comparable concepts have been mentioned but were never fully developed. In my research I try to combine Jung's concept of the archetype with the elaborated methodology of narrative analysis. Archetypes can manifest as narratives and the identity construction of a person via narrating the life story can be influenced or even totally structured by archetypal stories which give a specific form as well as a specific meaning to the person's identity. The method of extracting an underlying archetypal pattern from an autobiographical narrative is demonstrated. The results of the research on 20 autobiographical interviews and the inherent archetypal patterns are summarized. The major aim of this paper is to describe in detail the application of a well established method of the social sciences on a key concept of Jungian psychology to show that these concepts can be integrated into recent research frameworks of academic sciences. On the other hand it shows that Jungian concepts can be investigated through established and well defined research methods in empirical research settings.