Psychodynamische Kurzzeittherapie bei dependenter und vermeidender Persönlichkeitsstörung

Although dependent and avoidant personality disorders are frequent in patient populations, there are only few studies in which the efficacy of psychotherapeutic interventions for these two personality disorders was reviewed. The purpose of this study was to examine whether psychodynamic short-term therapy based on the model of the cyclic maladaptive pattern (CMP) is effective for reducing the mental impairment of patients diagnosed with a dependent or an avoidant personality disorder. Data were collected via a self-report scale (Symptom-Checklist, SCL) and expert ratings by therapists [Global Assessment of Functioning (GAF) Scale and Severity of Impairment-Score (Beeinträchtigungsschwere-Score, BSS)]. In the study 20 patients received 25 sessions of outpatient therapy. Mental impairment decreased significantly from the beginning to the end of the therapy; moreover this decrease remained stable over a 2-year period following completion of the therapy. The findings show that psychodynamic short-term therapy can lead to an enduring improvement in the mental health of patients with a dependent or an avoidant personality disorder.     

Die Abschaffung der somatoformen Störungen durch DSM-5 – ein akademischer Schildbürgerstreich?

The new fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has introduced “somatic symptom disorder” as a diagnosis which is no longer differentiated into medically unexplained and explained physical symptoms. As a consequence, the status of somatoform disorders as an independent clinical group is called into question. Against all original intentions, the semantic, conceptual and practical problems of the previous classification have not been solved. The validity of the new diagnosis is doubtful because of its over-inclusive character. Further points of criticism are the less than optimal selection of psychological features, the abandonment of hypochondriasis as a homogeneous concept and imprecise workmanship of the diagnostic criteria where exclusion criteria needed for differential diagnosis are missing.     

Granger Causality—Statistical Analysis Under a Configural Perspective

The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.     

Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera

In this article, we examine the performance of different eye blink detection algorithms under various constraints. The goal of the present study was to evaluate the performance of an electrooculogram- and camera-based blink detection process in both manually and conditionally automated driving phases. A further comparison between alert and drowsy drivers was performed in order to evaluate the impact of drowsiness on the performance of blink detection algorithms in both driving modes. Data snippets from 14 monotonous manually driven sessions (mean 2 h 46 min) and 16 monotonous conditionally automated driven sessions (mean 2 h 45 min) were used. In addition to comparing two data-sampling frequencies for the electrooculogram measures (50 vs. 25 Hz) and four different signal-processing algorithms for the camera videos, we compared the blink detection performance of 24 reference groups. The analysis of the videos was based on very detailed definitions of eyelid closure events. The correct detection rates for the alert and manual driving phases (maximum 94%) decreased significantly in the drowsy (minus 2% or more) and conditionally automated (minus 9% or more) phases. Blinking behavior is therefore significantly impacted by drowsiness as well as by automated driving, resulting in less accurate blink detection.     

Direction dependence analysis: A framework to test the direction of effects in linear models with an implementation in SPSS

In nonexperimental data, at least three possible explanations exist for the association of two variables x and y: (1) x is the cause of y, (2) y is the cause of x, or (3) an unmeasured confounder is present. Statistical tests that identify which of the three explanatory models fits best would be a useful adjunct to the use of theory alone. The present article introduces one such statistical method, direction dependence analysis (DDA), which assesses the relative plausibility of the three explanatory models on the basis of higher-moment information about the variables (i.e., skewness and kurtosis). DDA involves the evaluation of three properties of the data: (1) the observed distributions of the variables, (2) the residual distributions of the competing models, and (3) the independence properties of the predictors and residuals of the competing models. When the observed variables are nonnormally distributed, we show that DDA components can be used to uniquely identify each explanatory model. Statistical inference methods for model selection are presented, and macros to implement DDA in SPSS are provided. An empirical example is given to illustrate the approach. Conceptual and empirical considerations are discussed for best-practice applications in psychological data, and sample size recommendations based on previous simulation studies are provided.     

Public concerns about violent video games are moral concerns—How moral threat can make pacifists susceptible to scientific and political claims against violent video games

Public discussions about the harmfulness of violent media are often held in the aftermath of violent felony. At the same time, we know little about whether and how experiencing real‐life violence impacts the way laypersons perceive and evaluate debates about virtual violence. In Study 1, we provided data indicating that both real‐life violence and violent video games are perceived as morally threatening by people who regard nonviolence to be an important moral value (i.e., pacifists). In Study 2, we hypothesized and found that when pacifists perceive threat from the presence of real‐life violence, they are especially susceptible to scientific and political claims indicating that violent video games are harmful. Our findings are in line with the value protection model and research on the psychological consequences of threat. Implications of the present findings are discussed with regard to a better understanding of the violent video games debate in the general public.