Motor execution affects action prediction

Previous studies provided evidence of the claim that the prediction of occluded action involves real-time simulation. We report two experiments that aimed to study how real-time simulation is affected by simultaneous action execution under conditions of full, partial or no overlap between observed and executed actions. This overlap was analysed by comparing the body sides and the movement kinematics involved in the observed and the executed action. While performing actions, participants observed point-light (PL) actions that were interrupted by an occluder, followed by a test pose. The task was to judge whether the test pose depicted a continuation of the occluded action in the same depth angle. Using a paradigm proposed by Graf et al., we independently manipulated the duration of the occluder and the temporal advance of the test pose relative to occlusion onset (occluder time and pose time, respectively). This paradigm allows the assessment of real-time simulation, based on prediction performance across different occluder time/pose time combinations (i.e., improved task performance with decreasing time distance between occluder time and pose time is taken to reflect real-time simulation). The PL actor could be perceived as from the front or back, as indicated by task instructions. In Experiment 1 (front view instructions), evidence of action simulation was obtained for partial overlap (i.e., observed and performed action corresponded either in body side or movement kinematics), but not for full or no overlap conditions. The same pattern was obtained in Experiment 2 (back view instructions), ruling out a spatial compatibility explanation for the real-time pattern observed. Our results suggest that motor processes affect action prediction and real-time simulation. The strength of their impact varies as a function of the overlap between observed and executed actions.     

Estimation of the predictive power of the model in mixed‐effects meta‐regression: A simulation study

Several methods are available to estimate the total and residual amount of heterogeneity in meta‐analysis, leading to different alternatives when estimating the predictive power in mixed‐effects meta‐regression models using the formula proposed by Raudenbush (1994, 2009). In this paper, a simulation study was conducted to compare the performance of seven estimators of these parameters under various realistic scenarios in psychology and related fields. Our results suggest that the number of studies (k) exerts the most important influence on the accuracy of the results, and that precise estimates of the heterogeneity variances and the model predictive power can only be expected with at least 20 and 40 studies, respectively. Increases in the average within‐study sample size () also improved the results for all estimators. Some differences among the accuracy of the estimators were observed, especially under adverse (small k and ) conditions, while the results for the different methods tended to convergence for more optimal scenarios.     

Development and Psychometric Evaluation of the Utrecht Grief Rumination Scale

Two different types of ruminative coping, depressive rumination and grief rumination, negatively influence bereavement outcome. Although grief-specific rumination is likely to be relevant in the bereavement context no internationally validated scale to measure grief rumination exists. Therefore, the current contribution aims to validate the Utrecht Grief Rumination Scale (UGRS), a scale developed to measure grief-specific rumination, in an English sample. Psychometric properties of the English UGRS were compared with those in a Dutch sample. 204 British adults (89 % women, 11 % men), bereaved on average 16 months ago, and 316 Dutch adults (88 % women, 12 % men), bereaved on average 12 months ago, filled out online questionnaires. Different types of rumination (grief rumination, brooding, reflection, trait rumination) and symptoms of psychopathology (anxiety, depression, complicated grief) were measured. A correlated five factor model provided the best fit for the UGRS. Multi-group comparisons showed that the factor structures of the English and Dutch version of the UGRS were highly similar across language groups. The UGRS showed excellent reliability. Results further supported the construct, convergent, divergent and concurrent validity of the English UGRS. The psychometric properties of the Dutch UGRS corroborated these findings. The UGRS appears a valid instrument to assess grief-specific rumination in international research and may have potential applicability as a clinical instrument to assess rumination in individuals with problematic grief.     

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.