To date, only little is known about the self-directed perception and processing of subtle gaze cues in social anxiety that might however contribute to excessive feelings of being looked at by others. Using a web-based approach, participants (n=174) were asked whether or not briefly (300 ms) presented facial expressions modulated in gaze direction (0°, 2°, 4°, 6°, 8°) and valence (angry, fearful, happy, neutral) were directed at them. The results demonstrate a positive, linear relationship between self-reported social anxiety and stronger self-directed perception of others' gaze directions, particularly for negative (angry, fearful) and neutral expressions. Furthermore, faster responding was found for gaze more clearly directed at socially anxious individuals (0°, 2°, and 4°) suggesting a tendency to avoid direct gaze. In sum, the results illustrate an altered self-directed perception of subtle gaze cues. The possibly amplifying effects of social stress on biased self-directed perception of eye gaze are discussed.

Structural equation model (SEM) trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is computationally demanding. As a remedy, we propose to guide the construction of SEM trees by a family of score-based tests that have recently been popularized in psychometrics (Merkle and Zeileis, 2013; Merkle et al., 2014). These score-based tests monitor fluctuations in case-wise derivatives of the likelihood function to detect parameter differences between groups. Compared to the likelihood-ratio approach, score-based tests are computationally efficient because they do not require refitting the model for every possible split. In this paper, we introduce score-guided SEM trees, implement them in semtree, and evaluate their performance by means of a Monte Carlo simulation.

Dynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method to study heterogeneity in SEM parameters as a function of observed covariates. In the present paper, we derive how IPCs can be calculated for general maximum likelihood estimates and evaluate the performance of IPC regression to estimate group differences in dynamic panel models in discrete and continuous time. We show that IPC regression can be slightly biased in samples with large group differences and present a bias correction procedure. IPC regression showed generally promising results for discrete time models. However, due to highly nonlinear parameter constraints, caution is indicated when applying IPC regression to continuous time models.

The dual-process model of developmental regulation distinguishes two processes of self-regulation (assimilation = tenacious goal pursuit, and accommodation = flexible goal adjustment) that depend on differing conditions, but both contribute to successful development. Four experiments were conducted to investigate whether assimilation and accommodation can be induced or at least shifted by sensorimotor and cognitive manipulations. Experiment 1 investigated the relation between body manipulation and self-regulation. It was shown that assimilation could be triggered when participants were asked to hold on to golf balls as compared to being asked to drop them. Experiment 2 showed that a semantic priming of "let go" or "hold on" via instructions influenced the processes of self-regulation. Experiment 3 and Experiment 4 investigated the role of cognitive sets (divergent thinking) and motivational processes (thinking about one's action resources) in enhancing accommodation or assimilation. As expected, accommodation was triggered by an intervention activating divergent thought, and participants were more assimilative when they thought about their action resources. In sum, the results indicate that assimilation and accommodation can be induced experimentally; they were systematically dependent on physical, cognitive, and motivational states. The implications of the findings were discussed in the light of the dual-process model.

Personality psychology has traditionally focused on stable between-person differences. Yet, recent theoretical developments and empirical insights have led to a new conceptualization of personality as a dynamic system (e.g., Cybernetic Big Five Theory). Such dynamic systems comprise several components that need to be conceptually distinguished and mapped to a statistical model for estimation. In the current work, we illustrate how common components from these new dynamic personality theories may be implemented in a continuous-time modeling framework. As an empirical example, we use experience sampling data from N = 180 persons (with on average T = 40 [SD = 8] measurement occasions) to investigate four different effects between momentary happiness, momentary extraverted behavior, and the perception of a situation as social: (1) between-person effects, (2) contemporaneous effects,(3) autoregressive effects, and (4) cross-lagged effects. We highlight that these four effects must not necessarily point in the same direction, which is in line with assumptions from dynamic personality theories.

Unmodeled differences between individuals or groups can bias parameter estimates and may lead to false-positive or false-negative findings. Such instances of heterogeneity can often be detected and predicted with additional covariates. However, predicting differences with covariates can be challenging or even infeasible, depending on the modeling framework and type of parameter. Here, we demonstrate how the individual parameter contribution (IPC) regression framework, as implemented in the R package ipcr, can be leveraged to predict differences in any parameter across a wide range of parametric models. First and foremost, IPC regression is an exploratory analysis technique to determine if and how the parameters of a fitted model vary as a linear function of covariates. After introducing the theoretical foundation of IPC regression, we use an empirical data set to demonstrate how parameter differences in a structural equation model can be predicted with the ipcr package. Then, we analyze the performance of IPC regression in comparison to alternative methods for modeling parameter heterogeneity in a Monte Carlo simulation.

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Attention is a key success factor in elite sports. Mindfulness training is suspected to be a determinant of attention. The present study was a wait-list controlled investigation of the effects of a mindfulness-based sport psychology program on sustained and selective attention in young elite athletes (n = 137) and the effects of mindfulness training dosage on improving attention scores. In addition, long-term effects were examined. Selective and sustained attention were assessed in a pre–post design using the Frankfurter Aufmerksamkeits-Inventar 2, a go/no-go task. The results of this study indicate that the Berlin Mindfulness-Based Training for Athletes improved both sustained and selective attention in young athletes and that more training in the same amount of time resulted in higher scores in the assessment. The data also indicate that students who continued to practice independently after the intervention had higher scores in the long-term measure.