Young, middle-aged, and older raters (N=154) evaluated 1,026 prototypical facial poses of neutrality, happiness, anger, disgust, fear, and sadness stemming from 171 young, middle-aged, and older posers. The majority of poses were rated as multi-faceted, that is, to comprise several expressions of varying intensities. Consistent with the notion of age-related increases in negativity–avoidance/positivity effects, crossed-random effects analyses showed an age-related decrease in the attributions of negative, but not positive and neutral, target expressions (that the poser intended to show), and an (...) age-related increase in the attributions of positive and neutral, but not negative, non-target expressions (that the posers did not intend to show). Expressions were more difficult to read the older the posers, particularly for male posers. These age-of-poser effects were independent of the valence of the expression, but partly differed across age groups of raters. The study supports the idea of multi-dimensionality and age-dependency of emotion perception. (shrink)

Structural equation model 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. 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. (shrink)

In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. (...) GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data. (shrink)