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In contrast, when participants actively attempted to retrieve primes from their definitions, no phonological facilitation was observed. Successful retrieval of semantic and both primes facilitated subsequent target retrieval, whereas, failure to retrieve semantic and both primes inhibited subsequent target retrieval. These facilitatory and inhibitory influences of prime retrieval for semantic and both primes were independent of feedback on retrieval performance (Experiment 4) and participants' overall knowledge of the primes and targets (Experiment 5), and also did not extend to retrieval from episodic memory (Experiment 6). The findings are consistent with ongoing retrospective processes during target retrieval, which reengage prime retrieval success or failure and consequently produce benefits and costs during repeated retrieval from semantic memory. (PsycINFO Database Record (c) 2020 APA, all rights reserved).Determining the number of factors is one of the most crucial decisions a researcher has to face when conducting an exploratory factor analysis. As no common factor retention criterion can be seen as generally superior, a new approach is proposed-combining extensive data simulation with state-of-the-art machine learning algorithms. First, data was simulated under a broad range of realistic conditions and 3 algorithms were trained using specially designed features based on the correlation matrices of the simulated data sets. Subsequently, the new approach was compared with 4 common factor retention criteria with regard to its accuracy in determining the correct number of factors in a large-scale simulation experiment. Sample size, variables per factor, correlations between factors, primary and cross-loadings as well as the correct number of factors were varied to gain comprehensive knowledge of the efficiency of our new method. A gradient boosting model outperformed all other criteria, so in a second step, we improved this model by tuning several hyperparameters of the algorithm and using common retention criteria as additional features. This model reached an out-of-sample accuracy of 99.3% (the pretrained model can be obtained from https//osf.io/mvrau/). A great advantage of this approach is the possibility to continuously extend the data basis (e.g., using ordinal data) as well as the set of features to improve the predictive performance and to increase generalizability. (PsycINFO Database Record (c) 2020 APA, all rights reserved).Reporting the reliability of the scores obtained from a scale or test is part of the standard repertoire of empirical studies in psychology. With reliability being a key concept in psychometrics, researchers have become more and more interested in evaluating reliability coefficients across studies and, ultimately, quantify and explain possible between-study variation. This approach-commonly known as "reliability generalization"-can be specified within the framework of meta-analysis. The existing procedures of reliability generalization, however, have several methodological issues (a) unrealistic and often untested assumptions on the measurement model underlying the reliability coefficients (e.g., essential τ-equivalence for Cronbach's α); (b) the use of univariate approaches to synthesizing reliabilities of total and subscale scores; (c) the lack of comparability across different types of reliability coefficients. However, these issues can be addressed directly through meta-analytic structural equation modeling (MASEM)-a method that combines meta-analysis with structural equation modeling through synthesizing either correlation matrices or model parameters across studies. The primary objective of this article is to present the potential MASEM has for the meta-analysis of reliability coefficients. We review the extant body of literature on the use of reliability generalization, discuss and illustrate two MASEM approaches (i.e., correlation-based and parameter-based MASEM), and propose some practical guidelines. Future directions for utilizing MASEM for reliability generalization are discussed. (PsycINFO Database Record (c) 2020 APA, all rights reserved).Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. selleck compound (PsycINFO Database Record (c) 2020 APA, all rights reserved).Although most of the school violence literature is focused on peer victimization, interest is growing in teachers being victimized by their students. However, there is far less interest in students being victimized by their teachers, patterns of mutual victimization, and how they are associated with other school factors. Using the conceptual framework of school violence in evolving contexts, the present study examined teacher-to-student victimization in Chile and tested, for the first time, the associations of student-to-teacher victimization, peer victimization, school safety, classroom climate, and school climate at the individual and school levels. The sample consisted of 50,344 students (51.6% female) in Grades 5-8 in 431 schools in Chilean public-funded schools. Findings indicated that verbal types of teacher-student mutual victimization were more prevalent than physical and sexual victimization. Teacher-to-student victimization was higher among male and younger students. Multilevel analyses showed that student-to-teacher victimization, school safety, classroom climate, and school climate were associated with teacher-to-student victimization.