Until some of the basic science questions are resolved (e.g., diagnostic clarity, valid screening, and assessment measures) about adjustment disorders, we may not be able to develop adequate evidence-based interventions for the disorders, and it will be difficult to understand the trajectory of these disorders throughout treatment. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Behavior change interventions that incentivize desired behavior are highly effective for improving personal health, but difficult to maintain long term. Relapse is common and examining the mechanisms that contribute to relapse in experimental settings can identify processes relevant to substance abuse treatment. We developed a laboratory task that parallels a recent operant model of relapse after incentivized choice reported in the rodent laboratory. In two experiments, undergraduate participants first learned to make an operant response (keyboard button; R1) to earn a reinforcer consisting of an image of a preferred snack food (O1). In a second phase (Phase 2), R1 was still reinforced, but a new response (R2) was introduced and reinforced with a different reinforcer (a coin; O2). In a test phase, contingent incentives for R2 were removed (extinction) and relapse of R1 was assessed. Experiment 1 found that the O2 contingency suppressed R1 during Phase 2, and R1 relapsed rapidly in the test. Neither effect was consistently related to O2 value. Experiment 2 examined whether noncontingent presentations of O1 or O2 during the test could weaken relapse. Here, we found that noncontingent reinforcers did little to reduce or slow the increase in R1 responding. The present experiments highlight a laboratory approach to studying variables that may influence relapse after incentivized treatment. We identify and discuss areas for development to address differences between the present results and prior observations from animal and clinical studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Risk-taking can fuel innovation and growth, but it can also have devastating consequences for individuals and organizations. Here we examine whether risk-taking affords social-hierarchical benefits to risk-takers. Specifically, we investigate how risk-taking influences perceived dominance, prestige, and the willingness to endorse risk-takers' leadership. Integrating insights from costly signaling theory and the dominance/prestige framework of social rank, we theorized that risk-taking increases leadership endorsement to the degree that it fuels perceptions of prestige, but decreases leadership endorsement to the degree that it fuels perceptions of dominance. However, we also hypothesized that risk-induced perceptions of dominance do translate into leadership endorsement in competitive (rather than cooperative) intergroup settings. We tested these hypotheses in four studies involving different samples, methods, and operationalizations. In Study 1, participants performed an implicit association test (IAT) that revealed that people associate risk with leader positions, and safety with follower positions. Study 2 was a longitudinal field survey conducted during the September 2019 Israeli elections, which showed that voters' perceptions of politicians' risk-taking propensities prior to the elections positively predicted perceived dominance and prestige as well as voting behavior during the elections. Finally, Studies 3 and 4 demonstrated that people are willing to support risk-takers as leaders in the context of competitive (as opposed to cooperative) intergroup situations, because perceived dominance positively predicts leadership endorsement in competitive (but not cooperative) intergroup settings. We discuss implications for understanding the social dynamics of organizational rank and the perpetuation of risky behavior in organizations, politics, and society at large.  https://www.selleckchem.com/products/oxidopamine-hydrobromide.html  (PsycInfo Database Record (c) 2021 APA, all rights reserved).Temporal complexity refers to qualities of a time series that are emergent, erratic, or not easily described by linear processes. Quantifying temporal complexity within a system is key to understanding the time based dynamics of said system. However, many current methods of complexity quantification are not widely used in psychological research because of their technical difficulty, computational intensity, or large number of required data samples. These requirements impede the study of complexity in many areas of psychological science. A method is presented, tangle, which overcomes these difficulties and allows for complexity quantification in relatively short time series, such as those typically obtained from psychological studies. Tangle is a measure of how dissimilar a given process is from simple periodic motion. Tangle relies on the use of a three-dimensional time delay embedding of a one-dimensional time series. This embedding is then iteratively scaled and premultiplied by a modified upshift matrix until a convergence criterion is reached. The efficacy of tangle is shown on five mathematical time series and using emotional stability, anxiety time series data obtained from 65 socially anxious participants over a 5-week period, and positive affect time series derived from a single participant who experienced a major depression episode during measurement. Simulation results show tangle is able to distinguish between different complex temporal systems in time series with as few as 50 samples. Tangle shows promise as a reliable quantification of irregular behavior of a time series. Unlike many other complexity quantification metrics, tangle is technically simple to implement and is able to uncover meaningful information about time series derived from psychological research studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. Recently, methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models commonly used in social and behavioral research. These methods allow researchers to estimate large models even in the presence of small sample sizes. However, some drawbacks of the LASSO, such as high false positive rates (FPRs) and inconsistency in selection results, persist at the same time. We propose the application of stability selection, a method based on repeated resampling of the data to select stable coefficients, to regularized SEM as a mechanism to overcome these limitations. Across 2 simulation studies, we find that stability selection greatly improves upon the LASSO in selecting the correct paths, specifically through reducing the number of false positives.