How a high working memory capacity can increase proactive interference

Previous findings suggested that a high working memory capacity (WMC) is potentially associated with a higher susceptibility to proactive interference (PI) if the latter is measured under high cognitive load. To explain such a finding, we propose to consider susceptibility to PI as a net effect of individual executive processes and the intrinsic potential for PI. With the latter, we refer to the amount of information that is activated at a given time and that has the potential to exert PI subsequently. In two studies deploying generalized linear mixed models, susceptibility to PI was modeled as the decline of performance over trials of a complex span task. The results revealed that a higher WMC was associated with a higher susceptibility to PI. Moreover, the number of stimuli recalled in one trial as a proxy variable for the intrinsic potential for PI negatively affected memory performance in the subsequent trial.     

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model

A three-level piecewise growth model (3L-PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L-PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals (level 2) who are members of a single cluster (level 3). However, in longitudinal research, it is sometimes difficult for data structures to remain purely clustered during a study, such as when some students change classrooms or schools over time. One resulting data structure in this situation is known as a multiple membership structure, where some lower-level units are members of more than one higher-level unit. The new multiple membership PGM (MM-PGM) extends the 3L-PGM to handle multiple membership data structures frequently found in the social sciences. This study sought to examine the consequences of ignoring individual mobility across clusters when estimating a 3L-PGM in comparison to estimating a MM-PGM. MM-PGM estimates were less biased (especially in the cluster-level coefficient estimates), although we found substantial bias in cluster-level variance components across some conditions for both models.     

A note on computing Louis’ observed information matrix identity for IRT and cognitive diagnostic models

Using Louis’ formula, it is possible to obtain the observed information matrix and the corresponding large-sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis’ formula is commonly de-emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis’ formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis’ formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis’ formula with previously existing methods. Results from these presentations suggest that Louis’ formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility.     

Toward a More Credible Understanding of Post-Traumatic Growth

Traditional research on post-traumatic growth has utilized methodologically flawed cross-sectional designs that involve retrospective assessments of post-traumatic growth. This has resulted in a majority of research in this field suffering from a lack of credibility and reliability. In this special issue, we present nine articles that seek to make innovative conceptual and methodological contributions with the goal of promoting better research practices on post-traumatic growth. In the introduction to this special issue, we provide an overview of these contributions, and discuss the implications of these articles both to improving future scholarship and to encouraging personality scientists to examine this important phenomenon in the years and decades to come.     

The contribution of school experience to students' resilience following a terror-related homicide

Schools have a significant effect on students' development, and serve as important social agencies for interventions for students facing disasters. However, little is known about the effect of students' school experience itself on their resilience when facing extreme negative events. The present study focused on students who were exposed to terror-related homicide with the aim of investigating the contribution of school climate resources to their resilience. Since resilience is associated not only with fewer negative outcomes, but also with positive change, the contribution of schools was studied as both inhibiting post-traumatic stress symptoms (PTS) and enhancing post-traumatic growth (PTG). A mixed-methods research design was used. The participants included 117 (52% girls) high school students (mean age = 14.54; SD = 1.49). Twenty-five of them were interviewed in addition to responding to the research questionnaires. Different aspects of the school climate were found to be associated with students' PTS and PTG, yielding two overarched factors explaining the school's role as a protective resource: sheltering and supporting. The former is associated with fewer PTS and the latter with higher PTG. The use of different resources for different forms of resilience is discussed.     

From Implicit Theories to Creative Achievements: The Mediating Role of Creativity Motivation in the Relationship between Stereotypes,Growth Mindset,and Creative Achievement

Research has demonstrated that implicit theories of creativity are crucial in shaping an individual’s behavior and real‐life decisions toward being creative. The present study proposed and examined the underlying mechanisms of how two kinds of implicit theories—the growth mindset of the creative self and the stereotype of creative others—are associated with creative achievements through the mediating role of creativity motivation. Participants were 606 undergraduate students who were enrolled in an education major in two universities in China. Overall, the study found that Chinese students held a positive image toward a creative student, regarding him or her as highly competent, warm, and popular. Student perceptions of a creative other were positively related to their growth mindset of creativity. Moreover, results verified both the mediating role of creativity motivation on growth mindset, as well as the effect of positive stereotyping of the creative other on students’ creative achievement. These findings point to promising creativity motivation strategies including the cultivation of a malleable view of creativity and of creative role models, that may, in turn, promote creative achievement by encouraging students to do, learn, and accomplish new things.     

初中生友谊质量与孤独感的变化轨迹:一项多元潜变量增长模型分析

为考察初中生友谊质量与孤独感的变化轨迹及两者间的关系,采用关系网络问卷、孤独感量表对西安市的875名初中生（M=12.73±0.18;男生494名）进行三年追踪测量,使用多元潜变量增长模型进行分析。结果发现：（1）初中三年,中学生的友谊质量逐渐提高,孤独感水平逐渐下降;（2）初一时的友谊质量和孤独感水平存在显著的个体差异,初中三年期间二者的发展速度也存在个体差异;（3）初一时的友谊质量可显著预测初一时的孤独感水平（β=-1.77,p<0.001）及随后的发展速度（β=0.20,p<0.05）;（4）男生的孤独感水平初始值高于女生,友谊质量的初始值则低于女生;高SES个体孤独感的起始值高于低SES个体。本研究结果揭示了初中三年期间个体友谊质量与孤独感的动态变化关系及友谊质量对孤独感的预测作用,对改善青少年孤独感的现状有一定的现实指导意义。     

Dynamics Versus Development in Numerosity Estimation: A Computational Model Accurately Predicts a Developmental Reversal

Perceptual judgments result from a dynamic process, but little is known about the dynamics of number-line estimation. A recent study proposed a computational model that combined a model of trial-to-trial changes with a model for the internal scaling of discrete numbers. Here, we tested a surprising prediction of the model—a situation in which children's estimates of numerosity would be better than those of adults. Consistent with the model simulations, task contexts led to a clear developmental reversal: children made more adult-like, linear estimates when to-be-estimated numbers were descending over trials (i.e., backward condition), whereas adults became more like children with logarithmic estimates when numbers were ascending (i.e., forward condition). In addition, adults’ estimates were subject to inter-trial differences regardless of stimulus order. In contrast, children were not able to use the trial-to-trial dynamics unless stimuli varied systematically, indicating the limited cognitive capacity for dynamic updates. Together, the model adequately predicts both developmental and trial-to-trial changes in number-line tasks.     

Performance of Mental Health Counselors in Integrated Primary and Behavioral Health Care

Integrated primary and behavioral health care (IPBH) is becoming a preferred mode of service delivery in the United States. Integrated care includes the participation of medical and mental health professionals, such as mental health counselors. The clinical outcomes of these professionals need to be studied to determine their effectiveness in such settings. We examined the performance of 10 mental health counselors on the clinical outcome of 1,747 clients treated in an IPBH center. Analyses using growth curve modeling and pre‐post test design revealed that mental health counselors were effective overall, but they differed in client dropout rates and efficiency in reducing clients' initial symptoms. We used the analyses to rank order counselors based on their effectiveness. Counselors who were the most effective varied in their efficiency but demonstrated the lowest client dropout rates. Implications for future research and counseling practice were discussed.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.