Diagnostics of Robust Growth Curve Modeling Using Student's t Distribution

Growth curve models with different types of distributions of random effects and of intraindividual measurement errors for robust analysis are compared. After demonstrating the influence of distribution specification on parameter estimation, 3 methods for diagnosing the distributions for both random effects and intraindividual measurement errors are proposed and evaluated. The methods include (a) distribution checking based on individual growth curve analysis; (b) distribution comparison based on Deviance Information Criterion, and (c) post hoc checking of degrees of freedom estimates for t distributions. The performance of the methods is compared through simulation studies. When the sample size is reasonably large, the method of post hoc checking of degrees of freedom estimates works best. A web interface is developed to ease the use of the 3 methods. Application of the 3 methods is illustrated through growth curve analysis of mathematical ability development using data on the Peabody Individual Achievement Test Mathematics assessment from the National Longitudinal Survey of Youth 1997 Cohort (Bureau of Labor Statistics, U.S. Department of Labor, 2005).     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

A phase-field model for deformation twinning

We propose a phase-field model for modeling microstructure evolution during deformation twinning. The order parameters are proportional to the shear strains defined in terms of twin plane orientations and twinning directions. Using a face-centered cubic Al as an example, the deformation energy as a function of shear strain is obtained using first-principle calculations. The gradient energy coefficients are fitted to the twin boundary energies along the twinning planes and to the dislocation core energies along the directions that are perpendicular to the twinning planes. The elastic strain energy of a twinned structure is included using the Khachaturyan's elastic theory. We simulated the twinning process and microstructure evolution under a number of fixed deformations and predicted the twinning plane orientations and microstructures.     

Long-term memory of hierarchical relationships in free-living greylag geese

Animals may memorise spatial and social information for many months and even years. Here, we investigated long-term memory of hierarchically ordered relationships, where the position of a reward depended on the relationship of a stimulus relative to other stimuli in the hierarchy. Seventeen greylag geese (Anser anser) had been trained on discriminations between successive pairs of five or seven implicitly ordered colours, where the higher ranking colour in each pair was rewarded. Geese were re-tested on the task 2, 6 and 12 months after learning the dyadic colour relationships. They chose the correct colour above chance at all three points in time, whereby performance was better in colour pairs at the beginning or end of the colour series. Nonetheless, they also performed above chance on internal colour pairs, which is indicative of long-term memory for quantitative differences in associative strength and/or for relational information. There were no indications for a decline in performance over time, indicating that geese may remember dyadic relationships for at least 6 months and probably well over 1 year. Furthermore, performance in the memory task was unrelated to the individuals’ sex and their performance while initially learning the dyadic colour relationships. We discuss possible functions of this long-term memory in the social domain.     

Need Satisfaction Supportive Game Features as Motivational Determinants: An Experimental Study of a Self-Determination Theory Guided Exergame

Empirical studies have validated that basic needs satisfaction supported by video game play predicts motivation and engagement outcomes. However, few studies specifically manipulated game features for each of the three basic needs specified in the self-determination theory (SDT) to examine how the game features impact players' need satisfaction and game experience. The current study employed an in-house developed exergame and manipulated the game features in a 2 (autonomy-supportive game features: on vs. off) × 2 (competence-supportive game features: on vs. off) experiment to predict need satisfaction, game enjoyment, motivation for future play, effort for gameplay, self-efficacy for exercise using the game, likelihood of game recommendation, and game rating. The manipulated game features led to the corresponding need satisfaction. Manipulated autonomy-supportive and competence-supportive game features had main effects on most motivation and engagement outcomes. Need satisfaction of autonomy and need satisfaction of competence were both found to be mediators for the relationships between the game features and the motivation and engagement outcomes. The findings add evidence to support the underlying mechanism postulated by SDT for media enjoyment and motivation as well as the emerging entertainment research conceptualizing enjoyment as need satisfaction. The findings also have practical implications for intervention effort that intends to capitalize the motivational pull of video games.     

The Third-Person Effects of Political Attack Ads in the 2004 U.S. Presidential Election

This study examines the indirect effects of extensive negative political attack ads in the 2004 presidential election from a third-person effects perspective. Results of a survey using a probability sample of 496 college students indicate that these students believe attack ads harm others more than themselves. Moreover, the respondents tended to perceive attack ads in traditional media to have a greater harmful effect on self and others than attack ads on the Internet. Contingent factors that account for the magnitude of third-person effects include social distance and knowledge. Further, exposure to attack ads was found to be the strongest predictor of perceived harms of such ads on self and others, but only perceived harm on others is a significant predictor of support for restrictions on attack ads. The study contributes to research on the third-person effect by testing perceived harms of attack ads on self and others separately on likelihood to support restrictions.     

音乐对工作记忆影响及机制的ERP研究

以肖邦音乐为刺激材料,采用数字n-back实验范式来研究肖邦音乐对工作记忆的影响方式,并通过运用事件相关电位技术(ERP技术),研究不同音乐条件下,正常人大脑皮层的脑电活动情况,进而探讨音乐对工作记忆影响的神经机制。30名被试参加了3种音乐条件(无音乐、先导音乐、背景音乐)下的n-back(n=1、2)工作记忆任务的行为实验,收集行为数据;14名被试参加了同样实验过程的ERP实验,记录脑电数据。行为结果发现音乐条件对工作记忆效果有显著影响,且先导音乐条件下记忆效果最好;脑电结果显示P3波幅随记忆负荷的增加呈下降趋势,高记忆负荷水平下,P3波幅随先导音乐、背景音乐、无音乐的顺序逐渐呈现下降趋势;源定位分析发现音乐对工作记忆的促进作用可能跟大脑顶叶被激活有关。     

A neural autopilot theory of habit: Evidence from consumer purchases and social media use

This article applies a two-process “neural autopilot” model to field data. The autopilot model hypothesizes that habitual choice occurs when the reward from a behavior has low numerical “doubt” (i.e., reward prediction errors are small). The model toggles between repeating a previous choice (habit) when doubt is low and making a goal-directed choice when doubt is high. The model has ingredients established in animal learning and cognitive neuroscience and is simple enough to make nonobvious predictions. In two empirical applications, we fit the model to field data on purchases of canned tuna and posting on the Chinese social media site Weibo. This style of modeling is called “structural” because there is a theoretical model of how different variables influence choices by agents (the “structure”), which tightly restricts how hidden variables lead to observed choices. There is empirical support for the model, more strongly for tuna purchases than for Weibo posting, relative to a baseline “reduced-form” model in which current choices are correlated with past choices without a mechanistic (structural) explanation. An interesting set of predictions can also be derived about how consumers react to different kinds of changes in prices and qualities of goods (this is called “counterfactual analysis”).     

选择反应作业中的反应组织

通过2-选择反应作业的两个反应复杂度不对称（不等）实验因素考察反应组织,发现在同一作业中无反应复杂度效应,而作业之间反应复杂度效应显著。这提示选择反应是选择已完成组织的反应,而不是实时地进行反应动作组织,进而引入动作模块的概念。即把反应组织视为选择动作模块过程。其实际意义是,在操作界面的优化设计中,要提高一组同时可选择操作R中的某一种操作Ri(R∈Ri|i=1,2,…,n）的绩效,其有效途径是应降低这一组操作R的平均复杂度;如果只降低Ri的复杂度,要提高其绩效收效甚微。