(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some—but not all—of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.     

Influence of simple action on subsequent manual and ocular responses

Recent investigations into how action affects perception have revealed an interesting “action effect”—that is, simply acting upon an object enhances its processing in subsequent tasks. The previous studies, however, relied only on manual responses, allowing an alternative stimulus-response binding account of the effect. The current study examined whether the action effect occurs in the presence of changes in response modalities. In Experiment 1, participants completed a modified action effect paradigm, in which they first produced an arbitrary manual response to a shape and then performed a visual search task in which the previous shape was either a valid or invalid cue—responding with a manual or saccadic response. In line with previous studies, the visual search was faster when the shape was a valid cue but only if the shape had been acted upon. Critically, this action effect emerged similarly in both the manual and ocular response conditions. This cross-modality action effect was successfully replicated in Experiment 2, and analysis of eye movement trajectories further revealed similar action effect patterns on direction and numerosity. These results rule out the stimulus-response binding account of the action effect and suggest that it indeed occurs at an attentional level.     

多元动机网格测验（MMG-S）中文版的修订报告

对Sokolowski等人编制的简版多元动机网格测验（MMG-S）作了中文版的修订。MMG-S的验证性因素分析表明：中文修订版MMG-S仍保持英文版的六因子结构,修订后六因子的α系数在0.70~0.84之间。中文修订版MMG-S具有良好的心理学测量指标,可用于测量企业管理人员的三种社会性动机。     

程序公正作用机制中的调节变量

程序公正是指用于决定分配的过程是否公正。回顾近年来程序公正作用机制的相关理论以及实证研究结果发现, 对程序公正的效果起调节作用的主要有四类因素, 分别为情景因素、个体特征因素、分配结果因素以及领导者因素。今后该主题的研究应进一步关注发言权效应的跨文化验证、探索本土化程序公正原则、进一步考察情景变量的调节效应、加强领导者因素的研究、结合分配公正进行研究, 并应加强程序公正的应用性研究。     

内隐记忆中的神经机制研究方法

知觉启动与语义启动神经机制的研究是内隐记忆神经机制研究的两条重要途径。本文介绍了内隐记忆神经机制的研究途径之一：知觉启动神经机制的研究进展情况,并对研究中存在的问题及解决方法提出建议。     

组织公正氛围的理论机制、研究视角及研究框架

组织公正是员工对工作场所公正环境的心理感知, 研究层面有个体与群体之分。以往研究多集中在个体层面, 以致研究结果缺乏对群体现象的有效解释。进入新世纪学者们开始关注群体层面的公正氛围研究, 并在理论和实证方面取得了较大突破。通过回顾相关研究可以发现：(1)在理论机制上, 公正氛围的形成可以通过社会信息加工理论、吸引-选择-磨合模型、公正传染概念和公正启发理论来解释; (2)在研究视角上, 主要存在维度视角、整体视角、感知来源视角、氛围属性视角和第三方视角; (3)在研究主题上, 主要涉及领导、团队和组织三方面对公正氛围的影响, 以及公正氛围对个体、团队和组织三个层面的影响效果。未来研究可着重从多种领导行为对不同公正氛围影响的比较、其他领导因素对公正氛围的影响、不同公正氛围对结果变量影响的比较、新视角公正氛围测量方法的尝试, 以及文化因素对公正氛围的影响研究等方面入手。     

具身认知视角的饮食消费行为——基于心理模拟“具身”方式

随着具身认知理论的发展, 基于心理模拟“具身”方式的饮食消费行为研究得到了一些新颖、有趣的研究结果, 为过食与肥胖、食品营销等问题的解决提供了新视角。心理模拟既能促进进食也能抑制进食; 心理模拟容易性因素与动机性因素是影响心理模拟程度的两大类主要前因变量; 未来需要进一步探索心理模拟的容易性因素与动机性因素、心理模拟提高消费欲望与产生腻厌反应的边界条件, 以及利用心理模拟探讨肥胖高危人群——高奖赏敏感性个体过食的深层次内在机制等。     

Bayesian generalized structured component analysis

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.     

DNA研究的历史带给我们的启示

自然科学发展日新月异。墨守成规的传统思维模式常常是羁绊研究工作停滞不前的主要障碍。通过纵观DNA双螺旋结构的研究历程,认识到实事求是的态度是确立正确研究方向的基础,而全方位、多视角、多学科交叉的开放性思维模式给科学研究带来了更宽阔的途径。     

DNA研究的历史带给我们的启示

自然科学发展日新月异.墨守成规的传统思维模式常常是羁绊研究工作停滞不前的主要障碍.通过纵观DNA双螺旋结构的研究历程,认识到实事求是的态度是确立正确研究方向的基础,而全方位、多视角、多学科交叉的开放性思维模式给科学研究带来了更宽阔的途径.