大学新生主观社会地位与抑郁的变化轨迹:一项多元潜变量增长模型分析

采用整群取样的方法在全国按东部、中部和西部共抽取了7所高校的1983名新生,进行了历时四个月的四次追踪测试。使用潜变量增长模型建模,分别考察了新生主观社会地位和抑郁的变化轨迹,并就两者间的变化关系进行了分析。结果发现新生入学后四个月内：（1）主观社会地位呈阶段化线性增长,其起始水平和第一阶段的增长速度存在显著个体间差异;（2）抑郁呈二次方增长,其起始水平和增长速度存在显著个体间差异;（3）入学后主观社会地位的下滑速度能有效预测新生抑郁水平的上升速度。研究基于情绪压制理论,对主观社会地位与抑郁间的变化关系进行了分析。     

学习者生成图解对说明文阅读理解和应用的效果

在112名大学生被试阅读难度不同的两种材料并解答相关的理解题和应用题时,按照阅读时是否要求生成图解与答题时是否允许生成图解两个条件将他们平均分为四个小组,探讨阅读画图与答题画图对被试理解和应用不同难度材料的影响。结果表明,阅读时学习者生成的图解可显著促进对难度较高材料的理解;答题时学习者生成的图解可显著促进对难度较低材料的应用;阅读时是否画图与答题时是否画图对理解和应用所学习材料没有显著交互作用。     

管理评价判断模型与策略获取

管理评价是在不确定情景中的一种多特征决策,非补偿策略是决策者所采用的主导判断模型。作为一种结构建模程序,策略捕捉可以建构包含决策标准及其权重的统计模型,获取决策内在过程和预测判断。多层线性模型能够方便地考察人内和人间变异,表征判断非线性结构,是分析策略捕捉数据的恰当方法。管理评价设计可以借鉴行为经济学和实验经济学的思路,实现对现实管理决策过程的抽象模拟。     

Dynamic Psychotherapy and the Problem of Time

This paper examines the dilemmas psychodynamic clinicians face in dealing with the challenges of the current clinical climate. We look to our theories, to the evolution of psychodynamic technique and to the collaborative nature of the therapeutic endeavor as they relate to the goals of psychotherapy. A case illustration is provided, and guidelines are offered for current professional practice.     

Affect, Curiosity, and Socialization-Related Learning: A Path Analysis of Antecedents to Job Performance

This study examined the influence of affect, curiosity, and socialization-related learning on job performance, with 233 service industry employees from a diverse variety of occupations completing surveys at their places of work. Both state and trait curiosity and socialization-related learning (learning associated with employee socialization) were hypothesized to mediate the relationship between affect (operationalized as state and trait anxiety and anger) and job performance. Structural equation analyses indicate that the data are consistent with the theoretical models proposed. As expected, anxiety negatively influences curiosity, socialization-related learning, and job performance; conversely, anger positively influences curiosity, socialization-related learning, and job performance. Overall, the findings suggest that affects indeed predict perception of job performance, but through the mediation of curiosity and the learning associated with the socialization process. These results highlight the importance of the complex interplay between affect, curiosity, and learning when thinking about successful employee socialization and best possible job performance.     

Capturing the interplay among within- and between-person processes using multilevel modeling techniques

Multilevel modeling is an excellent way to analyze nested or clustered data of the type commonly collected through investigations into the linkages between psychological functioning and relationship processes. This article describes two especially relevant applications of multilevel modeling. The first application, growth curve analysis, is already familiar to many researchers and involves modeling individuals’ change trajectories over time and relating the derived change parameters to person-level characteristics or phenomena. The purpose of this paper is to emphasize a second application, multilevel process analysis, which involves modeling within-subject characteristics other than change over a representation of time. Multilevel analysis of within-subject processes is particularly well-suited for hypotheses common to clinical psychology investigations, yet has received substantially less attention in the literature than its growth curve counterpart. Types of research questions and methodologies that can be addressed within the multilevel process analysis framework are described. Finally, aspects of multilevel process analysis are demonstrated with daily diary data collected from wives who reported on their marital happiness and depressed mood for 3 weeks.     

Analysis of the factor structure of the Sociocultural Attitudes Towards Appearance Questionnaire (SATAQ-3) in Spanish secondary-school students through exploratory structural equation modeling

The aims of the present study were: (1) to assess the factor structure of the SATAQ-3 in Spanish secondary-school students by means of exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) models; and (2) to study its invariance by sex and school grade. ESEM is a technique that has been proposed for the analysis of internal structure that overcomes some of the limitations of EFA and CFA. Participants were 1559 boys and girls in grades seventh to tenth. The results support the four-factor solution of the original version, and reveal that the best fit was obtained with ESEM, excluding Item 20 and with correlated uniqueness between reverse-keyed items. Our version shows invariance by sex and grade. The differences between scores of different groups are in the expected direction, and support the validity of the questionnaire. We recommend a version excluding Item 20 and without reverse-keyed items.     

Computational exploration of metaphor comprehension processes using a semantic space model

Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view ( Bowdle & Gentner, 2005 ), aptness view ( Glucksberg & Haught, 2006b ), and interpretive diversity view ( Utsumi, 2007 ); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization and comparison processes. This article attempts to answer the question regarding which views are plausible by using cognitive modeling and computer simulation based on a semantic space model. In the simulation experiment, categorization and comparison processes are modeled in a semantic space constructed by latent semantic analysis. These two models receive word vectors for the constituent words of a metaphor and compute a vector for the metaphorical meaning. The resulting vectors can be evaluated according to the degree to which they mimic the human interpretation of the same metaphor; the maximum likelihood estimation determines which of the two models better explains the human interpretation. The result of the model selection is then predicted by three metaphor properties (i.e., vehicle conventionality, aptness, and interpretive diversity) to test the three views. The simulation experiment for Japanese metaphors demonstrates that both interpretive diversity and vehicle conventionality affect the choice between the two processes. On the other hand, it is found that metaphor aptness does not affect this choice. This result can be treated as computational evidence supporting the interpretive diversity and conventionality views.     

Impulse processing: a dynamical systems model of incremental eye movements in the visual world paradigm

The Visual World Paradigm (VWP) presents listeners with a challenging problem: They must integrate two disparate signals, the spoken language and the visual context, in support of action (e.g., complex movements of the eyes across a scene). We present Impulse Processing, a dynamical systems approach to incremental eye movements in the visual world that suggests a framework for integrating language, vision, and action generally. Our approach assumes that impulses driven by the language and the visual context impinge minutely on a dynamical landscape of attractors corresponding to the potential eye-movement behaviors of the system. We test three unique predictions of our approach in an empirical study in the VWP, and describe an implementation in an artificial neural network. We discuss the Impulse Processing framework in relation to other models of the VWP.