Self-Interest, Symbolic Attitudes, and Support for Public Policy: A Multilevel Analysis

This paper examines the role of self-interest and symbolic attitudes as predictors of support for two domestic policy issues—guaranteed jobs and incomes and national health insurance—in the American National Election Survey (ANES) between 1972 and 2004. As was the case in 1976 when Sears, Lau, Tyler, and Allen (1980 ) first explored this topic, symbolic attitudes continue to be much more important predictors of policy attitudes than various indicators of self-interest over the 30 years we analyze. We explore this finding further to determine whether any individual/internal and external/contextual variables affect the magnitude of self-interest effects on policy support. Five possible internal moderators of self-interest effects are examined: (1) political knowledge, (2) issue publics, (3) political values, (4) social identifications, and (5) emotions, but none are found to boost the magnitude of the self-interest effect. However, we do find some evidence that contextual variables representing the social/information environment moderate the impact of self-interest on public opinion.     

空间场景表征中的参照系选取

被试在矩形房间中从两个不同的观察点学习物体场景并在多个朝向上对物体形成的空间关系进行判断,通过控制场景中物体主要内在轴相对于环境结构（房间和地毯）的方向和被试的学习顺序,探讨被试在场景空间表征中采用何种参照系和参照系选取时的影响因素。两个实验结果发现：（1）内在参照系（intrinsic reference systems）和环境参照系均可以用于物体场景的表征,两类参照系之间的关系却是影响被试物体场景表征时参照系选取的重要因素,即当内在参照系与环境参照系方向一致时,被试无论从哪个朝向学习,都选择从垂直于内在参照系和环境参照系的朝向进行表征。反之,当二者方向不一致时,表征时参照系的选择取决于被试的学习经历;（2）无论内在参照系与环境参照系方向是否一致,物体场景本身内在结构的规则性都能够促进空间记忆,即内在结构的规则性既有助于准确编码物体的相对位置,也有助于提高空间关系判断的准确性。     

Ignorance or integration: the cognitive processes underlying choice behavior

The fast‐and‐frugal heuristic framework assumes noncompensatory tools for human preferences (i.e., priority heuristic) and inferences (i.e., take the best heuristic). According to this framework, these heuristics predict choice behavior as well as model the cognitive processes underlying such behavior. The current paper presents two studies that juxtapose predictions derived from these two simple heuristics with alternative predictions derived from compensatory principles. Dependent measures that included reaction time, choice pattern, confidence level, and accuracy were better predicted by compensatory indices than by noncompensatory indices. These findings suggest that people do not rely on limited arguments only, but tend to integrate all acquired information into their choice processes. This tendency was replicated even when the experimental task facilitated the use of noncompensatory principles. We argue that the fast and frugal heuristics can predict the final outcome only under certain conditions, but even in these particular situations they are not applicable to the processes underlying choice behavior. An integrative model for choice behavior is proposed that better represents the data. Copyright © 2009 John Wiley & Sons, Ltd.     

The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person’s linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning.     

Behavioral Health Consultation and Primary Care: Lessons Learned

This article provides an overview of 20 years of professional experiences with developing and implementing a model for integrating behavioral health services into primary care. The Primary Care Behavioral Health (PCBH) model is designed to provide immediate access to behavioral care for a large number of primary care patients by positioning a behavioral health consultant in the exam room area to function as a core member of the primary care team. In an initial era of discovery, the authors were directly involved in developing and testing a variety of new approaches to providing behavioral health services in general medicine. In a second era focused on feasibility, the authors worked with Kaiser Permanente, the United States Air Force and Navy, the Veteran’s Administration, and the Bureau of Primary Care to system test this innovative model of integrated care. Now in an era devoted to dissemination, the authors review the various roles formal research, system level quality improvement initiatives and stakeholder analysis play in promoting integrated care. The authors also describe current efforts to (1) create a tool that helps systems develop integration targets and (2) use the PCBH model as a platform for teaching medical residents and behavioral health providers to work together in a redesigned primary care team model.     

使用验证性补偿多维IRT模型进行认知诊断评估

随着人们对测验反馈结果精细化的需求逐渐提高, 具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型(CDMs)闪耀着光芒的同时, 另一类能够在连续量尺上提供精细反馈的多维IRT模型(MIRTMs)似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能, 本文以补偿模型为视角, 聚焦于分别属于MIRTMs的多维两参数logistic模型(M2PLM)和属于CDMs的线性logistic模型(LLM); 之后为使两者具有可比性, 可对补偿M2PLM引入验证性矩阵(Q矩阵)来界定题目与维度之间的关系, 进而得到验证性的补偿M2PLM (CC-M2PLM), 并通过把潜在特质按切点划分为跨界属性, 以期使CC-M2PLM展现出其本应具有的认知诊断功能; 预研究表明logistic量尺上的0点可作为相对合理的切点; 然后, 通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能, 结果表明CC-M2PLM可用于分析诊断测验数据, 且认知诊断功能与直接使用LLM的效果相当; 最后, 以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。     

Logistic加权模型的理论构建与模拟分析

试题难度、试题考查重要性程度加权是多级记分试题的两个基本属性, 因而在IRT项目特征函数中需用不同参数来表示。以往多级记分模型用多个难度参数来描述多级记分试题的难度, 不能有效的表达多级记分试题的分数权重作用。从多级记分试题的分数加权作用角度, 本文提出Logistic加权模型并论述了理论构建思想。在Logistic加权模型下对项目参数估计的EM算法进行推导并编写了相应的参数估计程序。在Logistic加权模型下进行测验模拟, 发现项目参数估计的模拟返真性能良好。     

共享心智模型研究现状

共享心智模型是指团队成员共同拥有的知识结构,它使得团队成员能就团队作业形成正确的解释和预期,从而协调自己的行为以适应于团队作业和其他团队成员的需求。其研究为理解和提高团队绩效提供了新的视角,从而成为了团队研究的新热点。该文首先明确其定义以及相关概念,再介绍它的几种类型,进而介绍研究中采用的测量方法,最后总结其影响因素及对团队绩效的影响。文章最后指出研究中存在的问题以及今后的研究方向。     

时间因素对空间维度非线索更新的影响

采用实时窗口阅读技术,探讨文本阅读中时间因素在情境模型空间维度非线索更新中的作用。被试阅读角色在空间转换中插入时间间隔的短文,并对其间的物体进行再认探测。结果表明,在文本叙述中,如果在角色空间位置转换之后,增添一定的时间间隔,则可以促进相应空间设置内物体的非线索更新,并且,不同的时间间隔所起的作用是不同的。     

文本阅读中情境模型空间维度的非线索更新

采用实时窗口阅读技术,探讨文本阅读中不同研究范式下空间信息更新的特点。结果表明,阅读中情境模型空间信息的非线索实时更新是存在的,但要实现这种更新则需要一定的条件。相对于自动呈现材料,在更为自由的手动阅读条件下,先前阅读的空间信息,尤其是外显的位置信息（如“广场”）能够进行非线索的实时更新（实验一、实验二）;此外,实验三的结果表明,不同的探测位置对空间信息的通达也产生了影响。在更为敏感的探测位置上,内隐的空间实体信息（如,“雕像”）也表现出了非线索的更新。