Projecting group liking and ethnocentrism on in-group members: False consensus effect of attitude strength

The purpose of the present study was to examine the social projection effect concerning the strength of national attitudes. Japanese respondents sampled from the general population judged how patriotic and nationalistic opinions were distributed among Japanese (i.e. in-group) and American (out-group) citizens. The respondents' own positions regarding these attitude dimensions were also measured. As predicted, the respondents inflated estimates of the endorsement for positions close to their own, particularly when the target was the in-group. Estimates of opinion distributions in the out-group converged around stereotypic positions. The apparent projection effect in patriotism (i.e. in-group liking) was likely mediated by abstract trait evaluations of the home country, while the projection of nationalism (i.e. ethnocentrism) appeared to be a direct result of projection without such mediation. Different processes such as the motivation for cognitive consistency and the need to achieve social identity of the group were suggested to underlie projection effects in different domains. Implications of the results for the study of stereotyping and intergroup conflict are discussed.     

Identity Hierarchy Within the Sudanese Superordinate Identity: Political Leadership Promoting and Demoting Subordinate Groups

Social psychology often emphasizes the link between superordinate identities and intergroup harmony. Other research, however, has illuminated the possible pitfalls of such approaches, pointing at the potentially hierarchical nature of superordinate identities. Yet the research largely ignores who invokes and mobilizes specific definitions of superordinate identities. Using interviews with political leaders and participants from the general population, this article explores a non‐Western conflict case with a hierarchical government‐defined superordinate identity: Sudan. Focus is on the government demoting and promoting different subordinate identities within the superordinate. The criteria for the highest‐level subordinate category within the superordinate identity are discussed as pertaining to three factors—Muslim, Arabic speaking and Arab. Most participants discuss this superordinate identity as yielding an identity hierarchy and strengthening subordinate identities, thereby demonstrating the potential detrimental nature of superordinate identities. The article highlights the context dependency of the link between superordinate identity and intergroup harmony and adds to the void in research on the role of leadership in constructing superordinate identities.     

投射性认同的三次转向：内涵演变与概念比较

投射性认同发端自Freud的投射概念, 后由Klein正式提出, 经过Bion, Resenfeld, Grotstein等人的发展, 已经成为精神分析的核心概念之一。其内涵演变经历了从单向投射到双向互动, 从内心幻想到现实交流, 从母婴关系到咨访关系的三次重要转向。投射性认同较投射而言是一种更成熟复杂的防御机制, 与移情的差异则体现在起因、内容和结果等方面。近年来, 神经精神分析的兴起与镜像神经元的发现为理解投射性认同的发生机制提供了神经生物学的基础。     

客体投影方式对空间问题解决和再认的影响

通过设置四种实验条件,旨在阐明空间问题解决和再认的水平是如何受到客体不同投影方式的影响。除进一步支持了正投影问题的解决是以一个具有三维结构特性的心理表征为基础而并非是对二维正投影信息进行了充分识别的观点外,研究结果还表明：（１）这个具有客体三维结构特性的心理表征是建构于对正投影问题解决而非轴测投影图的再认过程中;（２）正投影问题解决的复杂性显著地高于轴测投影图问题解决而且一个建构于正投影问题解决中     

Rotation to Simple Loadings Using Component Loss Functions: The Oblique Case

Component loss functions (CLFs) similar to those used in orthogonal rotation are introduced to define criteria for oblique rotation in factor analysis. It is shown how the shape of the CLF affects the performance of the criterion it defines. For example, it is shown that monotone concave CLFs give criteria that are minimized by loadings with perfect simple structure when such loadings exist. Moreover, if the CLFs are strictly concave, minimizing must produce perfect simple structure whenever it exists. Examples show that methods defined by concave CLFs perform well much more generally. While it appears important to use a concave CLF, the specific CLF used is less important. For example, the very simple linear CLF gives a rotation method that can easily outperform the most popular oblique rotation methods promax and quartimin and is competitive with the more complex simplimax and geomin methods. The author would like to thank the editor and three reviewers for helpful suggestions and for identifying numerous errors.     

Social Categorization Moderates Social Projection

When categorized into social groups, people view members of in-groups, but not members of out-groups, as being similar to themselves. In three experiments, social categorization moderated the spread of social projection in both minimal and value-tagged laboratory groups and regardless of whether individual perceivers judged both groups or only one. The categorization effect tracked changes in the perceiver's group status so that most perceivers projected only to present but not past in-groups. The lack of out-group projection supported an anchoring hypothesis, according to which self-referent information is engaged only when it is considered applicable to the judgment at hand. The induction hypothesis and the differentiation hypothesis, which predicted positive and negative out-group projection, respectively, were not supported. Implications for theories of intergroup perception and bias are discussed.     

Generalized constrained multiple correspondence analysis

A comprehensive approach for imposing both row and column constraints on multivariate discrete data is proposed that may be called generalized constrained multiple correspondence analysis (GCMCA). In this method each set of discrete data is first decomposed into several submatrices according to its row and column constraints, and then multiple correspondence analysis (MCA) is applied to the decomposed submatrices to explore relationships among them. This method subsumes existing constrained and unconstrained MCA methods as special cases and also generalizes various kinds of linearly constrained correspondence analysis methods. An example is given to illustrate the proposed method.Heungsun Hwang is now at Claes Fornell International Group. The work reported in this paper was supported by Grant A6394 from the Natural Sciences and Engineering Research Council of Canada to the second author.     

Riemannian Newton and trust-region algorithms for analytic rotation in exploratory factor analysis

In exploratory factor analysis, latent factors and factor loadings are seldom interpretable until analytic rotation is performed. Typically, the rotation problem is solved by numerically searching for an element in the manifold of orthogonal or oblique rotation matrices such that the rotated factor loadings minimize a pre-specified complexity function. The widely used gradient projection (GP) algorithm, although simple to program and able to deal with both orthogonal and oblique rotation, is found to suffer from slow convergence when the number of manifest variables and/or the number of latent factors is large. The present work examines the effectiveness of two Riemannian second-order algorithms, which respectively generalize the well-established truncated Newton and trust-region strategies for unconstrained optimization in Euclidean spaces, in solving the rotation problem. When approaching a local minimum, the second-order algorithms usually converge superlinearly or even quadratically, better than first-order algorithms that only converge linearly. It is further observed in Monte Carlo studies that, compared to the GP algorithm, the Riemannian truncated Newton and trust-region algorithms require not only much fewer iterations but also much less processing time to meet the same convergence criterion, especially in the case of oblique rotation.