成瘾人群跨期选择的神经机制及干预方法

成瘾可分为物质成瘾和行为成瘾, 两类成瘾人群在跨期选择上都表现出高时间折扣率的缺陷, 但也有各自的特异性特征。成瘾人群跨期选择缺陷的神经基础主要集中在评估网络(腹内侧前额叶、纹状体、后扣带皮层等)、认知控制网络(前额叶皮层、前扣带皮层等)和预期想象网络(海马、杏仁核等); 可利用心理训练来改善成瘾人群跨期选择缺陷, 其干预方法包括工作记忆训练、预期想象训练、金钱管理指导等。未来研究应该从成瘾者跨期选择缺陷的认知机制、3个神经网络系统的交互作用机理、行为遗传学以及开发有效的干预方法等方面展开大量研究。     

Understanding patterns of attitude change: When implicit measures show change, but explicit measures do not

A common assumption in research on attitudes is that indirect measures assess relatively stable implicit attitudes, whereas traditional self-report measures assess more recently acquired explicit attitudes that coexist with old, presumably stable implicit attitudes. This assumption seems difficult to reconcile with research showing experimentally induced changes on implicit but not explicit measures. The present research tested a process-account of such asymmetrical patterns. Specifically, we argue that implicit measures show experimental effects that do not emerge on explicit measures when (a) the pairing of an attitude object with positive or negative valence creates new automatic associations in memory, and, at the same time, (b) the consideration of additional information about the attitude object eliminates the impact of automatic associations on self-reported evaluative judgments. Results from three studies support these predictions. Implications for research on attitude change are discussed.     

元代疫灾述论

元代疫灾严重程度难以想象,为害甚重。据史实考察可以发现：元代疫灾虽是由自然界的破坏力及人与人之间社会关系失调共同作用而引发的,但作用主体是有变化的,且时段划分比较明显。疫灾的时间分布密集,空间分布也较为平衡。元政府在防疫方面也有许多积极举措。     

On Similarity Coefficients for 2×2 Tables and Correction for Chance

This paper studies correction for chance in coefficients that are linear functions of the observed proportion of agreement. The paper unifies and extends various results on correction for chance in the literature. A specific class of coefficients is used to illustrate the results derived in this paper. Coefficients in this class, e.g. the simple matching coefficient and the Dice/Sørenson coefficient, become equivalent after correction for chance, irrespective of what expectation is used. The coefficients become either Cohen’s kappa, Scott’s pi, Mak’s rho, Goodman and Kruskal’s lambda, or Hamann’s eta, depending on what expectation is considered appropriate. Both a multicategorical generalization and a multivariate generalization are discussed.     

Who shapes the self of independents and interdependents? Explicit and implicit measures of the self's relatedness to family,friends and partner

We suggest that social relationships shape the self in different ways, depending on whether persons define themselves as independent or interdependent. While the self of independents is most strongly associated with mental representations of others to whom they are related because of their own deliberate action (e.g. friends), the self of interdependents is most strongly connected with representations of others with whom they share allocated group memberships (e.g. family members). We took both explicit (Study 1) and implicit measures (Studies 2, 3 and 4) on how strongly independent and interdependent selves are associated with self‐chosen versus allocated close others. In Studies 3 and 4, we additionally primed the independent or interdependent self. Both explicit and implicit measures indicated that mental representations of family members were more strongly associated with the interdependent self than with the independent self, while romantic partners and friends were connected with both the independent and interdependent self. Copyright © 2006 John Wiley & Sons, Ltd.     

Are members of low status groups perceived as bad, or badly off? Egalitarian negative associations and automatic prejudice

Three studies explored the hypothesis that implicit measures of prejudice can tap negative, yet egalitarian associations. In Study 1, automatically associating African Americans with oppression predicted greater automatic prejudice. In Studies 2 and 3, classically conditioning associations between the novel group Noffians and words like oppressed, maltreated, and victimized led to greater automatic prejudice against Noffians. Results suggest that White Americans’ negative automatic associations with African Americans may partly result from associating members of low status groups with unfair circumstances. Because automatic associations predict prejudiced behaviors, the burden of proof is on those wishing to argue that egalitarian negative associations complicate the assessment of automatic attitudes rather than contribute to prejudiced responses. Discussion focuses on the implications of egalitarian negative associations for the theory and measurement of automatic prejudice.     

Mixed effects modeling of Morris water maze data: Advantages and cautionary notes

Morris water maze data are most commonly analyzed using repeated measures analysis of variance in which daily test sessions are analyzed as an unordered categorical variable. This approach, however, may lack power, relies heavily on post hoc tests of daily performance that can complicate interpretation, and does not target the nonlinear trends evidenced in learning data. The present project used Monte Carlo simulation to compare the relative strengths of the traditional approach with both linear and nonlinear mixed effects modeling that identifies the learning function for each animal and condition. Both trend-based mixed effects modeling approaches showed much greater sensitivity to identifying real effects, and the nonlinear approach provided uniformly better fits of learning trends. The common practice of removing a rat from the maze after 90 s, however, proved more problematic for the nonlinear approach and produced an underestimate of y-axis intercepts.     

Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution then requires an integral equation that is of dimension equal to the number of nonlinear terms. For nonlinear functions that have linear coefficients, the improvement in computational speed and accuracy using the new algorithm can be dramatic. An illustration of the method with repeated measures data from a learning experiment is presented.     

Changing explicit and implicit attitudes: The case of self-esteem

Three experiments investigated predictions concerning asymmetrical patterns of implicit and explicit self-esteem change. Specifically, we investigated the influence of knowledge about the own self that is momentarily salient as well as the influence of affective valence associated with the self in memory on implicit and explicit self-esteem. The latter was induced by evaluative conditioning, the former by directed thinking about oneself. We found that while evaluative conditioning changed implicit but not explicit self-esteem (Experiment 1), thinking about the own self altered explicit but not implicit self-esteem (Experiment 2). Moreover, in a third experiment, it could be shown that the effect of evaluative conditioning can spill over to the explicit level when participants are asked to focus on their feelings prior to making their self-report judgements (Experiment 3). Implications of our results are discussed in terms of recent controversies regarding dual-process models of attitudes and associative versus propositional modes of information processing.