Social Networks as a Factor in Volunteering Among Elderly Japanese with Lower Socioeconomic Status

We investigated the effect of social networks on volunteering, which is known to be beneficial for people and society, among elderly Japanese of lower socioeconomic status. Western studies have demonstrated that social networks boost volunteering among wealthy people because of the norm that they should help the less fortunate. However, wealthy East Asians do not share this norm. Therefore, socioeconomic variables and volunteering tend not to be significantly related in Japan. We hypothesized that social networks would be more important to volunteering among the Japanese elderly, especially among people of a lower socioeconomic status. A total of 768 respondents (319 men, 432 women, 17 sex unspecified; M_age = 73.99 years, SD = 7.40 years) completed the survey. The results indicated that social networks were positively related to volunteering only among people with lower financial status. This result suggests that social networks can increase opportunities and help less advantaged elderly people to reap the benefits of volunteering.     

Experience With a Linguistic Variant Affects the Acquisition of Its Sociolinguistic Meaning: An Alien-Language-Learning Experiment

How do speakers learn the social meaning of different linguistic variants, and what factors influence how likely a particular social–linguistic association is to be learned? It has been argued that the social meaning of more salient variants should be learned faster, and that learners' pre-existing experience of a variant will influence its salience. In this paper, we report two artificial-language-learning experiments investigating this. Each experiment involved two language-learning stages followed by a test. The first stage introduced the artificial language and trained participants in it, while the second stage added a simple social context using images of cartoon aliens. The first learning stage was intended to establish participants' experience with the artificial language in general and with the distribution of linguistic variants in particular. The second stage, in which linguistic stimuli were accompanied by images of particular aliens, was intended to simulate the acquisition of linguistic variants in a social context. In our first experiment, we manipulated whether a particular linguistic variant, associated with one species of alien in the second learning phase, had been encountered in the first learning phase. In the second experiment, we manipulated whether the variant had been encountered in the same grammatical context. In both cases we predicted that the unexpectedness of a new variant or a new grammatical context for an old variant would increase the variant's salience and facilitate the learning of its social meaning. This is what we found, although in the second experiment, the effect was driven by better learners. Our results suggest that unexpectedness increases the salience of variants and makes their social distribution easier to learn, deepening our understanding of the role of individual language experience in the acquisition of sociolinguistic meaning.     

The Role of Network Support and Interference in Women's Perception of Romantic,Friend, and Parental Relationships

This study examined the association between support/interference from the best friend and closest parent to women's (a) satisfaction with the parent–daughter relationship, (b) satisfaction with the friendship, and (c) love for the romantic partner. The respondents (n = 162 females; 84% Caucasian, 1% Asian American, 10% Hispanic, 4% African American, and 1% Multiracial) completed a questionnaire packet to assess each of the factors. Results revealed that romantic love was unrelated to friend support, friend interference, or parental interference, but positively related to parental support. Parent support was a significant correlate of parent satisfaction, and a similar pattern emerged between friend support/friendship satisfaction. Further, best friend support moderated the relationship between friend interference and friendship satisfaction, such that interference was negatively related to satisfaction in low support conditions. Overall, the results suggested that network reactions to romance played a limited role in romantic affection, but were more strongly associated with network satisfaction.     

Sensitivity of MRQAP Tests to Collinearity and Autocorrelation Conditions

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions. Special thanks go to Cajo Ter Braak, Philip Hans Franses, Patrick Houweling, Pierre Legendre, three anonymous reviewers, the associate editor, and the editor for comments.     

等级效标分数的概率神经网络预测方法研究

针对基于统计学的等级效标分数预测存在的问题,提出了运用概率神经网络进行等级效标分数预测的方法。在20种条件下进行了计算机模拟实验,包括测验分数为单变量和多变量,以及各种水平的白噪声干扰条件,结果表明在测验分数为多变量的情况下,或者在有白噪声干扰的条件下,运用概率神经网络方法可以比统计学方法更好地对等级效标分数进行预测。     

Conceptual Hierarchies in a Flat Attractor Network: Dynamics of Learning and Computations

The structure of people's conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical ( Collins & Quillian, 1969 ). For example, superordinate concepts ( vegetable ) are assumed to reside at a higher level than basic-level concepts ( carrot ). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts ( vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory.     

Joint effects of social networks and information giving on innovative performance after organizational entry

We combine the social network approach and organizational socialization literature in order to examine the influence of social networks and proactive behavior on newcomers' innovative performance. A sample of new employees completed questionnaires on social network and proactive measures, and their supervisors rated their innovative performance. The results suggest that both sparse (low density) social networks and newcomers' information giving were related to innovative performance. The results also indicate that information giving moderated the relation between sparse social networks and innovative performance: when newcomers' information giving was high, the characteristics of their social networks were not related to their innovative performance.     

It is more than personal: Development and validation of a multidimensional privacy orientation scale

This study summarizes the development and validation of a multidimensional privacy orientation scale designed for measuring privacy attitudes of Social Network Site (SNS) users. Findings confirm the existence of four dimensions: (1) belief in the value of “privacy as a right”; (2) “other-contingent privacy”; (3) “concern about own informational privacy” and (4) “concern about privacy of others.” Moreover, a segmentation of SNS users based on these attitude scores reveals three types of users: (1) privacy advocates, who are concerned about both their own and other people’s privacy; (2) privacy individualists, who are concerned mostly about their own privacy, and (3) privacy indifferents, whose score on all dimensions are lower than other segments. The results indicate that the four privacy orientation dimensions and three user segments predict key differences in terms of privacy protective behavior, information disclosure, and viewing personal information of others.     

Integrating the Automatic and the Controlled: Strategies in Semantic Priming in an Attractor Network With Latching Dynamics

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category‐exemplar, mediated and backward prime‐target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model.