聋校低年级学生班级友伴网络建构及其特点研究— 一项两年的追踪研究

本文采用调查法与社会网络分析法对聋校三年级聋生在班级群体中的友伴网络进行2年的追踪研究,以探索友伴网络的建构及其动态发展特征。结果表明：①三年级初期,聋生在友伴网络的个体中心度与个体的外貌、成绩与年龄等个人特征有显著相关;但随年级升高,这种相关性逐渐消失。②聋生友伴网络中存在一个十分稳定的中心小团体,其团体成员在整个网络中处于中心地位。③聋生在班级群体中的互选朋友关系呈现出中等程度的稳定性。     

被动性社交网站使用与初中生抑郁的关系：链式中介效应分析

为探讨被动性社交网站使用、冗思、核心自我评价与初中生抑郁的关系,本研究采用被动性社交网站使用问卷、冗思问卷、核心自我评价问卷以及流调中心用抑郁量表对673名初中生进行调查研究。结果显示：（1）被动性社交网站使用与冗思和抑郁均呈显著正相关,而与核心自我评价呈显著负相关;核心自我评价与冗思、抑郁呈显著负相关;冗思与抑郁呈显著正相关;（2）被动性社交网站使用不仅对初中生抑郁具有显著的直接预测作用,而且能够通过三种间接效应对抑郁产生影响,即核心自我评价的单独中介作用、冗思的单独中介作用以及二者的链式中介作用。     

Differences in First Graders' School Adjustment: the Role of Classroom Characteristics and Social Structure of the Group

We examined differences among classes in academic and psychosocial aspects of school adjustment. The sample consisted of 1241 6-7-year-olds in 71 classes from 49 schools. Multilevel analyses showed that 83.5% of the variance was at the child level, 10.3% at the class level, and 6.2% at the school level. Thus, for the part of the variance that was not at the child level, differences among classes were more important than differences among schools. To identify relevant class factors, we focused on classroom characteristics and aspects of the social structure in the class such as integration and openness. Classroom and teacher variables were relatively unimportant compared to social structure indices that yielded significant correlation indicators of social interaction and intellectual competence and explained part of their variance at the class level. This suggested that the structure of the social relations contributed to differences among classes in several aspects of school adjustment.     

Guest Editorial: Who's Afraid of Life After Death?

The evidence for an afterlife is sufficiently strong and compelling that an unbiased person ought to conclude that materialism is a false theory. Yet the academy refuses to examine the evidence, and clings to materialism as if it were a priori true, instead of a posteriori false. I suggest several explanations for the monumental failure of curiosity on the part of academia. First, there is deep confusion between the concepts of evidence and proof. Second, materialism functions as a powerful paradigm that structures the shape of scientific explanations, but is not itself open to question. The third explanation is intellectual arrogance, as the possible existence of disembodied intelligence threatens the materialistic belief that the educated human brain is the highest form of intelligence in existence. Finally, there is a social taboo against belief in an afterlife, as our whole way of life is predicated on materialism and might collapse if near-death experiences, particularly the life review, were accepted as fact.     

Artificial intelligence for the measurement of vocal stereotypy

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.     

On the Meaning of Words and Dinosaur Bones: Lexical Knowledge Without a Lexicon

Although for many years a sharp distinction has been made in language research between rules and words—with primary interest on rules—this distinction is now blurred in many theories. If anything, the focus of attention has shifted in recent years in favor of words. Results from many different areas of language research suggest that the lexicon is representationally rich, that it is the source of much productive behavior, and that lexically specific information plays a critical and early role in the interpretation of grammatical structure. But how much information can or should be placed in the lexicon? This is the question I address here. I review a set of studies whose results indicate that event knowledge plays a significant role in early stages of sentence processing and structural analysis. This poses a conundrum for traditional views of the lexicon. Either the lexicon must be expanded to include factors that do not plausibly seem to belong there; or else virtually all information about word meaning is removed, leaving the lexicon impoverished. I suggest a third alternative, which provides a way to account for lexical knowledge without a mental lexicon.     

On the empirical indistinguishability of knowledge structures

In recent years a number of articles have focused on the identifiability of the basic local independence model. The identifiability issue usually concerns two model parameter sets predicting an identical probability distribution on the response patterns. Both parameter sets are applied to the same knowledge structure. However, nothing is known about cases where different knowledge structures predict the same probability distribution. This situation is referred to as ʻempirical indistinguishabilityʼ between two structures and is the main subject of the present paper. Empirical indistinguishability is a stronger form of unidentifiability, which involves not only the parameters, but also the structural and combinatorial properties of the model. In particular, as far as knowledge structures are concerned, a consequence of empirical indistinguishability is that the existence of certain knowledge states cannot be empirically established. Most importantly, it is shown that model identifiability cannot guarantee that a certain knowledge structure is empirically distinguishable from others. The theoretical findings are exemplified in a number of different empirical scenarios.     

With a little help from my friends: A computational model for the role of social support in mood regulation

The growing interest in the role of social support in mental and physical health has led to the development of several intelligent systems that aim to use social mechanisms to simulate healthy behaviour. In this paper a computational model of a human agent is presented which describes the effect of social support on mood. According to the literature, social support can either refer to the social resources that individuals perceive to be available or to the support that is actually provided in problematic situations. The proposed model distinguishes between both roles of social support. The role of social network characteristics has been taken into account, as an individual can perceive or receive social support through his/her social network. In addition, the number of connections (friends), strength of ties (relationships), social isolation and social integration have been studied. Simulation experiments have been done to analyze the effect of the different types of support in different scenarios and also to analyze the role of various social network characteristics on the mood level. It is shown that support can help to reduce the induced stress and thus can contribute to healthy mood regulation and prevention of depression. The presented model provides a basis for an intelligent support system for people with mood regulation problems that take the social network of people into account.