不同年级青少年的友伴网络结构

选择小六、初二和高一学生共１０４０名作被试,采用十项友伴提名法,要求被试至多提名１０个友伴,通过网络分析,从友伴提名数、友伴选择的双向性两个方面探讨不同年级学生友伴网络结构,结果显示,青少年平均拥有６～７个友伴,在这些友伴关系中,单向选择占明显的优势。性别、年级和学习成绩之间的交互作用以及父母受教育水平和家庭结构之间的交互作用（包括两两之间的交互作用）除个别外,均不明显;性别、年级和学习成绩的主效应较明显,而父母受教育水平和家庭结构的主效应却不明显。     

Peer networks and bullying in schools

This study looked at how the social constellations in school classes relate to bullying problems. Using peer-evaluation questionnaires, the peer networks of children with different participant roles (such as victim, bully, assistant of bully, reinforcer of bully, defender of victim, outsider) were explored. The subjects were 459 sixth-grade-children (218 girls, 241 boys), aged 11 to 12 years, in Finland. The main findings were: 1) Children who tended to behave in either similar or complementary participant roles in situations of bullying formed networks with each other. The individual child's behavior in bullying situations was strongly connected to how the members of his/her network behaved in such situations. 2) Bullies, assistants, and reinforcers belonged to larger networks than did defenders, outsiders and victims. 3) Children outside the networks were most often victims. It was concluded that behavior in bullying situations can be said to be one feature around which the peer networks in school classes are organized. Thus prevention, as well as intervention strategies against bullying should focus not only on individual children, but also on the wider social context of the class.     

The effects of nodality on the formation of equivalence classes

A four-member equivalence class (A → B → C → D) can be formed by training AB, BC, and CD. The nodal stimuli, B and C, mediate all of the derivative (transitive and equivalence) relations in the class. The derivative relations AC, CA, BD, and DB are separated by one node, whereas AD and DA are separated by two nodes. How do the number of nodes that separate the stimuli in a derivative relation influence the induction of stimulus control exerted by that relation? Seven college students learned two four-member classes made up of nonsense syllables. After training, all derivative relations were presented repeatedly without informative feedback. Stimulus control exerted by each derivative relation was assessed concurrently. For the 7 subjects, control exerted by the derivative relations increased gradually with repeated presentations. With 6 of the 7 subjects, the one-node relations exerted more control than the two-node relations during the process. However, the disparity between the one- and two-node relations decreased with repeated presentations. Eventually, all derivative relations exerted complete control. The control exerted by derivative relations during induction was inversely related to the number of nodes separating the terms in the derivative relations. These results demonstrate that nodal distance is a determinant of the relatedness of stimuli in equivalence classes. The findings are discussed in terms of remote association, semantic memory networks, and the study of transitive inference.     

Structure in semantic memory: A probabilistic approach using a continuous response task

In the continuous response (CR) task, the subject is given the name of a familiar category and must respond with category examples within a fixed time limit. The response sequence and the interresponse times are the measured variables. A general model is proposed for performance in the CR task. The time taken to generate a response is identified with the shortest search time from among a set of simultaneous search processes. Each response has a generation probability that is a function of the parameters governing the search process durations. Data involving the category states in the United States are used to test the model. The model accounts for the performance of nearly all individuals in these data.This article is based on a dissertation completed at the University of California-Berkeley in partial fullfillment of the requirements for the Ph.D. degree in psychology. Portions of the article were presented at the joint meeting of the Psychometric and Classification Societies (June 1984) and at the meeting of the Society for Mathematical Psychology (August 1984). The authors wish to thank Barbara Mellers, Juliet Schaffer, Steve Palmer, and Eleanor Rosch for helpful comments.     

序列语义的类别一致性关系能否进行无意识整合？

阈下语义之间能否进行整合,最近的研究给出了不同的结果。当多个阈下语义词语同时呈现时显示出无意识语义整合效应,但是当序列呈现时没有观察到无意识整合效应。本研究在保留同时呈现方法优点（不同空间呈现位置）的情况下研究了序列呈现的不同汉字词语之间能否产生无意识语义类别关系整合。在实验1中,两个相同或不同类别的词语作为阈下启动刺激先后在两侧呈现,且第一个词语消失后第二词语马上出现,结果显示出无意识语义类别整合效应。实验2延长了两个阈下启动词语之间的时间间隔,结果未发现无意识语义类别整合效应。另外,实验3进一步排除了实验1中第一个启动词语的视觉残留,结果也未观察到显著的无意识语义类别整合效应,说明实验1中观察到的无意识整合效应是视觉残留所致。综合三个实验结果表明,两个阈下词语之间的语义类别一致性关系只有在同时呈现这种特殊条件下才能进行无意识整合加工,说明了短时间维度对于无意识语义关系整合的重要性。     

启动任务中语言经验对藏-汉双语者主导语的影响

采用语义启动任务及藏语与汉语语义相关词和无关词对,探究语言经验能否改变熟练藏-汉双语者的母语主导优势和双语语义表征方式。结果表明,1)无论启动词为藏语还是汉语,藏-汉双语者对汉语L2目标词的加工速度和正确率均显著好于藏语L1,存在语义启动效应; 而在藏语目标词条件下则未发现显著的语义启动效应; 2)藏语到汉语的语义启动效应大于汉语到藏语的启动效应。结果表明,藏-汉双语者的汉语经验对藏语词的表征有影响,但汉语还不能完全替代母语藏语为主导语,结果在一定程度上支持Kroll的RHM模型和Green的ICM模型。     

Deconstructing trait anxiety: a network perspective

Background and objectives: For decades, the dominant paradigm in trait anxiety research has regarded the construct as signifying the underlying cause of the thoughts, feelings, and behaviors that supposedly reflect its presence. Recently, a network theory of personality has appeared. According to this perspective, trait anxiety is a formative construct emerging from interactions among its constitutive features (e.g., thought, feelings, behaviors); it is not a latent cause of these features.

Design: In this study, we characterized trait anxiety as a network system of interacting elements.

Methods: To do so, we estimated a graphical gaussian model via the computation of a regularized partial correlation network in an unselected sample (N?=?611). We also implemented modularity-based community detection analysis to test whether the features of trait anxiety cohere as a single network system.

Results: We find that trait anxiety can indeed be conceptualized as a single, coherent network system of interacting elements.

Conclusions: This radically new approach to visualizing trait anxiety may offer an especially informative view of the interplay between its constitutive features. As prior research has implicated trait anxiety as a risk factor for the development of anxiety-related psychopathology, our findings also set the scene for novel research directions.     

What if they are right? Network approval,expectations, and relationship maintenance behaviors

This study drew on uncertainty reduction and decision‐making theories to investigate how perceptions of approval of romantic relationships from family and friends can influence romantic partners' dynamics. Using a dyadic approach, the authors examined whether expectations of a partner's behavior in the relationship mediated the associations between perceived social network approval and relationship maintenance behaviors in a sample of 137 couples. The actor–partner interdependence mediation model (APIMeM) was applied. Results showed that women's and men's perceptions of approval from their own and their partner's network were associated with their own level of expectations of their partner's behaviors. In turn, women's and men's expectations were associated with their own and their partner's maintenance behaviors.     

Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks

Driving is a highly complex task that involves the execution of multiple cognitive tasks belonging to different levels of abstraction. Traffic emerges from the interaction of a big number of agents implementing those behaviours, but until recent years, modelling it by the interaction of these agents in the so called micro-simulators was a nearly impossible task as their number grows. However, with the growing computing power it is possible to model increasingly large quantities of individual vehicles according to their individual behaviours. These models are usually composed of two sub-models for two well-defined tasks: car-following and lane-change. In the case of lane-change the literature proposes many different models, but few of them use Computational Intelligence (CI) techniques, and much less use personalization for reaching individual granularity. This study explores one of the two aspects of the lane-change called lane-change acceptance, where the driver performs or not a lane-change given his intention and the vehicle environment. We demonstrate how the lane-change acceptance of a specific driver can be learned from his lane change intention and surrounding environment in an urban scenario using CI techniques such as feed-forward Artificial Neural Network (ANN). We work with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) architectures. How they perform one against the other and how the different topologies affect both to the generalization of the problem and the learning process are studied.