Group‐based evaluations for pupil‐on‐teacher violence: The impact of teacher intervention strategy

Although extreme violence to teachers is rare, the fact remains that in the UK, 29% of teachers report having been physically assaulted by a pupil (ATL, 2008a ). The ways in which responsibility for such assaults are attributed can have legal, educational and managerial implications. In the current study, teachers (N = 66), pupils (N = 68) and parents (N = 64) from a large secondary school in the UK read an incident report form outlining an incident depicting a pupil physically assaulting a teacher. The incident report was manipulated such that, prior to being assaulted, the teacher had either separated the assailant pupil from another pupil using a physical or non‐physical intervention. Results revealed that participating parents' and teachers' evaluations of the assailant's parents and the teacher differed from those of pupils in several ways. The results are discussed in terms of group‐based responsibility for deviant behaviour and implications for teacher behaviour in response to pupil on teacher violence. Copyright © 2010 John Wiley & Sons, Ltd.     

趋近和回避动机的区分及其对心理病理学的影响

趋近和回避是动机的两种最基本形式, 反映着个体与环境的相互作用方式, 是个体适应环境的核心机能。回避动机保证了个体的生存, 趋近动机则促进个体的成长。两类动机系统在前额叶皮层呈不对称偏侧化分布, 趋近动机与左侧额叶皮层激活相连, 回避动机与右侧额叶皮层激活相连。Youngstorm 和Izard等认为两类动机系统失调可能与一系列的情绪和行为问题有关, 如躁狂、抑郁、焦虑和儿童多动症等。这一观点已得到一些相关研究和临床研究证实。建议未来研究关注趋近-回避动机区分与情绪和认知功能研究的融合, 进一步检验趋近-回避动机系统失调模型, 并加强趋近和回避动机系统的可塑性研究。     

想象未来的认知加工成分及其脑网络

合理预期和想象未来事件或场景, 有利于个体做出适应性反应, 促进生存与发展。近年来, 研究者对想象未来的认知加工成分和神经机制进行了深入探讨, 提出了想象未来的相关概念和理论。我们首先概述了想象未来实验常用的思维采样范式和词语-线索范式, 接着介绍了想象未来的自我参照加工、心理场景构建等认知加工成分。此外, 特别介绍了想象未来的默认网络模型以及与之相关的其他大尺度脑网络, 并进一步提出了想象未来的脑网络研究框架。未来的研究可从想象未来的动态脑网络、复杂脑网络以及与疾病的关系等角度入手, 深入探究想象未来的神经机制。     

Never quite good enough: The paradox of sticky developmental relationships for elite university graduates

Drawing on the mentoring, education, and social psychology literatures, this longitudinal study examines how the persistence of developmental relationships from an elite graduate school influences subjective career outcomes during early career. Participants (n = 136) were surveyed about their developmental networks—a group of individuals who take an active interest in and action to advance a focal individual’s career—and subjective career outcomes over the 10 years (1996-2006) post graduation. Results show that although receiving mentoring from one’s entire developmental network was positively related to career-related self-efficacy and perceptions of career success, this was not the case for ties retained from graduate school. Continuing to receive mentoring support from developers from graduate school and further, from peers from graduate school, was negatively related to perceptions of career success. These findings offer insight into the dynamics and potentially negative consequences of developmental networks and highlight the significance of social comparison during early career.     

Effects of Attention on the Strength of Lexical Influences on Speech Perception: Behavioral Experiments and Computational Mechanisms

The effects of lexical context on phonological processing are pervasive and there have been indications that such effects may be modulated by attention. However, attentional modulation in speech processing is neither well documented nor well understood. Experiment 1 demonstrated attentional modulation of lexical facilitation of speech sound recognition when task and critical stimuli were identical across attention conditions. We propose modulation of lexical activation as a neurophysiologically plausible computational mechanism that can account for this type of modulation. Contrary to the claims of critics, this mechanism can account for attentional modulation without violating the principle of interactive processing. Simulations of the interactive TRACE model extended to include two different ways of modulating lexical activation showed that each can account for attentional modulation of lexical feedback effects. Experiment 2 tested conflicting predictions from the two implementations and provided evidence that is consistent with bias input as the mechanism of attentional control of lexical activation.     

Social psychology and social networks: Individuals and social systems

Social psychology and social network research are both centrally concerned with human sociality. Despite some historically significant interactions between the two, these areas of investigation have not been usefully deployed together in recent research endeavours. The present paper attempts to bring out some points of both theoretical and methodological contentions, to characterize the gap between them, to traverse briefly the trajectories of their historical development, and to provide some concrete instances of these differences. Intellectual resources available to Asian social psychology are reviewed, which may help bridge the gap between the two areas of research. We conclude by calling for a greater integration of social psychology and social network perspectives in future research.     

Who do students with mild disabilities nominate as cool in inclusive general education classrooms?

Examined the nominations that elementary students with mild disabilities made for peers they perceived as cool. The total sample was comprised of 948 students (496 girls, 452 boys) from the metropolitan Chicago area and North Carolina and included 107 (11.3%) students with mild disabilities. Overall, students with mild disabilities nominated prosocial and athletic peers who affiliate with students with mild disabilities. However, aggressive boys who are central in the social network were highly likely to view aggressive peers as cool. Results are discussed in relation to implications for social interventions for students with mild disabilities.     

Memory Maintenance

ABSTRACT— Change in the synaptic communication between neurons—known as synaptic plasticity—plays a key role in learning and memory. It is not yet clear, however, whether the properties of synaptic plasticity are sufficient to account for long-term-memory maintenance. Recent studies have revealed that synaptic plasticity can indeed persist for weeks or months, as might be expected of a long-term-memory mechanism. However, memories encoded by neural systems are not static; they continue to evolve as new learning occurs. Furthermore, neural-network modeling has shown that synapses must be able to reconfigure their connection strengths during new learning if old information is to be preserved. Recent tests confirm that synapses, once modified, retain their capacity for further modification, indicating that they can indeed operate in the manner predicted to be necessary for memory maintenance in a dynamic learning network.     

Are unsupervised neural networks ignorant? Sizing the effect of environmental distributions on unsupervised learning

Learning environmental biases is a rational behavior: by using prior odds, Bayesian networks rapidly became a benchmark in machine learning. Moreover, a growing body of evidence now suggests that humans are using base rate information. Unsupervised connectionist networks are used in computer science for machine learning and in psychology to model human cognition, but it is unclear whether they are sensitive to prior odds. In this paper, we show that hard competitive learners are unable to use environmental biases while recurrent associative memories use frequency of exemplars and categories independently. Hence, it is concluded that recurrent associative memories are more useful than hard competitive networks to model human cognition and have a higher potential in machine learning.     

Semantic boost on episodic associations: an empirically-based computational model

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words more easily than unrelated words; this advantage increased linearly with repeated co-occurrence. Second, we developed a computational model, SEMANT, suggesting a possible mechanism for this semantic-episodic interaction. In SEMANT, episodic associations are implemented through lateral connections between nodes in a pre-existent self-organized map of word semantics. These connections are strengthened at each instance of concomitant activation, proportionally with the amount of the overlapping activity waves of activated nodes. In computer simulations SEMANT replicated the dynamics of associative learning in humans and led to testable predictions concerning normal associative learning as well as impaired learning in a diffuse semantic system like that characteristic of schizophrenia.