Just banter? Friendship,teasing and experimental aggression in adolescent peer networks

Aggressive behaviour serves many useful social functions, yet can also have damaging consequences. In line with evidence showing adolescent development in social cognitive abilities, we hypothesized that the use of aggression would become more sophisticated with age. We investigated adolescent aggression towards peers using an experimental, hypothetical aggression paradigm, the hot sauce paradigm, in a school‐based social network setting. Participants (N = 162 aged 11–17, 98 male) indicated which strength of imaginary hot sauce they would allocate to each of their classmates. A Social Network Questionnaire quantified participants’ perceived dyadic social tie strength with each classmate, and the incidence of mutual or unilateral dyadic real‐world aggression (e.g. teasing). Participants allocated weaker hot sauce to peers with whom they reported strong, positive social ties and an absence of self‐reported unilateral real‐world aggression. With increasing cross‐sectional age, there was a decrease in the impact of social tie strength and an increase in the extent to which hot sauce allocation was predicted by self‐reported mutual real‐world aggression. This pattern of findings is consistent with young (vs. late) adolescent use of experimental, hypothetical Hot Sauce aggression to reflect real‐world animosity, while late adolescents’ behaviour is more subtle. These findings extend our understanding of the dyadic social context of adolescent aggressive behaviour using a novel experimental aggression paradigm.     

Analysis of driver’s decision distance and merging distance in work zone area based on parametric survival models: With the aid of a driving simulator experiment

Work zones affect traffic safety and efficiency by changing the road condition and drivers’ maneuver. Therefore, it is important to fully understand drivers’ merging behavior in work zone related areas. In this study, a model framework composed of decision-distance analysis and merging-distance analysis was proposed, which could describe both decision-making and lane-changing process of merging behavior. A road environment with work zone was developed based on a driving simulator, and six scenarios composed of two levels of traffic volume and three different lane-end sign’s locations were designed. Forty-two licensed participants, including 21 females (10 professional drivers vs. 11 normal drivers) and 21 males (15 professional drivers vs. 6 normal drivers) finally completed the experiment. Based on the experimental data, parametric survival models were established to analyze the effects of traffic sign location, traffic situation and driver characteristics on drivers’ decision distance and merging distance. The results showed that: (i) the lane-end sign’s location affected the decision point of lane changing and further affected the merging distance. However, the effect was weakened when the sign was placed far away from the work zone; (ii) merging distance in high traffic volume condition was shorter than that in low traffic volume condition; (iii) work zone posed greater challenges for female drivers as they merged later than males, and females were reluctant to adjust their merging distance according to different gap conditions. The findings shed some light on the future improvement of traffic design and management in work zones.     

ELM-HTM guided bio-inspired unsupervised learning for anomalous trajectory classification

Artificial intelligent systems often model the solutions of typical machine learning problems, inspired by biological processes, because of the biological system is faster and much adaptive than deep learning. The utility of bio-inspired learning methods lie in its ability to discover unknown patterns, and its less dependence on mathematical modeling or exhaustive training. In this paper, we propose a new bio-inspired learning model for a single-class classifier to detect abnormality in video object trajectories. The method uses a simple but dynamic extreme learning machine (ELM) and hierarchical temporal memory (HTM) together referred to as ELM-HTM in an unsupervised way to learn and classify time series patterns. The method has been tested on trajectory sequences in traffic surveillance to find abnormal behaviors such as high-speed, unusual stops, driving in wrong directions, loitering, etc. Experiments have also been performed with 3D air signatures captured using sensors and used for biometric authentication(forged/genuine). The results indicate a significant gain over training time and classification accuracy. The proposed method outperforms in predicting long-time patterns by observing small steps with an average accuracy gain of 15% as compared to the state-of-the-art HTM. The method has applications in detecting abnormal activities in videos by learning the movement patterns as well as in biometric authentication.     

Relationship between environmental factors and burnout of psychotherapists: Meta‐analytic approach

The present study aims to extend the scope of understanding on the relationships between environmental factors and burnout symptoms by targeting and including more extensive occupational demanding variables related to therapist burnout. The present study includes not only common occupational stressors (e.g., working hours, role overload and role conflict) but also variables reflecting the quality of relationship with clients (e.g., case load and negative clientele) that have not been dealt with before in meta‐analytic studies. A meta‐analysis was conducted on 27 original studies published from the year 2006 to 2018. The findings showed that, among environmental factors, role overload had the most significant positive correlations with exhaustion. In addition, negative clientele had the most significant positive correlations with depersonalisation and reduced accomplishment. Furthermore, caseload and low income had relatively weak relations with therapist burnout. Based on these results, implications and limitations of the study are discussed.     

“A definite feel‐it moment”: Embodiment,externalisation and emotion during chair‐work in compassion‐focused therapy

Chair‐work is an experiential method used within compassion‐focused therapy (CFT) to apply compassion to various aspects of the self. This is the first study of CFT chair‐work and is focused on clients' lived experiences of a chair‐work intervention for self‐criticism. Twelve participants with depression were interviewed following the chair‐work intervention and the resulting data were examined using interpretative phenomenological analysis. Three superordinate themes were identified: “embodiment and enactment,” “externalising the self in physical form” and “emotional intensity.” The findings suggest the importance of accessing and expressing various emotions connected with self‐criticism, whilst highlighting the potential for client distress and avoidance during the intervention. The role of embodying, enacting and physically situating aspects of the self in different chairs is also suggested to be an important mechanism of change in CFT chair‐work. The findings are discussed in terms of clinical implications, emphasising how core CFT concepts and practices are facilitated by the chair‐work process.     

A thematic analysis of practitioners' understanding of domestic abuse in terms of post‐traumatic stress disorder (PTSD) and complex‐PTSD (C‐PTSD)

There is a growing body of evidence suggesting that domestic abuse (DA) should be conceptualised within the complex post‐traumatic stress disorder (C‐PTSD) model. Recently, in the draft of the International Classification of Diseases, Eleventh Revision, produced by the World Health Organization (WHO), C‐PTSD was included as a separate criterion in which DA is incorporated (ICD‐11, WHO, 2018). In this study, a thematic analysis was used to explore to what extent practitioners working with DA survivors are familiar with PTSD and C‐PTSD. Research into such a prevalent and detrimental problem as DA is important to understand whether the development of theoretical knowledge about DA and C‐PTSD is addressed in practice. In a Women's Centre in South London, six semi‐structured interviews with middle‐aged female practitioners were conducted to investigate each counsellor's experiences, knowledge and reflections. Six final themes were constructed to summarise the main results. The findings demonstrate limited practitioner understanding of DA in terms of C‐PTSD, which seems to impact not only the effectiveness of treatment plans with DA survivors, but also counsellors’ own psychological and physical states. It is also indicated that DA can be conceptualised within the C‐PTSD model that corresponds with previous literature indicating the complex nature of DA. The overall results of the current research acknowledge that DA sectors should not be neglected and better funding and effective psychoeducation in this field are needed.     

Idiographic network analysis of discrete mood states prior to treatment

Idiographic network models based on time‐series data have received recent attention for their ability to model relationships among symptoms and behaviours as they unfold in time within a single individual (cf. Epskamp, Borsboom, & Fried, 2018; Fisher, Medaglia, & Jeronimus, 2018). Rather than examine the correlational relationships between variables in a sample of individuals, an idiographic network examines correlations within a single person, averaged over many time points. Because the approach averages over time, the data must be stationary (i.e. relatively consistent over time). If individuals experience varying states over time—different mixtures of symptoms and behaviours in one moment or another—then averaging over categorically different moments may undermine model accuracy. Fisher and Bosley (2019) address these concerns via the application of Gaussian finite mixture modelling to identify latent classes of time points in intraindividual time‐series data from a sample of adults with major depressive disorder and/or generalised anxiety disorder (n = 45). The present paper outlines an extension of this work, wherein network analysis is used to model within‐class covariation of symptoms. To illustrate this approach, network models were constructed for each intraindividual class identified by Fisher and Bosley (137 networks across the 45 participants, mean classes/person = ~3, range = 2–4 classes/person). We examine the relative consistency in symptom organisation between each individual's multiple mood state networks and assess emergent group‐level patterns. We highlight opportunities for enhanced treatment personalisation and review nomothetic patterns relevant to transdiagnostic conceptualisations of psychopathology. We address opportunities for integrating this approach into clinical practice and outline potential shortcomings.     

内隐创业领导与多层次积极追随力的互动机制

多层次积极追随力是创业企业成长的重要保证。已有研究强调不同显性领导对积极追随力的影响, 结论存在较多争议。对此, 以内隐领导理论为基础, 构建了“内隐创业型领导-积极追随力”的多层次互动模型。根据此模型, 内隐创业型领导通过关系认同和领导代表性对个体与团队积极追随力产生促进作用; 团队积极追随力通过积极心理资本塑造内隐创业型领导; 团队差序氛围、员工传统性和领导特质调节焦点是互动关系重要的边界条件。     

变量间的网络分析模型及其应用

变量间的网络分析模型近年来被广泛应用于心理学研究。不同于将潜变量作为观测变量的共同先导因素的潜变量模型, 网络分析模型将观测变量作为初级指标, 采用图论的方法建立观测变量之间的关系网络, 其中变量为网络的节点, 而变量间的关系是节点之间的连线。因此网络分析可以突显观测变量之间的联系以及观测变量相互影响而形成的系统。通过变量网络中基于各个节点特征的指标(如中心性)以及基于整体结构特征的指标(如小世界性), 网络分析为研究各种心理现象提供了新的可视化的描述方式和理解视角。近10年来, 网络分析的方法已在人格心理学、社会心理学和临床心理学等领域得到一定的应用。未来研究应继续发展和完善网络分析模型的理论和方法, 使之运用到更多的数据类型和更广的研究领域中。