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。     

中学教师工作狂类型与工作绩效的关系研究：基于潜在剖面分析

以229名中学教师对研究对象,采用潜在剖面分析方法探索其工作狂类型,并进一步揭示这些类型与工作绩效间的关系。结果表明:（1）教师按工作狂得分可分为三类：重度工作狂、中等偏高工作狂和中等偏低工作狂;（2）不同类别工作狂会对工作绩效产生不同程度的影响。     

相生相成：拥有生命意义与亲社会行为相互关系的纵向研究

本研究旨在探讨拥有生命意义与亲社会行为的相互关系。研究1对我国10省15所高校2375名学生的拥有生命意义、亲社会行为进行横断面调查。研究2对四川某高校878名学生的拥有生命意义和亲社会行为进行间隔三个月和六个月的三次追踪测量。结果发现：大学生拥有生命意义与亲社会行为具有相互促进的关系,拥有生命意义能稳定正向预测亲社会行为,亲社会行为也能够在一定程度上促进拥有生命意义,但这种促进作用不够稳定;拥有生命意义对利他的、情绪的、匿名的、依从的、紧急的亲社会行为有显著正向影响;而只有利他的亲社会行为能显著预测拥有生命意义。