The role of epistemic and social characteristics in children’s selective trust: Three meta‐analyses

Over the last 15 years, researchers have been increasingly interested in understanding the nature and development of children’s selective trust. Three meta‐analyses were conducted on a total of 51 unique studies (88 experiments) to provide a quantitative overview of 3‐ to 6‐year‐old children’s selective trust in an informant based on the informant’s epistemic or social characteristics, and to examine the relation between age and children’s selective trust decisions. The first and second meta‐analyses found that children displayed medium‐to‐large pooled effects in favor of trusting the informant who was knowledgeable or the informant with positive social characteristics. Moderator analyses revealed that 4‐year‐olds were more likely to endorse knowledgeable informants than 3‐year‐olds. The third meta‐analysis examined cases where two informants simultaneously differed in their epistemic and social characteristics. The results revealed that 3‐year‐old children did not selectively endorse informants who were more knowledgeable but had negative social characteristics over informants who were less knowledgeable but had positive social characteristics. However, 4‐ to 6‐year‐olds consistently prioritized epistemic cues over social characteristics when deciding who to trust. Together, these meta‐analyses suggest that epistemic and social characteristics are both valuable to children when they evaluate the reliability of informants. Moreover, with age, children place greater value on epistemic characteristics when deciding whether to endorse an informant’s testimony. Implications for the development of epistemic trust and the design of studies of children’s selective trust are discussed.     

Do infants have a sense of numerosity? A p‐curve analysis of infant numerosity discrimination studies

Research demonstrating that infants discriminate between small (e.g., 1 vs. 3 dots) and large numerosities (e.g., 8 vs. 16 dots) is central to theories concerning the origins of human numerical abilities. To date, there has been no quantitative meta‐analysis of the infant numerical competency data. Here, we quantitatively synthesize the evidential value of the available literature on infant numerosity discrimination using a meta‐analytic tool called p‐curve. In p‐curve the distribution of available p‐values is analyzed to determine whether the published literature examining particular hypotheses contains evidential value. p‐curves demonstrated evidential value for the hypotheses that infants can discriminate between both small and large unimodal and cross‐modal numerosities. However, the analyses also revealed that the published data on infants’ ability to discriminate between large numerosities is less robust and statistically powered than the data on their ability to discriminate small numerosities. We argue there is a need for adequately powered replication studies to enable stronger inferences in order to use infant data to ground theories concerning the ontogenesis of numerical cognition.     

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 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.     

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。     

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。     

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。     

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.