动机与情境对不同自控水平儿童学业求助的影响

本实验以小学儿童为被试,研究了在不同动机取向和情境条件下的不同自我控制水平儿童的学业求助行为。结果表明：（１）单独无自尊压力的解难题情境比群体压力情境导致更多的求助行为。（２）低自控儿童比高自控儿童产生更多的执行性求助,但主要表现在单独无自尊压力的情境下。（３）在群体压力情境下,自我卷入的儿童比任务卷入的儿童表现出更多的求助行为。（４）在单独无自尊压力的情境下,任务卷入的儿童比自我卷入的儿童表现出     

正负参照点下性别和损益情景对大学生延迟折扣的影响

大量研究表明,延迟折扣受到个体特质性和状态性因素的影响。本研究通过两个实验分别探究正参照点(实验1)和负参照点(实验2)下,特质性因素性别和状态性因素损益情景对大学生个体延迟折扣的影响。实验1研究发现,在正性参照点下,无论男女,收益情景的主观远期价值远高于损失情景。实验2研究发现,在负性参照点下,男女在损益情景的主观远期价值不同;与男性相比,女性的主观远期价值在损失情景中更高,而在收益情景中则无显著差异。本研究结果表明,在不同的参照点下,男女在损益情景中表现了不同的延迟折扣倾向;与男性相比,女性在负性参照点面对损失情景跨期选择时,更愿意选择延迟赔偿。     

社会网络视角下儿童青少年同伴关系网络与欺凌相关行为的共同变化关系

欺凌是一种群体动态过程,同伴生态是欺凌发生发展的重要情境。虽然已有研究重视影响欺凌的同伴因素,但从群体动态关系视角细致探讨欺凌相关行为(欺凌、受欺凌和保护行为)发展变化的研究仍十分有限。从社会网络与欺凌相关行为相互影响的关键问题入手,重点考察欺凌相关行为在同伴关系网络中的发展进程,揭示了同伴关系网络的结构特征和内部关系作为欺凌相关行为是否发生、如何发展的重要生态标志,阐明了存在相同行为特征的个体分群汇聚的选择效应和群体内行为传染的影响效应。未来研究应结合多维社会网络、多类型网络结构和关系特征、不同欺凌角色以及我国本土化特点等进一步加强欺凌治理的群体生态研究,从而更好地为群体生态层面上的欺凌治理提供科学依据。     

Dynamic estimation in the extended marginal Rasch model with an application to mathematical computer-adaptive practice

We introduce a general response model that allows for several simple restrictions, resulting in other models such as the extended Rasch model. For the extended Rasch model, a dynamic Bayesian estimation procedure is provided, which is able to deal with data sets that change over time, and possibly include many missing values. To ensure comparability over time, a data augmentation method is used, which provides an augmented person-by-item data matrix and reproduces the sufficient statistics of the complete data matrix. Hence, longitudinal comparisons can be easily made based on simple summaries, such as proportion correct, sum score, etc. As an illustration of the method, an example is provided using data from a computer-adaptive practice mathematical environment.     

Personality,Resilience, and Psychopathology: A Model for the Interaction between Slow and Fast Network Processes in the Context of Mental Health

Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast-evolving psychopathology network (e.g. a depressive episode) can influence the slow-evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology     

Network structure reveals clusters of associations between childhood adversities and development outcomes

Exposure to childhood adversity is common and associated with a host of negative developmental outcomes. The most common approach used to examine the consequences of adversity exposure is a cumulative risk model. Recently, we have proposed a novel approach, the dimensional model of adversity and psychopathology (DMAP), where different dimensions of adversity are hypothesized to impact health and well‐being through different pathways. We expect deprivation to primarily disrupt cognitive processing, whereas we expect threat to primarily alter emotional reactivity and automatic regulation. Recent hypothesis‐driven approaches provide support for these differential associations of deprivation and threat on developmental outcomes. However, it is not clear whether these patterns would emerge using data‐driven approaches. Here we use a network analytic approach to identify clusters of related adversity exposures and outcomes in an initial study (Study 1: N = 277 adolescents aged 16–17 years; 55.1% female) and a replication (Study 2: N = 262 children aged 8–16 years; 45.4% female). We statistically compare our observed clusters with our hypothesized DMAP model and a clustering we hypothesize would be the result of a cumulative stress model. In both samples we observed a network structure consistent with the DMAP model and statistically different than the hypothesized cumulative stress model. Future work seeking to identify in the pathways through which adversity impacts development should consider multiple dimensions of adversity.     

Neuroevolution-based autonomous robot navigation: A comparative study

The field of neuroevolution has achieved much attention in recent years from both academia and industry. Numerous papers have reported its successful applications in different fields ranging from medical domain to autonomous systems. However, it is not clear which evolutionary optimization techniques lead to the best results. In this paper, multilayer perceptron (MLP) neural networks (NNs) are trained and optimized using four advanced bio-inspired evolutionary algorithms (EA). The algorithms are Multi-Verse Optimizer (MVO), Moth-flame optimization (MFO), Cuckoo Search (CS) and Particle Swarm Optimization (PSO). Each algorithm is equipped with two operators: evolutionary population dynamics and mutation, which impact on exploration and exploitation. Optimized MLPs are then used for the navigation of an autonomous robot. Accuracy and area under the curve metrics are used for the evaluation and comparison metrics. Moreover, two well-regarded gradient descent algorithms including Back propagation (BP) and Levenberg Marquardt (LM) are utilized to validate the results obtained by evolutionary-based MLP trainers. It is observed that MLPs developed using MFO are the most robust ones among MLPs trained using other evolutionary and gradient descent algorithms.     

On portfolio management with value at risk and uncertain returns via an artificial neural network scheme

This paper focuses on the computation issue of portfolio optimization with scenario-based Value-at-Risk. The main idea is to replace the portfolio selection models with linear programming problems. According to the convex optimization theory and some concepts of ordinary differential equations, a neural network model for solving linear programming problems is presented. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the portfolio selection problem with uncertain returns. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.     

A comprehensive analysis of the integration of team research between sport psychology and management

Both sports and organizations rely on teams. As such, the sport psychology and management literatures have contributed greatly to our understanding of team functioning. Despite this, previous reviews based on subsets of articles in these literatures indicate a lack of communication between them. In this article, we assess the state of integration between the entirety of the sport psychology and management literatures on teams by considering the full set of interconnected team articles in the SCOPUS database (6974 articles over 69 years). We use this data to conduct a combination of citation network analysis and content analysis via topic modeling to evaluate conceptual integration. The data show that interdisciplinary discussion between these two fields is lacking, particularly regarding the integration of sport psychology into management research. Whereas 7% of references to team articles in sport psychology come from management journals, only 0.6% of team references in management journals come from sport psychology. Despite this, longitudinal analysis indicates that in the last 10 years the rate of integration between these fields is increasing. We identify specific topics that have accounted for this integration and suggest topics ripe for future integration.     

Key players in sport teams. An exploratory study on the effects of attachment styles on intra-team relational networks

Relationship issues in sport are considered vital for the overall wellbeing of athletes (Jowett, 2005). The way athletes interact, create and maintain relationships is thought to be powerfully influenced by the athletes' attachment states of mind (Thomson & Jaque, 2018). To our knowledge, no study so far has investigated the impact players' attachment styles, as personality dispositions, have on overall intra-team relationship dynamics. This paper examines intra-team social relational networks from the perspective of adult attachment theory. Cross-sectional data were collected from 10 sport teams (158 players) from different sports. Self-report data were collected to assess attachment style and social relationships with teammates. Social network analyses (SNA) were used to calculate players' centrality scores based on incoming and outgoing relational nominations. Multilevel linear analysis was further performed to check inferential relationships between the study variables. Data showed that attachment styles of players, as personality dispositions, affected partly centrality degree positions within the various team social relational networks. The study offers an innovative perspective on the interactional and dynamic world of social relationships in sports teams and confirms that attachment style differences among players predispose them to particular positions within the team social relational networks. Although the current research has an exploratory character, it shows a new direction in the study of groups and sport team dynamics.