Eating Disorder Symptom and Fear Change Trajectories During Imaginal Exposure Therapy: A Slope Network Analysis

Eating disorders (EDs) are characterized by fears related to food, body image, and social evaluation. Exposure-based interventions hold promise for targeting a range of ED fears and reducing ED psychopathology. We investigated change mechanisms and optimal fear targets in imaginal exposure therapy for EDs using a novel approach to network analysis. Individuals with an ED (N = 143) completed up to four online imaginal exposure sessions. Participants reported ED symptoms and fears at pretreatment, posttreatment, and 6-month follow-up. We constructed networks of symptoms (Model 1), fears (Model 2), and combined symptoms and fears (Model 3). Change trajectory networks from the slopes of symptoms/fears across timepoints were estimated to identify how change in specific ED symptoms/fears related to change in other ED symptoms/fears. The most central changing symptoms and fears were feeling fat, fear of weight gain, guilt about one’s weight/shape, and feared concerns about consequences of eating. In Model 3, change in ED fears bridged to change in desire to lose weight, desiring a flat stomach, following food rules, concern about eating with others, and guilt. As slope networks present averages of symptom/fear change slopes over the course of imaginal exposure therapy, further studies are needed to examine causal relationships between symptom changes and heterogeneity of change trajectories. Fears of weight gain and consequences of eating may be optimal targets for ED exposure therapy, as changes in these fears were associated with maximal change in ED pathology. Slope networks may elucidate change mechanisms for EDs and other psychiatric illnesses.     

Can animation compensate for temporal processing difficulties in deaf people?

This article addresses the issue of animation as an aid for temporal processing difficulties in deaf people learning the Highway Code. A decision-making task involving static or animated road situations was performed by 21 deaf and 24 hearing participants. They were confronted with four types of driving situations (overtaking, negotiating roundabouts, highways, and intersections) and had to decide whether or not to proceed. Participants were presented with two different formats (static vs. animated) and two levels of difficulty (simple vs. complex). Results showed that deaf participants had poorer performances in the static condition than hearing participants. Performance was better in the animated condition than in the static condition, especially in deaf participants. The benefits of animation were greater in complex situations for all participants. Decisions made on dynamic road situations were facilitated by the presence of spatiotemporal dimensions. These proved helpful to deaf candidates who have difficulties in this particular area.     

A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires

This article reviews the causal implications of latent variable and psychometric network models for the validation of personality trait questionnaires. These models imply different data generating mechanisms that have important consequences for the validity and validation of questionnaires. From this review, we formalize a framework for assessing the evidence for the validity of questionnaires from the psychometric network perspective. We focus specifically on the structural phase of validation, where items are assessed for redundancy, dimensionality, and internal structure. In this discussion, we underline the importance of identifying unique personality components (i.e. an item or set of items that share a unique common cause) and representing the breadth of each trait's domain in personality networks. After, we argue that psychometric network models have measures that are statistically equivalent to factor models but we suggest that their substantive interpretations differ. Finally, we provide a novel measure of structural consistency, which provides complementary information to internal consistency measures. We close with future directions for how external validation can be executed using psychometric network models. © 2020 European Association of Personality Psychology     

Towards end-to-end likelihood-free inference with convolutional neural networks

Complex simulator-based models with non-standard sampling distributions require sophisticated design choices for reliable approximate parameter inference. We introduce a fast, end-to-end approach for approximate Bayesian computation (ABC) based on fully convolutional neural networks. The method enables users of ABC to derive simultaneously the posterior mean and variance of multidimensional posterior distributions directly from raw simulated data. Once trained on simulated data, the convolutional neural network is able to map real data samples of variable size to the first two posterior moments of the relevant parameter's distributions. Thus, in contrast to other machine learning approaches to ABC, our approach allows us to generate reusable models that can be applied by different researchers employing the same model. We verify the utility of our method on two common statistical models (i.e., a multivariate normal distribution and a multiple regression scenario), for which the posterior parameter distributions can be derived analytically. We then apply our method to recover the parameters of the leaky competing accumulator (LCA) model and we reference our results to the current state-of-the-art technique, which is the probability density estimation (PDA). Results show that our method exhibits a lower approximation error compared with other machine learning approaches to ABC. It also performs similarly to PDA in recovering the parameters of the LCA model.     

A multiplex view of leadership structure in management teams

In the hope of complementing the structural perspective in upper echelon research and advancing a fine-grained understanding of dyadic leadership influence in management teams, the current study combines two types of intrateam structures—leadership network and friendship network—to create a multidimensional conceptualization of leadership structure in management teams. Specifically, we propose that management teams with a denser singular leadership network (i.e., a network consisting of many leadership ties that are not coupled with friendship ties) should have lower management team cohesion, which subsequently renders worse business unit performance. To contrast, management teams with a denser multiplex network (i.e., a network consisting of many leadership ties that are coupled with friendship ties) should have higher management team cohesion, which subsequently renders better business unit performance. Guided by structural contingency perspective, we further propose that management team task interdependence will strengthen team cohesion's positive impact on business unit performance. To test the hypothesized model, we collected team-level social network data and multiple-wave survey data from 697 managers nested in 148 hotels (i.e., 148 management teams) owned by a large hospitality company. We also obtained objective performance data for each hotel (i.e., occupancy percentage rate and revenue per available room per day) as the criterion measure. The data supported our hypotheses. The theoretical and practical implications of our findings are discussed.     

A Social Network Analysis of Friendship and Spiritual Support in a Religious Congregation

Religious congregations are social settings where people gather together in community to pursue the sacred (Pargament, 2008). Such settings are important to understand as they provide a context for individuals to develop relationships, share ideas and resources, and connect individuals to larger society (Todd, 2017a). Yet, research to date has not deeply examined the inherently relational nature of religious congregations. Thus, in this study, we used social settings theory (Seidman, 2012; Tseng & Seidman, 2007) to develop and test hypotheses about relationships within one Christian religious congregation. In particular, we used social network analysis to test hypotheses about relational activity, popularity, and homophily for friendship and spiritual support types of relational links. Our findings demonstrate how relational patterns may be linked to participation in congregational activities, occupying a leadership role, a sense of community and spiritual satisfaction, stratification, socialization, and spiritual support. Overall, this advances theory and research on the relational aspects of religious congregations, and more broadly to the literature on social settings. Limitations, directions for future research, and implications for theory and religious congregations also are discussed.     

Finding a home: Developmental and neurobiological perspectives on cultural models

This article looks at cultural models in the light of human development, and neurobiological findings in motivation, learning, and cognition. It is argued that at the individual level, the acquisition of cultural models relies on several innate, neurobiologically based motivational, learning, and cognitive systems. These are: (a) a primary motivation to form social bonds which is driven by affect; (b) highly specialized social learning circuits, involving, but not limited to, mirror neuron systems, that facilitate the encoding of social information through implicit, embodied, imitational learning processes; and (c) the formation of culturally based templates for behavior and cognition centered around structures, collectively known as the “default mode network,” which is essential to self‐understanding, autobiographical memory, social cognition, prospection, and theory‐of‐mind. Cultural models, it is argued, are acquired through innate motivational processes that tie the individual emotionally to a secure base of familiar people and customs. This instinctual desire for proximity to others facilitates the efficient, largely implicit, patterning of knowledge and expectations. Shared knowledge and expectations, in turn, create a common, mostly implicit or unconscious, experience of subjectivity within groups. This allows each individual to automatically and effortlessly interact with similarly enculturated others.     

FNDNet – A deep convolutional neural network for fake news detection

With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.     

Characterizing positive and negative ‘voicers’ in elite sport teams: The role of the Five Factor Model and narcissism in players’ frequency and passing on of voice

ObjectiveThis study examined the role of the Five Factor Model and grandiose narcissism in players’ positive (i.e., constructive voice, supportive voice) and negative voice (i.e., defensive voice, destructive voice) in elite sport teams.MethodPlayers from six field hockey and seven korfball teams from the two highest national levels were assessed for four weeks. Using social network analyses, players’ personality was related to their self-reported voice frequency, their voice frequency as perceived by all teammates (other-ratings), and the extent to which they pass on voice.ResultsExtraversion was positively related to players’ frequency of positive and negative voice. Other traits such as conscientiousness and emotional stability were only related to, respectively, positive or negative types of voice. Not all personalities (e.g., extraversion) were consistent in how they assess their own voice versus how others perceive this. Interestingly, traits such as extraversion, emotional stability and the agentic facet of narcissism were found to predict the passing on of voice.ConclusionThis study explored the importance of personality for (a) players’ frequency of a differentiated set of positive and negative voice and (b) the extent to which they function as ‘gates’ that more covertly pass on voice. Further, the results provide perspective on how specific personalities view their voice behavior versus how their teammates perceive their voice behavior. In this way, this study is a first step in identifying players who have the potential to endanger or strengthen a team in a clear or subtle, yet influential way.     

信任的认知神经网络模型

信任是指一方在基于对另一方行为期望的基础上愿意冒一定的风险, 以期在将来得到积极结果的心理过程。近年, 认知神经取向的研究对信任行为引起的特定脑区激活进行了考察, 却忽略了大规模脑网络在信任过程中的整体作用。在总结前人研究的基础上提出信任的认知神经网络模型, 并从认知神经网络视角对信任行为进行解释和整合。在模型中, 信任行为是动力系统、情感系统和认知系统相互作用的结果, 并分别与奖励网络、显著网络、中央执行网络和默认网络等神经网络激活有关。此外, 模型还强调信任行为的反馈机制, 以此构成完整的建构模型。模型阐明了心理系统与中枢神经网络之间的对应关系, 从认知神经角度解释了信任行为发生的心理机制和神经基础。