The organization of construal networks and functional adaptation

This review examined the association of construal network organizations with functional adaptation and psychological well-being. Recent neuropsychological research supports the presence of distinct construal networks in the brain that organize action at different levels of goals and tasks. Construal networks are sets of connected construals, or mental representations of objects, events, and behaviors. Little attention, however, has been given to how the organization of construal networks promotes functional adaptation. Cognitive processes, construal levels, personal meaning, cultures, and situations influence the configurations of construal networks. The reviewed evidence indicated that construal network organization facilitates functional adaptation and well-being, either though the coherence or fit of the assembled construals with each other or through the mediation of their fit with situations or contexts, like a culture. This review goes beyond previous studies by describing the constructive, creative, and hypothetical aspects of construal organizations and their effects on functional adaptation and psychological well-being.     

Acute exercise and children’s cognitive functioning: What is the optimal dose of cognitive challenge?

Acute bouts of exercise have the potential to benefit children’s cognition. Inconsistent evidence on the role of qualitative exercise task characteristics calls for further investigation of the cognitive challenge level in exercise. Thus, the study aim was to investigate which “dose” of cognitive challenge in acute exercise benefits children’s cognition, also exploring the moderating role of individual characteristics. In a within-subject experimental design, 103 children (M_age = 11.1, SD = 0.9, 48% female) participated weekly in one of three 15-min exergames followed by an Attention Network task. Exergame sessions were designed to keep physical intensity constant (65% HR_max) and to have different cognitive challenge levels (low, mid, high; adapted to the ongoing individual performance). ANOVAs performed on variables that reflect the individual functioning of attention networks revealed a significant effect of cognitive challenge on executive control efficiency (reaction time performances; p = .014, ƞ²_p = .08), with better performances after the high-challenge condition compared to lower ones (ps < .015), whereas alerting and orienting were unaffected by cognitive challenge (ps > .05). ANOVAs performed on variables that reflect the interactive functioning of attention networks revealed that biological sex moderated cognitive challenge effects. For males only, the cognitive challenge level influenced the interactive functioning of executive control and orienting networks (p = .004; ƞ²_p = .07). Results suggest that an individualized and adaptive cognitively high-challenging bout of exercise is more beneficial to children’s executive control than less challenging ones. For males, the cognitive challenge in an acute bout seems beneficial to maintain executive control efficiency also when spatial attention resources cannot be validly allocated in advance. Results are interpreted referring to the cognitive stimulation hypothesis and arousal theory.     

探究事件相关脑电/脑磁信号中的神经表征模式：基于分类解码和表征相似性分析的方法

探究不同心智活动下的神经表征差异, 是认知神经科学关注的核心问题之一。早期的脑电/脑磁分析方法主要关注组平均后的神经响应水平, 这要求在关注的时间进程上, 各个被试在相同刺激条件下事件相关电位/事件相关磁场的振幅大小和方向、以及地形图分布和极性均要有较高的一致性。近些年来, 研究者们将功能性磁共振成像研究中常用到的两种技术——机器学习中的分类算法(即基于分类的解码)和表征相似性分析——引入到了脑电/脑磁数据分析中。这两种新技术可以克服传统脑电/脑磁数据基于具体电压/磁感应强度波形平均分析的缺点, 具有在个体水平上探究神经表征编码的特点, 为人们探究大脑在不同时间进程上如何对特定的神经表征信息进行动态编码提供了新的思路。两种技术基于不同的方法学原理来抽提个体间一致的脑认知加工机制, 还为脑电/脑磁研究开展跨时域、跨任务、跨模态、跨群体比较不同认知过程中的表征差异提供了更多新颖的途径。我们首先通过与传统的脑电/脑磁分析方法进行比较, 系统性介绍了基于分类的解码和表征相似性分析的原理和操作流程, 之后对两种方法的应用场景进行了梳理, 并在最后对未来可供研究的方向提出了我们的见解。     

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 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.     

Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

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.     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

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.