Timing in cognition and EEG brain dynamics: discreteness versus continuity

This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of operational architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts in Brain Mind 2:291-29, 2001; Neurosci Biobehav Rev 28:827-836, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based on the OA framework, that brain functioning is best conceptualized in terms of continuity-discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research.     

Functional Status, Receipt of Help, and Perceived Health

We model the effects of disability, functional limitation, and receipt of help on perceived health. This analysis specifies a model with two dimensions of disability and three dimensions of functional limitation, including upper body disability, lower body disability, basic activities of daily living (ADLs), household ADLs, and advanced ADLs. The latent variables of receiving help are modeled for each of the ADLs as intervening between limitations and perceived health. The results show that instrumental social support in the form of receiving help for ADLs has no substantive independent effect on perceived health. In addition, receiving instrumental social support shows a pattern of task-specific responses to individual measures of need. Implications for both caregiver and care recipient are discussed.     

The parental support networks of mothers and fathers: A multidimensional approach

This paper argues for a multidimensional approach to the study of the social support networks of mothers and fathers of young children. Network structure (e. g. size, density, member characteristics), support content (e. g. emotional, informational, economic and child care) and support quality (e. g. helpfulness, satisfaction with support, conflict associated with support and willingness to access support in future need) are distinguished. The goals of the study were: (1) to empirically test the multidimensionality of parental support networks; (2) to assess the relations among these three dimensions for mothers and for fathers; and (3) to examine how couples' support networks are interrelated. Individual at-home interviews were conducted with 100 parents to derive measures of parental support structure, content and quality. Fathers (N = 31) were compared to their spouses (N = 31) and to a comparison group of demographically similar married mothers (N = 38). For all three groups, the same three predicted dimensions emerged in the factor structure of their networks; in general, few gender differences were found. However, relations among structure, content and quality differed by gender, with network structure predicting support content and quality only for mothers. The network structure of couples was positively correlated, and overlap in network members was related to own and spouse's network helpfulness, satisfaction and child care support.     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.     

Individual differences in harm-related moral values are associated with functional integration of large-scale brain networks of emotional regulation

Emotions affects moral judgements, and controlled cognitive processes regulate those emotional responses during moral decision making. However, the neurobiological basis of this interaction is unclear. We used a graph theory measurement called participation coefficient (‘PC’) to quantify the resting-state functional connectivity within and between four meta-analytic groupings (MAGs) associated with emotion generation and regulation, to test whether that measurement predicts individual differences in moral foundations-based values. We found that the PC of one of the MAGs (MAG2) was positively correlated with one of the five recognized moral foundations–the one based on harm avoidance. We also found that increased inter-module connectivity between the ventromedial prefrontal cortex, dorsolateral prefrontal cortex and middle temporal gyrus with other nodes in the four MAGs was likewise associated with higher endorsement of the Harm foundation. These results suggest that individuals' sensitivity to harm is associated with functional integration of large-scale brain networks of emotional regulation. These findings add to our knowledge of how individual variations in our moral values could be reflected by intrinsic brain network organization and deepen our understanding of the relationship between emotion and cognition during evaluations of moral values.     

The protective role of social network factors during the COVID-19 pandemic in the United States

The present study examined the relationship between COVID-19 threat perception, isolating health precautions, and loneliness. As a test of the stress-buffering hypothesis (Cohen & Wills, 1985), this study also examined if social network factors representing various aspects of social support moderated, or weakened, the relationship between threat perception, isolating health precautions, and loneliness. Participants (N = 1149) provided information about themselves, as well as 15 other people they know via an online survey. We found that structural and compositional social network factors, density, number of close alters, network threat perception, network covid cautiousness and number of vaccinated alters all negatively related to loneliness. Further, using moderated mediation analyses, we found that network threat perception and network covid cautiousness moderated the indirect relationship between threat perception and loneliness through precautions. At high levels of these factors, the mediation was less likely to be significant suggesting that the social network factors may buffer people from the loneliness that sometimes comes with engaging in isolating health precautions in response to the perceived threat of COVID-19.     

空间导航的脑网络基础和调控机制

空间导航在生活中时刻发生,空间能力衰退是阿尔兹海默症的重要早期表现。早期关于空间导航神经机制的研究主要关注单个脑区的特异性功能,但这些脑区如何交互以整合不同模态的信息支持复杂导航行为尚不清楚。脑成像技术、脑网络建模方法和神经调控手段的发展,为在脑网络水平理解人类空间导航的认知神经机制提供了重要研究手段。本研究试图融合空间导航认知神经机制研究的最新进展,借助脑网络建模、大数据分析、微电流刺激等前沿研究手段,研究空间导航脑网络的关键拓扑属性特征(如模块化、核心节点等),探寻该功能特异性神经网络的重要影响因素和调控机制,并构建空间导航的脑网络理论模型。研究成果将有利于理解人类复杂导航行为的脑网络基础,为阿尔兹海默症等相关认知障碍脑疾病的筛查和诊断提供重要参考。     

The social network of solidarity with migrants: The role of perceived injunctive norms on intergroup helping behaviors

Using an ego-centered network approach, we examine across two studies whether and how injunctive network norms—behaviors that are approved by alters—are related to majority members’ decisions to participate in helping actions supporting migrants. We hypothesize that the more people perceive their personal social networks as positive toward humanitarian actions for migrants, the more they consider their opinions on migration issues as self-defining, and the more they are willing to mobilize in helping behaviors. With a name generator approach, we collected personal social network data among majority members of Belgian, mobilized volunteers (Study 1, N = 204) and Swiss, non-mobilized participants (Study 2, N = 247). Results demonstrate the impact of injunctive network norms in promoting and maintaining helping actions for migrants, and the role of self-defining attitudes. Overall, the results highlight the importance of injunctive norms within personal social networks for participation in intergroup helping behaviors.     

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.     

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.