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.     

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。     

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。     

Enhanced Verbal Statistical Learning in Glossolalia

Glossolalia (“speaking in tongues”) is a rhythmic utterance of word-like strings of sounds, regularly occurring in religious mass gatherings or various forms of private religious practices (e.g., prayer and meditation). Although specific verbal learning capacities may characterize glossolalists, empirical evidence is lacking. We administered three statistical learning tasks (artificial grammar, phoneme sequence, and visual-response sequence) to 30 glossolalists and 30 matched control volunteers. In artificial grammar, participants decide whether pseudowords and sentences follow previously acquired implicit rules or not. In sequence learning, they gradually draw out rules from repeating regularities in sequences of speech sounds or motor responses. Results revealed enhanced artificial grammar and phoneme sequence learning performances in glossolalists compared to control volunteers. There were significant positive correlations between daily glossolalia activity and artificial grammar learning. These results indicate that glossolalists exhibit enhanced abilities to extract the statistical regularities of verbal information, which may be related to their unusual language abilities.     

Social Networks as a Factor in Volunteering Among Elderly Japanese with Lower Socioeconomic Status

We investigated the effect of social networks on volunteering, which is known to be beneficial for people and society, among elderly Japanese of lower socioeconomic status. Western studies have demonstrated that social networks boost volunteering among wealthy people because of the norm that they should help the less fortunate. However, wealthy East Asians do not share this norm. Therefore, socioeconomic variables and volunteering tend not to be significantly related in Japan. We hypothesized that social networks would be more important to volunteering among the Japanese elderly, especially among people of a lower socioeconomic status. A total of 768 respondents (319 men, 432 women, 17 sex unspecified; M_age = 73.99 years, SD = 7.40 years) completed the survey. The results indicated that social networks were positively related to volunteering only among people with lower financial status. This result suggests that social networks can increase opportunities and help less advantaged elderly people to reap the benefits of volunteering.     

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.     

Surface features can deeply affect artificial grammar learning

Three experiments explored the extent to which surface features explain discrimination between grammatical and non-grammatical strings in artificial grammar learning (AGL). Experiment 1 replicated Knowlton and Squire’s (1996) paradigm using either letter strings as in the original study, or an analogous set of color strings to further explore if learning was affected by type of stimuli. Learning arose only with letter strings, but the results were mostly due to the discrimination of non-grammatical strings containing highly salient illegal features. Experiments 2 and 3 tested a new grammar devised to control for those features. Experiment 2 showed reduced grammar learning effects, and again only for letter materials. Experiment 3 explored the effect of additional practice with letter stimuli, and found increased learning only in the spaced practice condition, though additional practice also produced more explicit knowledge. These findings call for further research on the boundary conditions of learning in AGL paradigms.     

PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis

This paper introduces a novel PSO-GA based hybrid training algorithm with Adam Optimization and contrasts performance with the generic Gradient Descent based Backpropagation algorithm with Adam Optimization for training Artificial Neural Networks. We aim to overcome the shortcomings of the traditional algorithm, such as slower convergence rate and frequent convergence to local minima, by employing the characteristics of evolutionary algorithms. PSO has a property of faster convergence rate, which can be exploited to account for the slower pace of convergence of the traditional BP (which is due to low values of gradients). In contrast, the integration with GA complements the drawback of convergence to local minima as GA, possesses the capability of efficient global search. So by this integration of these algorithms, we propose our new hybrid algorithm for training ANNs. We compare both the algorithms for the application of medical diagnosis. Results display that the proposed hybrid training algorithm, significantly outperforms the traditional training algorithm, by enhancing the accuracies of the ANNs with an increase of 20% in the average testing accuracy and 0.7% increase in the best testing accuracy.