Children can optimally integrate multisensory information after a short action‐like mini game training

Combining information across different sensory modalities is of critical importance for the animal's survival and a core feature of human's everyday life. In adulthood, sensory information is often integrated in a statistically optimal fashion, so that the combined estimates of two or more senses are more reliable than the best single one. Several studies have shown that young children use one sense to calibrate the others, which results in unisensory dominance and undermines their optimal multisensory integration abilities. In this study we trained children aged 4–5 years with action‐like mini games, to determine whether it could improve their multisensory as well as their visuo‐spatial skills. Multisensory integration abilities were assessed using a visuo‐haptic size discrimination task, while visuo‐spatial attention skills were investigated using a multiple object tracking task (MOT). We found that 2‐weeks training were sufficient to observe both optimal multisensory integration and visuo‐spatial enhancements selectively in the group trained with action‐like mini games. This plastic change persisted up to 3 months, as assessed in a follow‐up. Our novel findings reveal that abilities that are commonly known to emerge in late childhood can be promoted in younger children through action‐like mini games and have long‐lasting effects. Our data have clinical implications, in that they suggest that specific trainings could potentially help children with multisensory integration deficits.     

The ontogeny of cumulative culture: Individual toddlers vary in faithful imitation and goal emulation

The success of human culture depends on early emerging mechanisms of social learning, which include the ability to acquire opaque cultural knowledge through faithful imitation, as well as the ability to advance culture through flexible discovery of new means to goal attainment. This study explores whether this mixture of faithful imitation and goal emulation is based in part on individual differences which emerge early in ontogeny. Experimental measurements and parental reports were collected for a group of 2‐year‐old children (N = 48, age = 23–32 months) on their imitative behavior as well as other aspects of cognitive and social development. Results revealed individual differences in children's imitative behavior across trials and tasks which were best characterized by a model that included two behavioral routines; one corresponding to faithful imitation, and one to goal emulation. Moreover, individual differences in faithful imitation and goal emulation were correlated with individual differences in theory of mind, prosocial behavior, and temperament. These findings were discussed in terms of their implications for understanding the mechanisms of social learning, ontogeny of cumulative culture, and the benefit of analyzing individual differences for developmental experiments.     

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.     

认知负荷取向下基于记忆-反应冲突的欺骗检测

欺骗检测一直是心理学的重要研究问题。基于欺骗理论的认知视角, 研究者提出欺骗检测的认知负荷取向。采用隐瞒信息测试这一测谎范式, 通过操纵认知负荷影响个体在虚假反应时的记忆-反应冲突解决过程, 考察增加认知负荷对欺骗检测的影响, 以期更好地揭示欺骗检测的认知机制。在此基础上, 以普通人群和犯罪嫌疑人为被试探查基于记忆-反应冲突的欺骗检测的行为和生理指标, 并根据获得的行为和生理指标, 采用机器学习方法进行建模, 预测个体的欺骗行为。研究结果将服务于司法、安防和人际交往等领域的欺骗检测。     

反馈相关负波与成瘾

反馈相关负波(feedback-related negativity, FRN)是反馈加工诱发的脑电成分, 体现了个体对奖赏的敏感性。成瘾分为物质成瘾和行为成瘾, 两类个体的反馈加工都呈现病理性模式。相对于普通反馈物(如金钱), 物质成瘾个体在加工成瘾物质时诱发的FRN波幅显著增大; 在与非成瘾被试的对照研究中, 物质成瘾者加工金钱反馈时的FRN也表现出与成瘾相关的异常模式; 在行为成瘾个体中亦观察到与物质成瘾个体类似的FRN失调现象。但以往多数研究中对成瘾类型的区分还不够详细, 未来研究应进一步考虑成瘾不同亚型的特点; 且成瘾个体往往伴随其他精神障碍(如抑郁、焦虑), 将来的研究要区分共病因素的影响, 揭示成瘾独特的奖赏加工机制问题。