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.     

Motion tracking of parents’ infant‐ versus adult‐directed actions reveals general and action‐specific modulations

Parents tend to modulate their movements when demonstrating actions to their infants. Thus far, these modulations have primarily been quantified by human raters and for entire interactions, thereby possibly overlooking the intricacy of such demonstrations. Using optical motion tracking, the precise modulations of parents’ infant‐directed actions were quantified and compared to adult‐directed actions and between action types. Parents demonstrated four novel objects to their 14‐month‐old infants and adult confederates. Each object required a specific action to produce a unique effect (e.g. rattling). Parents were asked to demonstrate an object at least once before passing it to their demonstration partner, and they were subsequently free to exchange the object as often as desired. Infants’ success at producing the objects’ action‐effects was coded during the demonstration session and their memory of the action‐effects was tested after a several‐minute delay. Indicating general modulations across actions, parents repeated demonstrations more often, performed the actions in closer proximity and demonstrated action‐effects for longer when interacting with their infant compared to the adults. Meanwhile, modulations of movement size and velocity were specific to certain action‐effect pairs. Furthermore, a ‘just right’ modulation of proximity was detected, since infants’ learning, memory, and parents’ prior evaluations of their infants’ motor abilities, were related to demonstrations that were performed neither too far from nor too close to the infants. Together, these findings indicate that infant‐directed action modulations are not solely overall exaggerations but are dependent upon the characteristics of the to‐be learned actions, their effects, and the infant learners.     

Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters

Human adults are adept at mitigating the influence of sensory uncertainty on task performance by integrating sensory cues with learned prior information, in a Bayes‐optimal fashion. Previous research has shown that young children and infants are sensitive to environmental regularities, and that the ability to learn and use such regularities is involved in the development of several cognitive abilities. However, it has also been reported that children younger than 8 do not combine simultaneously available sensory cues in a Bayes‐optimal fashion. Thus, it remains unclear whether, and by what age, children can combine sensory cues with learned regularities in an adult manner. Here, we examine the performance of 6‐ to 7‐year‐old children when tasked with localizing a ‘hidden’ target by combining uncertain sensory information with prior information learned over repeated exposure to the task. We demonstrate that 6‐ to 7‐year‐olds learn task‐relevant statistics at a rate on par with adults, and like adults, are capable of integrating learned regularities with sensory information in a statistically efficient manner. We also show that variables such as task complexity can influence young children's behavior to a greater extent than that of adults, leading their behavior to look sub‐optimal. Our findings have important implications for how we should interpret failures in young children's ability to carry out sophisticated computations. These ‘failures’ need not be attributed to deficits in the fundamental computational capacity available to children early in development, but rather to ancillary immaturities in general cognitive abilities that mask the operation of these computations in specific situations.     

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.     

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。