Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

Since the human brain functional mechanism has been enabled for investigation by the functional Magnetic Resonance Imaging (fMRI) technology, simultaneous modeling of both the spatial and temporal patterns of brain functional networks from 4D fMRI data has been a fundamental but still challenging research topic for neuroimaging and medical image analysis fields. Currently, general linear model (GLM), independent component analysis (ICA), sparse dictionary learning, and recently deep learning models, are major methods for fMRI data analysis in either spatial or temporal domains, but there are few joint spatial-temporal methods proposed, as far as we know. As a result, the 4D nature of fMRI data has not been effectively investigated due to this methodological gap. The recent success of deep learning applications for functional brain decoding and encoding greatly inspired us in this work to propose a novel framework called spatio-temporal convolutional neural network (ST-CNN) to extract both spatial and temporal characteristics from targeted networks jointly and automatically identify of functional networks. The identification of Default Mode Network (DMN) from fMRI data was used for evaluation of the proposed framework. Results show that only training the framework on one fMRI dataset is sufficiently generalizable to identify the DMN from different datasets of different cognitive tasks and resting state. Further investigation of the results shows that the joint-learning scheme can capture the intrinsic relationship between the spatial and temporal characteristics of DMN and thus it ensures the accurate identification of DMN from independent datasets. The ST-CNN model brings new tools and insights for fMRI analysis in cognitive and clinical neuroscience studies.

Here we characterize establishment of joint attention in hearing parent-deaf child dyads and hearing parent-hearing child dyads. Deaf children were candidates for cochlear implantation who had not yet been implanted and who had no exposure to formal manual communication (e.g., American Sign Language). Because many parents whose deaf children go through early cochlear implant surgery do not themselves know a visual language, these dyads do not share a formal communication system based in a common sensory modality prior to the child's implantation. Joint attention episodes were identified during free play between hearing parents and their hearing children (N = 4) and hearing parents and their deaf children (N = 4). Attentional episode types included successful parent-initiated joint attention, unsuccessful parent-initiated joint attention, passive attention, successful child-initiated joint attention, and unsuccessful child-initiated joint attention. Group differences emerged in both successful and unsuccessful parent-initiated attempts at joint attention, parent passive attention, and successful child-initiated attempts at joint attention based on proportion of time spent in each. These findings highlight joint attention as an indicator of early communicative efficacy in parent-child interaction for different child populations. We discuss the active role parents and children play in communication, regardless of their hearing status.

Being able to learn word meanings across multiple scenes consisting of multiple words and referents (i.e., cross-situationally) is thought to be important for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training, after several occurrences of each word-object pair. Diverse models are able to match the final level of performance of the standard paradigm, while the rich history and context of the learning trajectories remain obscured. This study examines accuracy and uncertainty over time in a version of the cross-situational learning task in which words are tested throughout training, as well as in a final test. With similar levels of performance to the standard task, we examine how well the online response trajectories match recent hypothesis- and association-based computational models of word learning.eing able to learn word meanings across multiple scenes consisting of multiple words and referents (i.e., cross-situationally) is thought to be important for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult to uncover the learning mechanics in part because the standard experimental paradigm, which presents a few words and objects on each of a series of training trials, measures learning only at the end of training, after several occurrences of each word-object pair. Diverse models are able to match the final level of performance of the standard paradigm, while the rich history and context of the learning trajectories remain obscured. This study examines accuracy and uncertainty over time in a version of the cross-situational learning task in which words are tested throughout training, as well as in a final test. With similar levels of performance to the standard task, we examine how well the online response trajectories match recent hypothesis- and association-based computational models of word learning.B.