In light of the dramatic growth of Chinese learners worldwide and a need for cross-linguistic research on Chinese literacy development, this study drew upon theories of visual complexity effect (Su & Samuels, 2010) and dual-coding processing (Sadoski & Paivio, 2013) and investigated a) the effects of character properties (i.e., visual complexity and radical presence) on character acquisition and b) the relationship between individual learner differences in radical awareness and character acquisition. Participants included adolescent English-speaking beginning learners of Chinese in the (...) U.S. Following Kuo et al. (2014), a novel character acquisition task was used to investigate the process of acquiring the meaning of new characters. Results showed that a) characters with radicals and with less visual complexity were easier to acquire than characters without radicals and with greater visual complexity; and b) individual differences in radical awareness were associated with the acquisition of all types of characters, but the association was more pronounced with the acquisition of characters with radicals. Theoretical and practical implications of the findings were discussed. (shrink)

Autism spectrum disorder is a group of complex neurodevelopment disorders characterized by altered brain connectivity. However, the majority of neuroimaging studies for ASD focus on the static pattern of brain function and largely neglect brain activity dynamics, which might provide deeper insight into the underlying mechanism of brain functions for ASD. Therefore, we proposed a framework with Hidden Markov Model analysis for resting-state functional MRI from a large multicenter dataset of 507 male subjects. Specifically, the 507 subjects included 209 subjects (...) with ASD and 298 well-matched health controls across 14 sites from the Autism Brain Imaging Data Exchange. Based on the HMM, we can identify the recurring brain function networks over time across ASD and healthy controls. Then we assessed the dynamical configuration of the whole-brain networks and further analyzed the community structure of transitions across the brain states. Based on the 19 HMM states, we found that the global temporal statistics of the specific HMM states were significantly altered in ASD compared to HCs. These specific HMM states were characterized by the activation pattern of default mode network, sensory processing networks [including visual network, auditory network, and sensory and motor network ]. Meanwhile, we also find that the specific modules of transitions between states were closely related to ASD. Our findings indicate the temporal reconfiguration of the brain network in ASD and provide novel insights into the dynamics of the whole-brain networks for ASD. (shrink)