The question of what determines brain laterality for auditory cognitive processing is unresolved. Here, we demonstrate a swap of hemisphere dominance from right to left during semantic interpretation of Chinese lexical tones in native speakers using simultaneously recorded mismatch negativity response and behavioral reaction time during dichotic listening judgment. The mismatch negativity, which is a brain wave response and indexes auditory processing at an early stage, indicated right hemisphere dominance. In contrast, the behavioral reaction time, which reflects auditory processing at (...) a later stage, indicated a right ear listening advantage, or left hemisphere dominance. The observed swap of hemisphere dominance would not occur when the lexical tone was substituted with a meaningless pure tone. This swap reveals dependence of hemisphere labor division initially on acoustic and then on functional cues of auditory inputs in the processing from sound to meaning. (shrink)

Neuroimaging technologies have improved our understanding of deception and also exhibit their potential in revealing the origins of its neural mechanism. In this study, a quantitative power analysis method that uses the Welch power spectrum estimation of functional near-infrared spectroscopy signals was proposed to examine the brain activation difference between the spontaneous deceptive behavior and controlled behavior. The power value produced by the model was applied to quantify the activity energy of brain regions, which can serve as a neuromarker for (...) deception detection. Interestingly, the power analysis results generated from the Welch spectrum estimation method demonstrated that the spontaneous deceptive behavior elicited significantly higher power than that from the controlled behavior in the prefrontal cortex. Meanwhile, the power findings also showed significant difference between the spontaneous deceptive behavior and controlled behavior, indicating that the reward system was only involved in the deception. The proposed power analysis method for processing fNIRS data provides us an additional insight to understand the cognitive mechanism of deception. (shrink)

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities (...) with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications. (shrink)