Subjects: Psychology >> Experimental Psychology submitted time 2019-01-14
Abstract： " It has been widely recognized that the social order of human societies is largely maintained by social norms. However, we still know little about the cognitive and emotional foundations which shape social norms, making it hard (if not impossible) to understand how social norms are generated and maintained. Prior neural studies, which mainly perform second-party punishment based on the ultimatum framework, rarely explore the relevant brain areas as well as the neural mechanisms of third-party punishment driven by social norms. In the current study, we try to provide evidences which support that two types of mechanisms (i.e., negative emotions and self-interest mechanisms) influence social norms compliance of third-parties with opposite directions. Meanwhile, right dorsolateral prefrontal area (DLPFC) plays a crucial role in this process. In this study, we used transcranial direct current stimulation (tDCS) to investigate whether effects of increased or decreased right DLPFC excitability influenced third-party punishment in a dictator game. Following an experimental design of “between-subject (tDCS treatments: anodal, cathodal, sham) × within-subject (cost of punishment treatments: without cost, with cost)”, ninety participants were first randomly assigned to receive either anodal, cathodal, or sham stimulation of 15 min, then they performed two dictator game tasks as third-parties. In Task Ⅰ (without cost) participants did not need to bear any costs for their punishments (none cost task), while in Task Ⅱ (with cost) they were required to pay for their punishment actions. The results are given as follows. We first performed repeated measured ANOVA and one-way ANOVA to examine the effect of tDCS stimulation treatments (anodal, cathodal and sham) on emotion response. We found significant main effects of tDCS on the emotion response. Meanwhile, post hoc analysis (SNK) showed that the anodal stimulation decreased the negative emotions while the cathodal stimulation enhanced the negative emotions. Second, results of repeated measured ANOVA and one-way ANOVA showed significant main effects of tDCS on punishments in the none cost Task Ⅰ, and post hoc analysis (SNK) showed that the cathodal stimulation significantly increased punishments while the results of anodal stimulation were insignificant. Third, We also conducted repeated measured ANOVA and one-way ANOVA to test whether the difference of punishments between two tasks differed in tDCS groups, and found main effects of tDCS were significant. Moreover, post hoc analysis (SNK) showed that the difference of punishments between two tasks was significantly higher in the cathodal stimulation than that in the sham stimulation, while the results of anodal stimulation were insignificant compared to that in the sham stimulation. The present study provides one of the first neural evidences for the role of right DLPFC in third-parties’ social norm compliance, and supports mechanism explanations of negative emotions and self-interest. The outcomes indicate that DLPFC, by affecting the processes of negative emotions and self-interest, is an important brain area of social norm compliance. When third-parties are confronted with violations of social norms, their brain first releases negative emotions, which drives third-parties to punish violators. Further, if third-parties need to pay for their compliance with social norms, their rational goals about self-interest will weaken negative emotional impulses, and finally make their compliance with social norms depends on the trade-offs between negative emotions and self-interest mechanisms. "
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem of complex feature extraction and low accuracy in human action recognition, this paper proposed a network structure combining batch normalization algorithm with GoogLeNet network model. Applying Batch Normalization idea in the field of image classification to action recognition field, it improved the algorithm by normalizing the network input training sample by mini-batch. For convolutional network, RGB image was the spatial input, and stacked optical flows was the temporal input. Then, it fused the spatio-temporal networks to get the final action recognition result. It trained and evaluated the architecture on the standard video actions benchmarks of UCF101 and HMDB51, which achieved the accuracy of 93.50% and 68.32%. The results show that the improved convolutional neural network has a significant improvement in improving the recognition rate and has obvious advantages in action recognition.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-17 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the issue of symmetric flips in the process of 3D shape registration, this paper developed an efficient shape registration algorithm based on intrinsic symmetric feature detection. Firstly, it constructed intrinsic symmetric point pairs of the model by heat kernel signature (HKS) and geometric constraints. Secondly, based on the spectral embedding space analysis, it extracted the intrinsic symmetric plane of the model and effectively identified the symmetrical properties of the model according to the model surface normal vector, getting intrinsic symmetry point pair. Therefore it presented the consistent spectral symmetry structure of the model. Finally, combining the coherent point drift method, it implemented the shape registration of non-rigid model based on spectral symmetry. The experimental results show that the matching method is efficient and robust to the non-rigid deformable shape matching. Moreover, the structural features in same category models also are effectively identified.
Subjects: Biology >> Neurobiology Subjects: Psychology >> Cognitive Psychology Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2018-03-15
Abstract： Hypnosis is an effective psychological technology in respiratory motion control. In this study, functional magnetic imaging was applied to an intra-subject (n＝13) design hypnosis experiment guided by hypnotists to analyze the respiratory motion control and neural activity in hypnosis. As a result, increased brain activities were observed in visual cortex, sensorimotor cortex, posterior cingulate cortex and middle temporal gyrus, and decreased in dorsolateral prefrontal cortex, cerebellum posterior lobe and supramarginal gyrus. Moreover, compared with normal state, enhanced correlation of brain activities (normal state, r＝0.64; hypnosis state, r＝0.80) was observed within large-scale resting-state networks. Increased connectivity between sensorimotor cortex and visual cortex in hypnosis was also observed, which implies their critical roles in neural mechanisms of hypnosis for respiration control and involvement of cognitive and perceptual processing therein. This study provides new insights for hypnosis study in psychology and cognitive neuroscience.
Peer Review Status:
Awaiting Review
Subjects: Engineering and technical science >> Biomedical Engineering submitted time 2018-03-14
Abstract： X-ray shading artifacts lead to CT number inaccuracy, image contrast loss and spatial non-uniformity, and therefore are considered as one of the fundamental limitations of CBCT. In order to solve this problem, a novel shading correction method was proposed. Specifically, we first use multi-threshold segmentation algorithm to segment the original CT image for constructing a template image where each structure is filled with the same CT number of a specific tissue type. Then, the L0 norm smoothing algorithm is used to smooth the CBCT image for constructing an image without texture. By subtracting the template from the image without texture, the residual images from various error sources are low-pass filtered to generate the estimated shading artifacts. Finally, the estimated shading artifacts are added back to the original image for shading correction. Compared with the CT image without correction, the proposed method reduces the overall CT number error from over 113 HU to be less than 13 HU and decreases the non-uniformity from over 9% to be less than 1%. The experimental results demonstrate that the proposed shading correction method using L0 norm smoothing and image segmentation can effectively correct the shading artifacts and its feasibility in clinical application is validated.
Peer Review Status:Awaiting Review
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