Automatic detection of information changes in the visual environment is crucial for individual survival. Researchers use the oddball paradigm to study the brain’s response to frequently presented stimuli and occasionally presented stimuli. The component that can be observed in the difference wave is called visual mismatch negativity, which is obtained by subtracting event-related potentials evoked by the deviant from ERPs evoked by the standard. There are three hypotheses to explain the vMMN. The sensory fatigue hypothesis considers that weakened neural activity (...) caused by repetition results in decreased ERPs of the standard. The memory trace hypothesis proposes that vMMN results from increased responses to the deviant. The predictive coding hypothesis attributes the difference to enhanced responses for deviants and suppression for standards. However, when distinguishing between these effects, previous researchers did not consider the effect of low-level features on the vMMN. In this experiment, we used face sequences composed of different emotions and presented an oddball sequence, a reverse oddball sequence, and an equiprobable sequence to participants. The deviant of the oddball sequence was subtracted from the standard of the oddball sequence, the reverse oddball sequence, and the same type of stimulus of the equiprobable sequence to get oddball-vMMN, reverse oddball-vMMN, and equiprobable-vMMN, respectively. The results showed no significant difference between vMMN2 and vMMN3 in 100–350 ms following stimulus onset, while the vMMN effect was significant, indicating that the probability of the standard did not affect vMMN, which supported the memory trace hypothesis. Additionally, the fearful-related vMMN were more negative than the neutral-related vMMN within the range of 100–150 ms, suggesting a negative bias. We analyzed the source location of different vMMNs. There was no significant difference in brain regions between different vMMNs. Time-frequency analysis showed that the deviant had stronger theta-band oscillatory than the standard. However, there was no difference between vMORs2 and vMORs3, indicating that vMORs reflect an enhanced response to the deviant in terms of neural oscillation, supporting the memory trace hypothesis. (shrink)

本书主要介绍了先秦、汉代、宋代、近代四个时期传统义利思想的历史发展及中国传统义利思想对日本义利观的影响。.

本书从思维、逻辑、方法的视角,将思维科学与逻辑科学、现代逻辑与辩证逻辑的知识统一在一个有机体系中,探讨了辩证逻辑的规律与创造思维的方法。.

本书内容包括:传统逻辑在知识创新中的作用、数理逻辑在知识创新中的作用、哲学逻辑在知识创新中的作用等。.

吴光辉,湖北省武汉市人,现为厦门大学外文学院讲师,日本语教育研究中心副主任。著有《传统与超越》。.

本书属于世纪之交的中西文学理论的比较研究。时间跨度是从20世纪80年代与90年代的中西文论概观的反思,到21世纪第一个十年中西文论景观的展望。.

本书从商品拜物教这一特殊理论视角对现代性的成因、流变、类型及其在当代中国的表现形式做了独特而深入的阐述和分析.

本书主要内容包括:初恋风云——数学教育主心骨的的百年寻觅之旅、践约时代——“现代数学教育方式”渐行渐的近、热题冷谈——数学教育焦点话题的哲学断想.

本书建构了一个新的艺术阐释体系——踪迹学,并试图对艺术和当代艺术作品作完全另类的解释,对中国传统艺术与当代艺术的合理对接提出了不乏新意的解说。.

本书内容包括:逻辑经验世界排除意义世界、意义世界的不可排除性、反对意义世界的根源、意义世界与在之真理、艺术显现真理等。.

Ben shu gong fen san bu: di yi bu hui gui zhen wo, di er bu hui gui tian di, di san bu hui gui tai ji. Nei rong bao kuo: lun sheng ming; sheng ming yu shi ge; sheng ming yu yin yue; xian dai ren sheng ming de yan bian; wo shi shui, tian di yu wo deng.

1. Xin yi shu chuang zuo lun, Xin yi shu lun ji -- 2. Mian xiang sheng huo, Lun yi shu de ji qiao -- 3. Yi yi dang shi -- 4. Xi wen le jian -- 5. Ge er bu ge -- 6. Lun Feng jie -- 7. Kai xin yue shi -- 8. Bu dao ding dian, Shen mei de min gan -- 9. Zai zai tan suo -- 10. Liao ran yu xin -- 11. Shen mei tan (...) -- 12. Si zeng xiang shi, Yi shen er ren -- 13. Shen mei xin tai -- 14. Hui jian zi ji -- 15. Diao su diao su -- 16. "Fu huo" de fu huo -- 17. Dong fang ji bai -- 18. Shen yu wu you -- 19. Tu na ying hua -- 20. Qu yu wu xie -- 21. Shu xin, ti ci xuan -- 22. Duan jian can pian. (shrink)

本书结合对马克思、恩格斯哲学原著的研究探讨,对大学生们在学习中提出的若干问题进行了阐释。.

Short-term traffic flow prediction is an effective means for intelligent transportation system to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short-term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, (...) the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder and radial basis function neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments. (shrink)