文化差异视角下个体合作行为的产生、影响机制建构及认知神经基础

合作行为是指个体或群体之间为了实现共同的目标和利益而进行的协同行为或意向。本文基于行为-认知-大脑的三重映射关系,对合作行为的文化差异性进行了深入阐释,并在此基础上构建了文化影响合作行为的社会认知中介模型;未来研究可从实证视角对合作行为文化差异的社会认知内容、认知神经机制等方面进行验证、挖掘和改善。     

超扫描视角下的社会互动脑机制

超扫描技术可同时记录多名被试在同一认知活动中的脑活动,并通过分析脑间活动同步及其与行为指标间的关系描述社会互动的群体脑机制。本文总结了近十多年超扫描研究在合作与竞争、动作和行为同步、人际交流等领域的成果,在已有研究基础上指出脑间活动同步可刻画社会互动中感觉运动、思维决策以及信息传递等三大层面上的互动情况,可能成为社会互动活动的神经标记,并阐述了超扫描技术研究的局限性及其研究展望与应用潜能。     

自豪感的神经基础：比较的视角

自豪感是对自身成就进行评估时产生的积极情绪体验。神经基础研究表明, 心理理论、自我参照、情绪、奖赏和记忆等相关脑区的协同作用构成了自豪感的神经基础, 而神经和生理的比较研究则揭示了自豪感和其他基本情绪以及道德情绪等在神经基础上的异同。以上结果为理解自豪感的复杂神经机制提供了依据。未来研究应对不同种类自豪感以及自豪感与认知过程相互作用的神经机制进行深入探讨。     

催产素对学习和记忆的调节效应：来自动物与人类的研究证据

作为一种神经肽, 催产素对于个体社会认知和情绪加工有着非常重要的调节作用, 其中就包括对在人们工作和生活中扮演着关键角色的学习和记忆活动的影响。采用不同模态技术的动物和人类研究一致表明了外源性催产素对于学习和记忆具有重要的调节作用, 这一作用可能是外源性催产素通过与多巴胺奖赏通路、边缘系统等学习和记忆关键脑网络中广泛分布的催产素受体相结合, 进而调整其功能状态实现的; 但与此同时, 催产素对学习和记忆的促进或抑制作用会因实验范式、刺激材料、给药时间、位置和剂量等因素的不同而存在差异。未来需要协同动物和人类研究的各自优势, 采用规范化的实验任务设置和给药程序克服当前该领域的研究局限, 并积极发掘催产素在干预相关精神疾病患者学习和记忆加工缺陷中的应用潜力。     

听而不“闻”?人声失认症的神经机制

人声身份识别对于社交交流的许多方面都至关重要, 大多数个体都能根据声音识别其声源者, 然而人声失认症患者似乎已经丧失了这种能力。人声失认症是指人声身份加工的不同阶段出现障碍, 症状主要包括获得性人声失认症, 发展性人声失认症及其亚型。获得性人声失认症患者受损脑区主要包括颞叶, 赫氏脑回和颞极, 发展性人声失认症主要与右后侧颞上沟的非典型性反应和颞叶与杏仁核间的功能联结障碍有关。以后的研究可以重点关注人声失认症的筛选方法, 界定范围和文化差异等方面。     

融合视角下创造力的认知神经机制

创造力的认知神经机制是近年来心理学研究领域的前沿和热点问题。通过融合创造力整体宏观视角和创造性产生过程的微观视角,对创造力的认知神经机制进行了综述。宏观视角下,创造力主要涉及α波和大脑前额叶、内外侧颞叶以及外侧顶叶; 微观视角下,在创造力产生过程中主要涉及α波序列位置效应以及默认网络和执行控制网络的功能耦合。未来研究方向应该结合多模态脑成像数据库,利用机器算法来探究创造力的本质; 关注青少年群体创造力的纵向发展趋势; 结合分子遗传学研究,探究与创造力有关的基因问题。     

Developmental changes in neural letter‐selectivity: A 1‐year follow‐up of beginning readers

The developmental course of neural tuning to visual letter strings is unclear. Here we tested 39 children longitudinally, at the beginning of grade 1 (6.45 ± 0.33 years old) and 1 year after, with fast periodic visual stimulation in electroencephalography to assess the evolution of selective neural responses to letter strings and their relationship with emerging reading abilities. At both grades, frequency‐tagged letter strings were discriminated from pseudofont strings (i.e. letter‐selectivity) over the left occipito‐temporal cortex, with effects observed at the individual level in 62% of children. However, visual words were not discriminated from pseudowords (lexical access) at either grade. Following 1 year of schooling, letter‐selective responses showed a specific increase in amplitude, a more complex pattern of harmonics, and were located more anteriorly over the left occipito‐temporal cortex. Remarkably, at both grades, neural responses were highly significant at the individual level and correlated with individual reading scores. The amplitude increase in letter‐selective responses between grades was not found for discrimination responses of familiar keyboard symbols from pseudosymbols, and was not related to a general increase in visual stimulation responses. These findings demonstrate a rapid onset of left hemispheric letter selectivity, with 1 year of reading instruction resulting in increased emerging reading abilities and a clear quantitative and qualitative evolution within left hemispheric neural circuits for reading.     

Using spotted hyena optimizer for training feedforward neural networks

Spotted hyena optimizer (SHO) is a novel metaheuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for training feedforward neural network (FNN), which is regarded as a challenging task since it is easy to fall into local optima. Our objective is to apply metaheuristic optimization algorithm to tackle this problem better than the mathematical and deterministic methods. In order to confirm that using SHO to train FNN is more effective, five classification datasets and three function-approximations are applied to benchmark the performance of the proposed method. The experimental results show that the proposed SHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.     

Earthquake disaster avoidance learning system using deep learning

The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 s on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96% in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.     

Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice.