客体相似性对不同认知方式个体视觉工作记忆表征的影响

采用整体检测的变化察觉范式,探讨客体的相似性对客体—空间型认知方式个体视觉工作记忆表征的影响。行为结果显示,在不相似客体的视觉工作记忆表征任务中,客体型者在长延时条件的成绩优于空间型者,而在相似客体的视觉工作记忆表征任务中,两者无差异;ERP结果显示,在不相似客体视觉工作记忆表征任务中,与客体型者相比,空间型者在大脑皮层诱发出更大的负SP。以上结果表明,客体型者比空间型者对不相似客体视觉工作记忆加工具有优势,其潜在机制可能在于,客体型者对不相似客体视觉工作记忆表征时的神经活动效能更高。     

不公平感及相关决策的认知神经机制

公平是人类社会生活的基本规范之一,不公平感及其相关决策则是研究者们关注的重要课题。长期以来,该领域的研究一般采用最后通牒博弈或其变式展开。大量脑成像研究探查了关于不公平感及相关决策的认知神经机制,尤其集中探讨了最后通牒博弈的回应者对不公平提议进行反应的脑区及其对应功能。经常得到关注的脑区包括了前脑岛、前扣带皮层、背外侧前额叶、内侧前额叶、杏仁核和颞顶交界等。对特殊人群的不公平感及相关决策进行研究可以帮助检验或澄清上述重要脑区及脑网络在不公平感及相关决策中扮演的角色,同时也阐释特殊人群的社会认知功能的特点。近年来,相当数量的研究关注了不同情境因素(包括分配方案相关因素和社会情境相关因素)调制不公平感及相关决策的过程,并讨论其背后的认知神经机制。未来的研究更应利用多模态数据分析方法,同时结合基因和激素层面的研究,以期深入对不公平感及相关决策的心理和生理机制的理解。     

书写对阅读的影响——来自传统书写与电脑打字的证据

书写和阅读是在文字的基础上发展起来的, 二者存在共享的神经网络。书写能影响阅读, 阅读不仅依靠视觉加工, 还依赖书写运动表征。不同书写方式对阅读的影响不同, 传统书写对阅读有促进作用, 电脑打字对阅读产生了一定的消极影响。对汉字来说, 传统书写能加强汉字的正字法表征, 促进汉字阅读; 电脑打字对汉字阅读的影响尚无一致结论。未来研究可以考察传统书写与电脑打字神经机制的差异, 以及电脑打字对汉字认知加工的影响及神经机制。     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research.     

探究事件相关脑电/脑磁信号中的神经表征模式：基于分类解码和表征相似性分析的方法

探究不同心智活动下的神经表征差异, 是认知神经科学关注的核心问题之一。早期的脑电/脑磁分析方法主要关注组平均后的神经响应水平, 这要求在关注的时间进程上, 各个被试在相同刺激条件下事件相关电位/事件相关磁场的振幅大小和方向、以及地形图分布和极性均要有较高的一致性。近些年来, 研究者们将功能性磁共振成像研究中常用到的两种技术——机器学习中的分类算法(即基于分类的解码)和表征相似性分析——引入到了脑电/脑磁数据分析中。这两种新技术可以克服传统脑电/脑磁数据基于具体电压/磁感应强度波形平均分析的缺点, 具有在个体水平上探究神经表征编码的特点, 为人们探究大脑在不同时间进程上如何对特定的神经表征信息进行动态编码提供了新的思路。两种技术基于不同的方法学原理来抽提个体间一致的脑认知加工机制, 还为脑电/脑磁研究开展跨时域、跨任务、跨模态、跨群体比较不同认知过程中的表征差异提供了更多新颖的途径。我们首先通过与传统的脑电/脑磁分析方法进行比较, 系统性介绍了基于分类的解码和表征相似性分析的原理和操作流程, 之后对两种方法的应用场景进行了梳理, 并在最后对未来可供研究的方向提出了我们的见解。     

南北朝禅学

南北朝时代,禅学是佛教的重要修持方式,尤其是在北朝形成了相对稳定的禅师传承关系和禅僧群体,在南朝则是沿着长江两岸和浙东出现多处禅修基地,颇具时代特色。本文拟就南北朝禅学及其特点作一简要的论述。     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

Brain tumor detection using fusion of hand crafted and deep learning features

The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.     

时距知觉的动物研究范式及相关神经机制

在探索时距知觉的脑机制的过程中, 相对于人类被试相关研究, 动物研究可以提供较多的药理学、分子生物学、单个神经元电生理学以及光遗传等方面的研究证据。目前较为常用的时距知觉动物研究范式包括时间二分法、峰值间隔法以及低比率差别强化法等。根据不同的研究需求, 动物研究的范式常会进行调整。对时距知觉的动物研究的探讨将基于两方面展开：(1) 常用的时距知觉动物研究范式的介绍及比较; (2) 基于动物研究范式的时距知觉神经机制研究进展, 旨在为深入探索时间知觉的心理学相关研究提供参考。