道德决策的机制及干预研究:自我相关性与风险水平的作用

现有的道德决策脑机制研究更多地关注了认知和情绪的交互作用,较少关注个体对"自我相关性"和"风险水平"信息的整合加工,更是忽视了帮助情境和伤害情境下道德决策的心理与脑机制的异同。因此,有必要通过行为、事件相关电位(ERP)和多模态脑功能成像(f MRI)等多种方法来系统考察帮助和伤害情境下自我相关性和风险水平交互影响道德决策的认知和神经机制,并进一步探讨共情训练对道德决策的干预作用。旨在揭示道德决策的时间进程和神经基础;探寻自我相关性与风险水平对道德决策的影响,及其在不同情境和共情水平下可能存在的变异;共情训练能否有效提升个体的道德决策水平。     

情绪加工的性别差异及神经机制

已有研究表明, 情绪加工存在显著性别差异。这主要表现为女性人群具有情绪识别优势, 更好的情绪记忆能力与更强的负性情绪易感性。此外, 情绪加工的性别差异也表现为情绪调节过程的不同：相比男性, 女性更善于抑制情绪行为, 却较难通过认知策略调节负性情绪。情绪加工的性别差异有重要生理基础, 与情绪脑结构, 荷尔蒙水平的男女差异有关。同时, 情绪加工的性别差异同女性更高的情绪障碍易感性有着密切联系：不善于调节负性情绪, 及更强的消极情绪易感性可能是女性更易患情绪障碍的重要原因。从情绪加工角度探讨情绪障碍的性别差异及其原因, 对于情绪障碍的预防和治疗具有重要意义。     

发展性计算障碍的最新研究进展

发展性计算障碍是一种影响算术技能获得的特定的学习障碍。截至目前,有关发展性计算障碍的认知与神经机制的理论尚存分歧,有关诊断与鉴别标准也未统一。近年来,对于发展性计算障碍的理论假设有从一般认知因素取向到数学特定因素取向发展的趋势。而随着脑成像技术的不断发展,对于发展性计算障碍神经机制的研究也从针对单个脑区的特异性功能发展到从功能连接网络角度进行研究。并且研究者们开始尝试开发基于数学认知基本理论的干预方法,并采用了利用生物技术手段的新方法。基因-脑-行为的整合研究将有助于全面揭示发展性计算障碍的发生机制,而建立在系统理论基础上经过科学评估的干预手段将可能有效促进障碍者的计算能力。     

Motivational Effects of Outcome Feedback on Motor Performance

This study examined the effects of success and failure feedback on subsequent motor performance. Based upon the general motivation (or level-of-aspiration) hypothesis, initial success should lead to better subsequent performance than does initial failure, while the reverse prediction was derived from the cognitive dissonance theory. To test these rival hypotheses, two experiments were conducted on undergraduate male students (n =120) performing a motor maze task. Initial failure improved subjects’ subsequent performance, thus supporting the dissonance theory. However, this effect was observed only under low-ego-involving conditions, thereby suggesting that the effects of dissonance and ego involvement are interdependent. The findings were discussed in terms of motivational and informational/attributional effects of outcome feedback on motor performance.     

Genetic counseling for a family with two distinct anomalies: A case report of a neural tube defect and 5p- syndrome in a fetus

A couple presented for genetic counseling because of an elevated maternal serum alpha fetoprotein. Ultrasound examination revealed the presence of a neural tube defect. The couple declined an amniocentesis, but chose serial ultrasound evaluations instead. Ultrasounds eventually identified microcephaly, but the couple continued to decline amniocentesis. After the child's birth, the diagnosis of 5p- syndrome was made. The couple's decision not to have an amniocentesis allowed the family their right to autonomy; however, prenatal chromosome analysis would have provided this couple with a great deal more prognostic information. We discuss the conflict between a counselor's duty to respect a client's freedom vs. duty to care for a client's welfare. We address issues of nondirective counseling and the need for more studies looking at the decision-making process in prenatal diagnosis.     

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。     

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.     

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.     

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。