Introduction

There are very significant conceptual links between theories of social macroevolution and theories of the World System development. It is shown that the growth of the World System complexity and integrity can be traced through a system of phase transitions of macroevolution. The first set of phase transition is connected with the agrarian, industrial, and information-scientific revolutions (that are interpreted as changes of “production principles”). The second set consists of phase transitions within one production principle. These phase transitions are analyzed on the basis of the World System urbanization dynamics, but they can be traced with respect to the other (cultural, economic, technological, demographic, political, etc.) dimensions of the World System development.     

Dysfunction of attentional networks for non-emotional processing in negative affect

It is unclear whether negative affect is associated with the impairment of attentional networks for non-emotional processing. Using the attention network test (ANT), which assesses the efficiency of alerting, orienting, and executive attention, we attempted to clarify which attentional networks were related to negative affect, i.e., anxiety, depression, and social anxiety. Forty-three participants completed the self-report questionnaires and the ANT. Negative affect was negatively correlated with orienting efficiency but not executive attention. Our results indicate that people with negative emotionality have an impaired orienting network for non-emotional stimuli. It is suggested that attentional bias for threatening stimuli in negative affect is due to the original impairment of the orienting network.     

局部表象产生中行为性别差异的神经基础

行为研究发现女性具有更好的局部表象产生能力, 但其神经基础尚不清楚。研究采用事件相关电位技术以及小世界网络分析方法, 探索局部表象产生中性别差异的大脑信息加工基础。结果显示在局部表象产生中, 女性行为反应较男性更快, 表象产生诱发的P300-650平均波幅更小, 且大脑功能网络的平均路径长度更短。深入分析发现, 反应时与脑网络的平均路径长度之间成正相关。该结果意味着低耗高效的大脑信息加工模式是女性具有更好的局部表象能力的神经基础。     

Relationships between non-pathological dream-enactment and mirror behaviors

Dream-enacting behaviors (DEBs) are behavioral expressions of forceful dream images often occurring during sleep-to-wakefulness transitions. We propose that DEBs reflect brain activity underlying social cognition, in particular, motor-affective resonance generated by the mirror neuron system. We developed a Mirror Behavior Questionnaire (MBQ) to assess some dimensions of mirror behaviors and investigated relationships between MBQ scores and DEBs in a large of university undergraduate cohort. MBQ scores were normally distributed and described by a four-factor structure (Empathy/Emotional Contagion, BehavioralImitation, Sleepiness/Anger Contagion, Motor Skill Imitation). DEB scores correlated positively with MBQ total and factor scores even with social desirability, somnambulism and somniloquy controlled. Emotion-specific DEB items correlated with corresponding emotion-specific MBQ items, especially crying and smiling. Results provide preliminary evidence for cross-state relationships between propensities for dream-enacting and mirror behaviors—especially behaviors involving motor-affective resonance—and our suggestion that motor-affective resonance mediates dream-enactment imagery during sleep and emotional empathy during waking.     

The Cascade Neural Network Model and a Speed-Accuracy Trade-Off of Arm Movement

We propose a hybrid neural network model of aimed arm movements that consists of a feedforward controller and a postural controller. The cascade neural network of Kawato, Maeda, Uno, and Suzuki (1990) was employed as a computational implementation of the feedforward controller. This network computes feedforward motor commands based on a minimum torque-change criterion. If the weighting parameter of the smoothness criterion is fixed and the number of relaxation iterations is rather small, the cascade model cannot calculate the exact torque, and the hand does not reach the desired target by using the feedforward control alone. Thus, one observes an error between the final position and the desired target location. By using a fixed weighting parameter value and a limited iteration number to simulate target-directed arm movements, we found that the cascade model generated a planning time–accuracy trade-off, and a quasi–power-law type of speed–accuracy trade-off. The model provides a candidate neural mechanism to explain the stochastic variability of the time course of the feedforward motor command. Our approach also accounts for several invariant features of multijoint arm trajectories, such as roughly straight hand paths and bell-shaped speed profiles.     

Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands (task-negative network, TNN) and compares effects of intelligence on neural effort in the TPN and the TNN. In a sample of 52 healthy subjects, functional magnetic resonance imaging was used to determine changes in neural effort associated with the processing of a working memory task. The task comprised three conditions of increasing difficulty: (a) maintenance, (b) manipulation, and (c) updating of a four-letter memory set. Neural effort was defined as signal increase in the TPN and signal decrease in the TNN, respectively. In both functional networks, TPN and TNN, neural effort increased with task difficulty. However, intelligence, as assessed with Raven's Matrices, was differentially associated with neural effort in the TPN and TNN. In the TPN, we observed a positive association, while we observed a negative association in the TNN. In terms of neural efficiency (i.e., task performance in relation to neural effort expended on task processing), more intelligent subjects (as compared to less intelligent subjects) displayed lower neural efficiency in the TPN, while they displayed higher neural efficiency in the TNN. The results illustrate the importance of differentiating between TPN and TNN when interpreting correlations between intelligence and fMRI measures of brain activation. Importantly, this implies the risk of misinterpreting whole brain correlations when ignoring the functional differences between TPN and TNN.     

默认网络的神经机制、功能假设及临床应用

认知神经科学的迅速发展使得研究者对大脑的功能有了深刻的认识。近些年来, 研究者对大脑在进行外在注意任务时产生的负激活有了更多的了解, 默认网络(Default Mode Network)的概念被提出, 并引起了很多神经科学家的关注, 关于默认网络的相关理论也已提出。当前综述对默认网络的研究历史, 概念理论, 个体和种系发展, 及其与疾病的关系以及临床应用等方面进行了概括总结。未来研究可从默认网络的个体发展, 种系差异, 与其他大脑网络(如注意网络, 视觉网络, 执行控制网络)的关系以及与具体疾病的关系等角度入手进行研究, 共同来探讨默认网络的神经机制。     

儿童欺负/受欺负与同伴网络的关系：p*模型分析

从四所小学的10个班中挑选出高欺负-受欺负班和低欺负-受欺负班,使用p^*模型分析班内朋友关系的网络结构特点及其与欺负、受欺负的关系。结果发现：（1）低欺负-受欺负班级中的同伴关系网络倾向于形成开放性朋友关系结构,如边、2-星、3-星、4-星结构;高欺负-受欺负班级中的同伴关系网络倾向于形成闭合性朋友关系结构,如三角形、孤立者结构;（2）高、低欺负-受欺负班级中,欺负者均处于同伴关系网络的边缘化位置,且所处的关系结构位置与自身欺负水平的关联不显著;（3）受欺负者所处的关系结构位置与自身受欺负水平的关联也不显著,但是在低欺负-受欺负班中,受欺负者倾向处于同伴关系网络的边缘化位置,而高欺负-受欺负班中,部分受欺负者处于边缘化位置,部分受欺负者处于闭合性关系结构的核心位置。     

基于GRM模型的BP神经网络参数估计

目前参数估计多采用统计方法,存在耗时长、要求被试样本容量大和项目数多等缺点。本文将BP神经网络和降维法相结合,对GRM的项目参数和考生能力参数进行估计。蒙特卡洛模拟结果显示：（1）不管是人多题少还是题多人少,该网络设计下的参数估计精度都较高;（2）可以应用到多个不同等级评分的参数估计中,甚至是超过15个等级的项目参数,估计精度也较高,这是其他参数估计方法所不可比拟的;（3）运行的时长和统计估计方法相比大大缩减。