行为免疫系统理论及其研究:新视野下的再考察

行为免疫系统理论认为, 为了应对流行病威胁, 人类进化出了一套特殊的行为反应倾向, 包括对疾病线索的警觉、厌恶, 对健康配偶的偏好, 对外群体的排斥, 对集体主义的推崇等。已有研究主要从宏观(群体)和微观(个体)两个水平开展, 虽然有大量研究结果支持该理论, 但也存在不少不一致甚至相反的发现。研究方法、理论基础和现代适用性等方面的不足可能是出现这种情况的原因。未来研究需克服上述问题, 并进一步拓展该理论的研究内容, 检验理论和相关研究的边界, 探讨行为免疫背后的生理机制等。     

Between Open-minded Critical Thinking and Closed-minded Allegiance: Educational Tensions in Societies Involved in Intractable Conflict

In modern democratic societies, one of the main roles of education is to promote critical thinking and open-mindedness. However, this objective can sometimes be seen as clashing in many societies with another one of education's primary objectives—to inculcate in the younger generation a set of common values and beliefs that reinforces collective identity. The current article examines the tension between these two educational goals—fostering open-mindedness and critical-thinking skills, on the one hand, and promoting closed-minded national identity, on the other hand, in the context of intractable conflicts. In particular, we offer an analysis of the unique challenges of nurturing open-minded and critical thinking among students in the presence of processes and content common in education systems of conflict-ridden societies. These challenges are discussed in relation to the role that open-mindedness and critical thinking could play in promoting peace-building processes were they not hindered by nationalistic educational approaches designed to ensure unwavering support of the conflict.     

A study of the compliance level of connected vehicle warning information in a fog warning system based on a driving simulation

The accidents in the freeway are frequent and more serious on foggy days. Connected vehicle (CV) technology as a new technology can inform drivers of fog conditions in advance so the drivers can adjust their driving behaviors through Human Machine Interface (HMI) and Dynamic Message Sign (DMS) in the fog warning system. The level of driver's compliance with a fog warning system is the key to assessing the effectiveness of the fog warning system. To evaluate the compliance level of fog warning system, the study established a CV system testing platform based on driving simulator, and analyzed the changes in driving behavior and influencing factors for drivers under three different visibility conditions (No fog, light fog and heavy fog) based on the platform, finally, comprehensive evaluation of the optimal fog warning system under different foggy conditions based on the compliance level were made. Research indicators are divided into three aspects: 1) the response degree, including the mean speed, the minimum speed in the fog zone, the difference of the speeds when entering and leaving the fog zone, the proportion of speed following, and the speed reduction proportion prompt; 2) the response start time, which is the time to start slowing down to the speed limit; and 3) the response time duration, namely, the time in which the speed limit is followed. The results show that the response degree of the driver to the fog warning system is high, the fog warning system can effectively reduce the driving speed of the drivers and improve the speed following proportion of the drivers, the deceleration ratio of the driver at each warning point is relatively high, and the influencing factors of various indicators are complex but are mostly related to visibility and technical level. Comprehensive evaluation results show that the warning mode of the combined HMI and DMS has the highest level of compliance under light fog conditions, and when fog concentration increases, compliance level of fog warning system with HMI only is higher than others. The study establishes a reference platform for CV system and provides methods and index system for the compliance level of CV research.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

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.     

Le développement d’un outil de mesure des comportements inadaptés au travail auprès de directions d’établissements scolaires du Québec

This study focuses on developing a measuring tool for unsuitable behaviour in the workplace. The tool has been developed from school principals. To do so, exploratory factor analyses have been conducted. Data have been collected from 232 senior staff members from school facilities across Quebec. Participants were invited to answer an online questionnaire. The results obtained help to classify inappropriate work behaviour into one factor. In addition to its theoretical value, this research brings several significant contributions to practice.     

反馈相关负波：一种抑郁症的生物标记物

反馈相关负波(feedback-related negativity, FRN)是一种反映个体奖赏敏感性的脑电成分; 而抑郁症患者的特征之一是奖赏敏感性的减弱。大量横向研究发现了重度抑郁症患者(major depressive disorder, MDD)及有患抑郁症风险群体的FRN异常; 另外, 纵向研究发现FRN的异常能够预测青春期抑郁发作及抑郁症状发展, 压力、睡眠等因素在其中起调节作用。这提示FRN可能在对抑郁症的诊断及分型、抑郁高危群体的筛查及干预等方面具有潜在价值。目前关于FRN的心理功能依然存在一些争议, 在未来的研究中应该进一步明确FRN的功能和测量方法, 并探明抑郁症的异质性和共病对FRN的作用, 也应关注老年人群体中FRN与抑郁症的关系。     

个体近似数量系统与其数学能力之间的关系：发展研究的证据

近似数量系统在个体数学能力的发展中起着重要的作用, 二者之间的关系受到年龄因素的影响。主要表现为, 随着年龄的增长, 相关程度逐渐减弱, 二者之间关系的作用机制可能由基数知识中介转变为多种中介变量的共同作用。未来可采用更严格的实验设计和多种研究方法考察各年龄段儿童近似数量系统与不同数学能力之间关系的发展趋势、因果方向、关键转折点和潜在机制, 以更好地理解近似数量系统在个体数学能力发展中所起的作用。     

父母婚姻冲突对儿童发展的影响及其机制

婚姻关系是家庭关系中最核心的关系, 家庭中父母婚姻冲突影响着儿童发展。众多研究发现, 父母婚姻冲突对儿童认知发展和情绪社会性发展均有重要影响, 而且父母婚姻冲突影响儿童发展存在复杂的作用机制(如中介机制和调节机制)。父母婚姻冲突对儿童发展的影响主要通过儿童认知-情绪过程和家庭过程来实现, 且这一影响会因多种儿童因素和环境因素的不同而不同。未来研究有必要同时考察父母婚姻冲突对儿童认知发展和情绪社会性发展的影响, 并进一步探究和整合多种作用机制, 为干预方案的制定提供有力支持。     

三数值加工系统假说：数值加工机制新探

数值加工的机制是数量认知领域的核心科学问题之一。三数值加工系统假说从新的角度阐释了各种数值加工机制的关系, 它认为认知系统通过三种不同的机制来快速分析非符号刺激的数值：感数(subitizing)机制精确地分析1~4个刺激的数值; 数量(numerosity)机制分析密度适中的刺激点阵的数量, 加工误差正比于被分析的数量, 符合韦伯定律; 当刺激的密度超出一定范围时, 密度(density)机制通过分析刺激密度来推断刺激的数量关系, 加工误差正比于被加工数值的平方根。一系列研究证实, 这三种数值加工机制具有不同的行为规律和脑电特征。未来研究需要探讨数量机制是否分别与感数机制、密度机制存在平行激活, 从而阐明三种数值加工机制的作用关系。