A bio-inspired model of behavior considering decision-making and planning,spatial attention and basic motor commands processes

Cognitive architectures (CA) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent’s motor behavior.     

The impact of auditory continual feedback on take-overs in Level 3 automated vehicles

ObjectiveTo implement auditory continual feedback into the interface design of a Level 3 automated vehicle and to test whether gaze behavior and reaction times of drivers improved in take-over situations.BackgroundWhen required to assume manual control in take-over situations, drivers of Level 3 automated vehicles are less likely than conventional drivers to spot potential hazards, and their reaction time is longer. Therefore, it is crucial that the interface of Level 3 automated vehicles will be designed to improve drivers’ performance in take-over situations.MethodIn two experiments, participants drove a simulated route in a Level 3 automated vehicle for 35 min with one imminent take-over event. Participants’ gaze behavior and performance in an imminent take-over event were monitored under one of three auditory interface designs: (1) Continual feedback. A system that provides verbal driving-related feedback; (2) Persistent feedback. A system that provides verbal driving-related feedback and a persistent beep; and (3) Chatter feedback. A system that provides verbal non-driving-related feedback. Also, there was a control group without feedback.ResultsUnder all three auditory feedback designs, the number of drivers' on-road glances increased compared to no feedback, but none of the designs shortened reaction time to the imminent event.ConclusionIncreasing the number of on-road glances during automated driving does not necessarily improve drivers’ attention to the road and their reaction times during take-overs.ApplicationPossible implications for the effectiveness of auditory continual feedback should be considered when designing interfaces for Level 3 automated vehicles.     

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。     

中西方“诚信”文化的差异及其现代整合

中国传统伦理文化中的诚信是建立在封建社会自给自足的小农经济基础之上 ,属于一种由人格信任、亲缘 (熟人 )信任所构成的道德范畴 ,强调的是伦理性 ;而西方法律文化中的诚信是建立在市场经济的契约关系之上 ,它强调的是规范和监督 ,体现了平等、自由与正当权利的法治精神。当代中国社会相对传统社会而言在社会结构、伦理关系、价值观念等方面都发生了重大变化 ,市场经济发展中的诚信危机现象 ,既有传统伦理道德文化的失灵问题 ,又有法律诚信的缺失问题。这就要求我们在党的十六届三中全会精神的指导下 ,实现传统伦理诚信文化的现代转型。     

比较改革前后的中国医疗卫生体制与伦理的优先

中国的医疗卫生问题近几年来一直是一个重大的社会热点问题。过去的市场化的医疗卫生有许多缺陷,特别是有重大的伦理缺陷。通过比较分析中国改革前后的医疗卫生特点,提出了今后中国医疗卫生体制改革以及伦理优先的思想轮廓。     

Identity,ideology, and personality: Examining moderators of affective polarization in New Zealand

Affective polarization (i.e., differences in evaluations of the in-party and out-party) has important implications for political outcomes including civic engagement and partisan-motivated attitudes and behaviours. In this study, we add to the burgeoning literature on affective polarization by examining moderators of the effect of in-party support on out-party opposition across the three main political parties in New Zealand. Using a large national probability sample of adults (N = 19,078), we assessed the effects of social demographics, Big-Five personality traits, liberal-conservative ideology, political identity centrality, and weekly news exposure on the relationship between in-party and out-party support. Results show political identity centrality is the strongest and most consistent moderator. The effects of some other moderators (e.g., personality, news exposure) are party-specific.     

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.