学生快速阅读过程的信息加工实质的研究

本研究主要探讨学生快速阅读过程的信息加工活动的实质。根据快速阅读理解测验成绩选出小学六年级优生、中等生、差生各 3 0名 ,共 90名 ,随机分成两个等组。首先对两个组的被试进行阅读预期测验 ,测定他们根据文章的部分信息预期文章基本内容的水平 ;第二步进行阅读找错测验 ,要求实验组快速阅读而对照组以平常速度阅读一篇有若干明显错字的文章 ,然后要求他们在作业卷上标出这些错字。研究结果表明 ,学生的快速阅读水平越高 ,他们对阅读材料的预期成绩也就越高 ;同时 ,被试在快速阅读的情况下发现文章中错字的数量要比正常阅读的情况下要少。据此初步认为 ,学生快速阅读文章的过程 ,是一种更多地利用概念推动 ,较少地需要材料信息的“预期—证实”的信息加工过程。     

正念训练对冲动性不同要素的影响：基于双加工理论

正念是指对当下身心体验不带有评判地觉察。基于双加工理论,以正念为核心的训练对个体冲动性不同要素均具有明显干预效果。一方面,有助于降低因情感系统引发的冲动驱力：通过调整个体的奖赏评估与预期,缓和诱发冲动的压力与负性情绪反应,减弱自动化反应的联结和强度;另一方面,提升个体由认知控制系统调节的冲动控制力：增强个体的认知控制和行为抑制能力。由于冲动性驱动因素和控制因素二者加工时序的先后差异和作用方向对抗性特点,正念训练促进两类要素的积极交互影响,缩小冲动性“驱动”与“控制”力量之间的不平衡,为个体更好地回应自身反应与外界刺激提供了有利缓冲,是降低个体冲动性的有效干预手段。     

Long-term benefits of prompts to use safety belts among drivers exiting senior communities

Senior drivers are vulnerable to automobile crashes and subsequent injury and death. Safety belts reduce health risks associated with auto crashes. Therefore, it is important to encourage senior drivers to wear safety belts while driving. Using a repeated baseline design (AAB), we previously reported that motivating signs boosted safety belt usage by drivers exiting senior communities from baseline (72% and 68% usage), to postinstallation of signs (94%), to 6 months follow-up (80%). The current study was a 4-year follow-up in which six senior communities, with seat belt signs, were compared to six matched control senior communities with no signs. Safety belt usage was stable, across 4 years, at approximately 80% for both male and female drivers and front seat passengers for the six communities with signs, and was approximately 55% for control sites. These finding suggest that the simple and low-cost intervention of erecting signs to prompt safety belt use has persistent benefits that affect driver and passenger behavior alike.     

THE FAILURE OF FEEDBACK ON ALCOHOL IMPAIRMENT TO REDUCE IMPAIRED DRIVING

We examined the effects of rules to govern drinking, individual feedback on blood alcohol concentration (BAC), and public posting of group data on impaired driving on the incidence of impaired driving. Level of impairment was determined from breath samples taken from tavern patrons. Following baseline, an intervention package consisting of (a) cards to guide patrons in pacing their drinking to stay under the legal limit, (b) individual feedback on BAC, and (c) posted group feedback on the percentage of patrons driving while impaired the preceding week was introduced in two taverns. Results indicated that the intervention package did not reduce the percentage of impaired drivers departing either tavern. The addition of a brief intensive police enforcement program directed at impaired driving produced a short-term reduction in impaired driving.     

Identifying and Remedying Failures of Selective Attention in Younger Drivers

ABSTRACT— Newly licensed drivers are disproportionally involved in fatal crashes, and there is evidence that failure to attend to potentially relevant information is a major contributor to this problem. Corroborating this, in controlled scenarios on a driving simulator, we have found that younger drivers attend to (i.e., fixate) target regions in the virtual world that contain information about potential risks much less frequently than do more experienced drivers. We have developed a PC-based training program that substantially improves younger drivers' attention to these regions in the driving simulator and have recently replicated these training results on the road in a real driving situation.     

Driving stimuli increases accessibility of aggression-related concepts in “angry” drivers

Stimuli present in aversive situations (even initially neutral stimuli) can become associated with aggressive feelings and thoughts become capable of acting as cues for aggressive thoughts. The present research examined whether driving stimuli can serve as triggers for aggression-related concepts for individuals predisposed to becoming angry while driving (i.e., high in self-reported trait driving anger). Using the General Aggression Model (Anderson & Bushman, 2002) as a guide, two studies demonstrated that participants high in trait driving anger responded more quickly to aggressive words when paired with driving than neutral stimuli. There were no differences in primes for nonaggressive words and nonwords. Study 2 also found that, for participants high in driving anger, increased accessibility of aggressive words following driving primes predicted self-reported anger in a provoking driving scenario.     

The influence of just-world beliefs on driving anger and aggressive driving intentions

Decades of research demonstrate that the extent to which one believes the world is just can have important interpersonal consequences. Unfortunately, most of the commonly studied consequences are negative in nature. Guided by previous research demonstrating the buffering effect of just‐world beliefs and anger, the present research explores how belief in a just world (BJW) may mitigate anger in the domain of driving anger and examines the limiting conditions of this effect. Study 1 demonstrated the expected negative relation between common measures of BJW and anger expression in a driving context. Study 2 found that the buffering effects of just‐world beliefs and driver aggression were greater when BJW was violated (vs. not). Study 3 replicated the effects on aggression and anger and established a mediational role of anger on the buffering effects of just‐world beliefs on thoughts and driver aggression. Aggr. Behav. 38:389‐402, 2012. © 2012 Wiley Periodicals, Inc.     

Kano model of autonomous driving user acceptance according to driver characteristics: A survey study

The development of advanced technology has revolutionized human life. In this regard, autonomous driving, a core technology currently being developed, is changing rapidly. In addition to improving technology, the acceptance of technology users must be secured. Most relevant studies conducted hitherto have involved evaluation using acceptance elements defined based on the technology acceptance model and the unified theory of acceptance and use of technology. In this study, 21 elements associated with the acceptance of autonomous driving are defined. The Kano model is used to classify the acceptance elements into five attributes and to propose guidelines for improving acceptance. Driver characteristics are classified based on four human factors, which are used to investigate differences in acceptance between groups. A Google survey and fieldwork were completed by 187 participants. Contrary to previous studies, no significant gender differences are observed in the current study. In terms of age, many obstacles are encountered in securing autonomous driving acceptance from the elderly driver group. Additionally, a more conservative tendency is indicated by people with more driving experience. The results of this study reveal important points for identifying elements that hinder future sustainability and commercialization of autonomous driving, thereby facilitating its further technological development.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

Comparing eye-tracking metrics of mental workload caused by NDRTs in semi-autonomous driving

The objective of this study was to verify the effectiveness of eye-tacking metrics in indicating driver’s mental workload in semi-autonomous driving when the driver is engaged in different non-driving related tasks (NDRTs). A driving simulator was developed for three scenarios (high-, medium-, and low-mental workload presented by SAE (Society of Automotive Engineers) Levels 0, 1, and 2) and three uni-modality secondary tasks. Thirty-six individuals participated in the driving simulation experiment. NASA-TLX (Task Load Index), secondary task performance, and eye-tracking metrics were used as indicators of mental workload. The subjective rating using the NASA-TLX showed a main effect of autonomous level on mental workload in both visual and auditory tasks. Correlation-matrix calculation and principal-component extraction indicated that pupil diameter change, number of saccades, saccade duration, fixation duration, and 3D gaze entropy were effective indicators of a driver’s mental workload in the visual and auditory multi-tasking situations of semi-autonomous driving. The accuracy of predicting the mental-workload level using the K-Nearest Neighbor (KNN) classifier was 88.9% with bootstrapped data. These results can be used to develop an adaptive multi-modal interface that issues efficient and safe takeover requests.