Effects of the function-specific instruction approach to neurofeedback training on frontal midline theta waves and golf putting performance

A recent meta-analysis has shown inconclusive results on the effectiveness of traditional electroencephalography (EEG) neurofeedback training (NFT) protocols in changing EEG activity and improving sports performance. To enhance the effectiveness of EEG NFT protocols, we explored a new approach to EEG NFT, namely the function-specific instruction (FSI) approach. The basic tenet underpinning effective verbal instruction is to induce mental states as the verbal instructions consider the meaning of the brainwave function in the target region and the EEG power magnitude. This study aimed to test whether a single session of FSI is efficacious in improving frontal midline theta (FMT) activity and putting performance. Method: Thirty-six skilled golfers with a handicap of 14.05 ± 9.43 were recruited. A consecutive sampling method was used to form three groups: an FSI group (n = 12), a traditional instruction (TI) group (n = 12), and a sham control (SC) group (n = 12). In the pre- and post-tests, each participant performed 40 putts from a distance of 3 m, and the number of holed putts was recorded. The participants were asked to perform 50 trials in a single session of NFT. Putting performance improved significantly from before to after NFT in the FSI group. Moreover, the FSI group demonstrated a significant decrease in FMT power, whereas the SC group demonstrated a significant increase in FMT power from before to after NFT. These findings suggest that the FSI approach is more effective in enhancing sustained attention and putting performance in skilled golfers than TI.     

认知控制的层级性：来自任务切换的脑电证据*

认知控制的主要研究范式之一是任务切换。以往研究发现切换代价受到认知控制层级性的调节, 但鲜有研究探索这一调节过程的动态神经机制。本研究通过嵌套的线索-任务切换范式考察不同层级任务切换代价的差异及其神经机制。在实验中, 要求被试完成高低两种层级任务, 低层级任务要求被试判断数字大小(或奇偶); 高层级任务则须先加工数字的某一语义特征(如当前数字是否是偶数), 然后进行大小判断。行为结果表明, 高层级任务切换代价显著大于低层级任务切换代价。线索锁时的脑电结果表明, 层级效应最早出现于P2成分, 切换效应(切换与重复之差)在CNV成分上受到任务层级的调控, 反映了在任务目标重构阶段给予高层级任务更多的选择性注意以及更高的主动性控制。目标锁时的脑电结果表明, 在N2及慢波(SP)成分上, 高层级任务切换与重复的波幅差异相比低层级任务显著更大, 反映了在抑制旧任务集与重构新反应集的过程中增强的反应性控制。这些结果为任务设置重构论和认知控制的层级性提供了新的证据。     

基于作答时间数据的改变点分析在检测加速作答中的探索——已知和未知项目参数

相对于传统的离散作答数据, 作答时间作为连续数据, 可以提供更多信息。改变点分析(change point analysis)技术在心理和教育领域是一个比较新的技术。本文一方面对改变点分析在心理测量领域的应用进行了一个综合的总结和分析; 另一方面, 将基于作答数据的两种改变点分析统计量推广到作答时间数据, 将改变点分析技术应用到测验异常作答模式：加速作答speededness的检测上。采用两种检验方法：似然比检验和Wald检验, 分别在已知和未知项目参数的条件下, 实现异常作答模式的检测。结果表明, 所采用的方法对于加速作答行为的检测具有很高的检验力, 同时能够很好的控制I类错误率。实证数据分析进一步表明本文中所使用的方法具有应用价值。     

An online hazard perception training course reduces heavy braking,speeding, and over-revving rates during everyday driving

In essence, driver training involves learning the skills required to drive safely and avoid dangerous events. However, in traditional on-road driver instruction, drivers virtually never accrue experience of the most significant types of events that they are learning to avoid: crashes. One means of providing this experience safely is to present novice drivers with video clips of real crashes, as part of structured learning exercises. A six-week automated online hazard perception training course for drivers, incorporating evidence-based training methods and over a hundred crash clips, was previously found to improve novice drivers’ hazard perception skill, which is known to be an important attribute for avoiding crashes. However, since hazard perception was measured using computer-based methods, the possibility remained that the training effect might not transfer to actual driving. We report a randomized control trial in which novice drivers were recruited to assess everyday driving behaviour objectively, using g-force triggered dashcams and GPS trackers installed in their vehicles. On-road data were collected for a one-month baseline period, and for a further two months after half of the sample completed the hazard perception training course. Drivers who completed the course significantly reduced their rate of heavy-braking events, their speeding behaviour, and their rate of over-revving events. These findings support the proposal that a relatively inexpensive and highly scalable hazard perception training intervention can improve on-road driving behaviour, with the clear potential to impact real-world driver safety.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

Towards autonomous artificial agents with an active self: Modeling sense of control in situated action

In this paper we present a computational modeling account of an active self in artificial agents. In particular we focus on how an agent can be equipped with a sense of control and how it arises in autonomous situated action and, in turn, influences action control. We argue that this requires laying out an embodied cognitive model that combines bottom-up processes (sensorimotor learning and fine-grained adaptation of control) with top-down processes (cognitive processes for strategy selection and decision-making). We present such a conceptual computational architecture based on principles of predictive processing and free energy minimization. Using this general model, we describe how a sense of control can form across the levels of a control hierarchy and how this can support action control in an unpredictable environment. We present an implementation of this model as well as first evaluations in a simulated task scenario, in which an autonomous agent has to cope with un-/predictable situations and experiences corresponding sense of control. We explore different model parameter settings that lead to different ways of combining low-level and high-level action control. The results show the importance of appropriately weighting information in situations where the need for low/high-level action control varies and they demonstrate how the sense of control can facilitate this.     

Driving around bends with or without shoulders: The influence of bend direction

Paved shoulders have long been used to create “forgiving” roads where drivers can maintain control of their vehicles even when as they drift out of the lane. While the safety benefits of shoulders have been well documented, their effects on driver behavior around curves have scarcely been examined. The purpose of this paper is to fill this gap by assessing whether the addition of shoulders affects driver behavior differently as a function of bend direction. Driver behavior in a driving simulator was analyzed on left and right curves of two-lane rural roads in the presence and absence of 0.75-m and 1.25-m shoulders. The results demonstrated significant changes in drivers’ lateral control when shoulders were provided. In the absence of oncoming traffic, the shoulders caused participants to deviate more toward the inner lane edge at curve entry, at the apex and at the innermost position on right bends but not left ones. In the presence of oncoming traffic, this also occurred at the apex and the innermost position, leading participants to spend more time off the lane on right curves. Participants did not slow down in either traffic condition to compensate for steering farther inside, thereby increasing the risk of lane departure on right curves equipped with shoulders. These findings highlight the direction-specific influence of shoulders on a driver’s steering control when driving around bends. They provide arguments supporting the idea that drivers view paved shoulders as a new field of safe travel on right curves. Recommendations are made to encourage drivers to keep their vehicle within the lane on right bends and to prevent potential interference with cyclists when a shoulder is present.     

视觉观点采择需要抑制控制的参与：一项负启动研究

从抑制控制模型出发,采用负启动范式考察视觉观点采择的认知机制。实验1采用自身体转换任务(N=35) 、实验2采用圆点数量判断任务(N=33)分别考察成人在完成二级和一级视觉观点采择任务时是否需要抑制控制的参与。结果发现,成人在完成两类视觉观点采择任务时均出现了负启动效应,说明当他人观点和自我观点不一致时,个体需要抑制自我的观点才能成功采择他人的观点。     

负性情绪在冲突适应中的作用机制：分离与整合视角

负性情绪如何影响冲突适应一直以来受到领域内研究者们的广泛关注。根据负性情绪产生的源头(冲突外部操纵vs.冲突内部固有), 对于该问题的论述可以分别从认知与情绪的分离与整合视角展开。分离视角下, 操纵于冲突之外的负性情绪(外部负性情绪)独立于冲突加工过程, 通过情绪加工系统或个体自身的动机/唤醒水平影响冲突适应。近些年来的研究发现冲突加工自动产生负性情绪(固有负性情绪), 寓示着认知冲突与负性情绪间的内在关联, 固有负性情绪因此可被视作诱发冲突适应的另一个有效来源。整合视角下, 操纵于冲突内部的(固有)负性情绪高度整合于冲突加工过程, 其功能与冲突信息类似, 通过内在地促进目标导向行为直接诱发冲突适应。对这一主题的论述加深了我们对负性情绪如何作用于冲突适应过程的理解, 也为探索认知与情绪系统的整合过程及作用机制提供了一个全新的视角, 在此基础上, 我们也提出一些未来可行的研究方向。