Driving mental workload and performance of ageing drivers

The World Health Organization stated that the global ageing population is increasing rapidly as well as the case of road accidents involving ageing drivers. This study presents the driving mental workload and performance model of ageing drivers in the context of real-time road driving. Twenty paid participants (ten males) with a mean age of 57.8 years old (SD = 2.7) and mean driving experience of 29.6 years (SD = 8.5) took part in driving experiments with three complexity levels of situation: simple situation (SS), moderately complex situation (MCS), and very complex situation (VCS). The subjective ratings using NASA Task Load Index (NASA-TLX), physiological measure using electroencephalogram, number of traffic violations (NTVs), speed variability, and reaction time of peripheral detection task were measured. The driving experiments reveal the following: (1) The subjective workload ratings on mean physical demand score were the highest. (2) The electroencephalogram results show that situation complexity had significant effects on theta relative power and alpha relative power of two channel locations (3) The highest mean NTVs was in VCS. (4) The mean speed variability in the MCS was significantly lower than that of in the SS and VCS. (5) The maximum reaction time was recorded in VCS while the minimum reaction time was recorded in the MCS. The overall driving performance score regression models were developed based on the strong correlation and linear relationship between mental workload and driving performance elements. The models may benefit as a reference for designers, manufacturers, developers, and policymakers in designing better driving environment for ageing drivers by integrating safety and transportation, thus, optimizing and sustaining the driving performance of ageing drivers.     

“Other” behavior and the DRO: The roles of extinction and reinforcement

Jessel et al. (2015) provided some evidence to suggest that “other” behavior is strengthened in the differential reinforcement of other behavior (DRO). The present study is a systematic replication of the Jessel et al. procedures. The effects of DRO and extinction on target responding, target-other responding (a response with an established history of reinforcement), and nontarget-other responding emitted by children with intellectual and developmental disabilities and children with no known diagnoses were compared. Other behavior increased in at least one DRO condition for each participant, suggesting that other behavior increases when using DRO, at least initially. Under extinction, target responding and target-other responding decreased to low rates for three of the five participants; however, rates of nontarget-other responding were elevated compared to the DRO condition. These results suggest that increased rates of target-other responding and nontarget-other responding during the DRO condition may be a result of extinction-induced variability.     

Modelling inter-individual differences in latent within-person variation: The confirmatory factor level variability model

Psychological theories often produce hypotheses that pertain to individual differences in within-person variability. To empirically test the predictions entailed by such hypotheses with longitudinal data, researchers often use multilevel approaches that allow them to model between-person differences in the mean level of a certain variable and the residual within-person variance. Currently, these approaches can be applied only when the data stem from a single variable. However, it is common practice in psychology to assess not just a single measure but rather several measures of a construct. In this paper we describe a model in which we combine the single-indicator model with confirmatory factor analysis. The new model allows individual differences in latent mean-level factors and latent within-person variability factors to be estimated. Furthermore, we show how the model's parameters can be estimated with a maximum likelihood estimator, and we illustrate the approach using an example that involves intensive longitudinal data.     

The Role of Social Network Structure in the Emergence of Linguistic Structure

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro-societies that varied in their network structure. We contrasted three types of social networks: fully connected, small-world, and scale-free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small-world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift).     

Tracking Fluctuations in Psychological States Using Social Media Language: A Case Study of Weekly Emotion

Personality psychologists are increasingly documenting dynamic, within-person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality-relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text-based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human-annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self-reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model-predicted emotion and self-reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology     

心率变异性与人格的神经质之间关系研究

本研究探讨心率变异性（HRV）特征在高水平应激条件下对神经质水平的区分力。研究选取200名被试以大五人格量表测量情绪稳定性,实施了应激诱发实验,利用光体积扫描传感器采集被试者HRV指标;利用LASSO回归分析筛选参数构建对神经质的预测模型。结果显示：模型的预测数据与神经质得分高低显著正相关;不同阶段内预测数据与神经质得分显著正相关;两两比较中实验条件阶段与其他阶段预测值差异显著。研究表明,在高应激条件下,HRV是人格神经质较好的预测指标。     

儿童图形识别取样的时空策略

本研究探讨儿童图形识别取样的时间和空间策略。被试为24名六年级学生和24名三年级学生,刺激材料为20个不规则无意义几何图形。结果发现:六年级学生采取取样空间扩展为主,取样时间调整为辅的策略,三年级学生采用增加取样时间为主,扩展取样空间为辅的取样策略。任务难度加大时,被试采用恒定取样点时间和增加取样点数目的时-空策略。在空间取样策略发展上,学生年龄和任务难度的交互影响显著。     

不同心理刺激诱发的交感和副交感神经活动的比较

让24名女大学生被试完成划消测验、镜画测验、心算和准备演讲四种应激任务,目的在于探讨在不同心理刺激下交感和副交感神经系统的活动是否存在差异。记录HRV（Heart Rate Variability,心率变异性）的频域成分TP（Total Power,总功率）、HF（High Frequency,高频）、LF（Low Frequency,低频）、LF/HF（低频高频比）、平均R-R间期和血压等指标在基线和不同应激任务期间的变化。通过单因素重复测量方差分析和多重比较发现,准备演讲期间的TP、LF值显著地高于基线和其它三项任务, HF的值显著地高于划消测验和心算;镜画测验、划消测验和心算期间的LF值与基线比较显著下降,TP、LF和HF三项指标在这三项任务之间无显著差异;不同任务期间在LF/HF上无显著主效应。平均R-R间期基线水平与所有任务间的比较及各任务之间有显著差异。收缩压基线水平与划消、镜画任务间的比较有显著差异。结果表明,准备演讲期间的交感神经和副交感神经张力显著地大于其它任务,不同任务表现出的不同的自主神经张力变化说明交感神经与副交感神经活动不是一方活动增强、另一方活动减弱的单纯的活动方式,这一结果为Berntson提出的自主神经活动可能存在九种活动搭配模式的理论提供了实验证据     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。     

大学生核心自我评价对生活满意度的影响：水平和稳定性的作用

核心自我评价水平与生活满意度的关系一直受到研究者的关注,但目前仍没有实证研究探讨核心自我评价稳定性与生活满意度的关系。本研究以193名大学生为被试,采用纵向研究设计于三个时间段分别测量被试的核心自我评价水平、核心自我评价稳定性以及生活满意度,以探讨三者之间的关系。结果发现,核心自我评价水平与稳定性的相关关系不显著;核心自我评价水平能够显著预测生活满意度,但稳定性对生活满意度的预测作用不显著;调节效应检验结果表明,核心自我评价稳定性在核心自我评价水平与生活满意度之间存在调节作用。