COMT基因对注意控制神经基础的调控效应：影像遗传学研究的元分析

为探索注意控制能力个体差异的遗传来源, 当前研究主要关注儿茶酚胺氧位甲基转移酶(catechol-O-Methyltransferase, COMT)基因对参与注意控制加工的前额叶脑区的调控作用。为进一步回答COMT基因是否也对全脑范围的注意脑区具有调控作用, 本文对17篇遗传影像学研究进行元分析。结果发现, COMT基因Val/Val (vv)基因型的被试在注意控制任务下, 不仅前额叶脑区的激活水平高于Met/Met(mm)基因型的被试, 在前扣带回和后扣带回等前额叶之外的脑区激活水平也高于mm基因型的被试, 而且在这些脑区的效应值(vv>mm)都较大(Cohen’s d > 0.8)。由此, 元分析结果表明：COMT基因不仅调控前额叶脑区, 而且对形成注意控制网络的多个脑区都有调控效应。此结果提示注意控制能力的个体差异可能部分的来自于COMT基因对注意控制网络的调控作用。     

人格行为遗传学研究的两类取向

随着行为主义衰落和生物学技术发展,长期遭受“冷遇”的人格行为遗传学研究近些年成为颇受关注的领域,并由数量遗传学取向发展到分子遗传学取向.数量遗传学取向主张运用双生子研究、收养研究等设计来估计群体中遗传因素对人格表现型方差的贡献率,在人格特质、人格障碍、态度与偏好等人格方面做了大量研究.分子遗传学取向主张在DNA水平上用基因测定方法研究特定基因对人格表现型的影响效应,着重从多巴胺、5-羟色胺和去甲肾上腺素三种神经递质路径考察了基因与人格的关系.未来研究要强调在数量遗传设计中加入对特定基因型的直接测量,注重多学科和多范式的有效整合,扩大对健康人群积极人格品质的研究,加强基因对人格的深层作用机制研究和分子医学层面的人格干预研究.     

Predatory aggression in lines of wild mice selected for interfemale aggression

Mice descending from lines previously selected for high and low levels of interfemale aggression and from a nonselected control line were exposed to live crickets on two consecutive test days. Latencies both to attack and to eat the cricket were recorded. No line or sex differences in attacking were found, although latency to attack decreased over test days. Control line mice were more likely to engage in the eating of the cricket than either the high or low selected lines, suggesting that the selection procedure may have dissociated attacking and eating components of predatory behavior.     

A data–information–knowledge cycle for modeling driving behavior

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors.