囊虫病诊断技术的发展与临床应用

囊虫病是一种由猪囊尾蚴寄生于人体组织内主要引起脑、眼等重要器官或组织损害的常见寄生虫病。目前对其诊断的实验技术研究已发展到高层水平,但被临床应用的并不多。本文从多个方面较全面地分析和回答了出现此现象的原因,并提出了拟解决临床问题的见解与探索思路。     

基于视觉传播理论探索视觉素养与读片技能的整合

视觉素养是"读图"时代人们的基本文化素养,鉴于医学影像学的特点和要求,影像诊断学专业学生必须拥有良好的视觉素养。本文从视觉素养概念、视觉基本理论以及视觉素养培养的策略等方面探索了视觉素养与读片技能的整合。作者建议在影像诊断学中,应讲授有关视觉素养的基本知识。     

影像诊断中的系统辩证思路

本文根据影像诊断逻辑学原理,探讨影像诊断思路。影像诊断思路可分为两个层面的思维和四个相应阶段。两个层面的思维是对象思维和元思维。四个阶段是影像观察理解、异常影像解析、疾病假说构建、影像诊断验证。这一过程我们称为影像诊断的二层四阶段系统辩证思路。     

工作倦怠评价标准的初步探讨

本研究运用工作倦怠问卷、一般健康问卷和自尊量表对四个样本的834名被试进行调查,初步制订了工作倦怠的评价标准。结果发现：（1）可以以耗竭分数〉25、人格解体分数〉11、成就感降低分数〉16作为工作倦怠的临界值;（2）工作倦怠从程度上可以分为零倦怠、轻度倦怠、中度倦怠和高度倦怠四种水平;（3）工作倦怠的评价标准具有较好的区分效度。     

Crowdsourcing and personality measurement equivalence: A warning about countries whose primary language is not English

In the search to find cheaper, faster approaches for data collection, crowdsourcing methods (i.e., online labor portals that allow independent workers to complete surveys for compensation) have risen in popularity as a tool for personality researchers, despite a lack of evidence regarding the equivalence of crowdsourcing with traditional data collection methods. The purpose of this study was to evaluate crowdsourcing as a data collection tool by examining the measurement equivalence of crowdsourced data (i.e., from Amazon.com’s MTurk) with more traditional samples (i.e., an undergraduate sample and a sample of organizational employees). Our results (using a popular measure of Big Five personality) provided evidence of measurement equivalence across all three samples, with one important exception: crowdsourced data (from MTurk) only exhibited measurement invariance with traditional data collection methods when responses were restricted to participants from native-English speaking countries. Although MTurk appears to be an easy, cost-effective data collection tool, our results suggest that MTurk data are similar to traditionally-collected data only when the MTurk sample is restricted to IP addresses from English-speaking countries.     

Car following with an inertia-oriented driving technique: A driving simulator experiment

BackgroundFor many decades, car-following (CF) and congestion models have assumed a basic invariance: drivers’ default driving strategy is to keep the safety distance. The present study questions that Driving to keep Distance (DD) is a traffic invariance and, therefore, that the difference between the time required to accelerate versus decelerate must necessarily determine the observed patterns of traffic oscillations. Previous studies have shown that drivers can adopt alternative CF strategies like Driving to keep Inertia (DI) by following basic instructions. The present work aims to test the effectiveness of a DI course that integrates 4 tutorials and 4 practice sessions in a standard PC computer designed to learn more adaptive driving behaviors in dense traffic. Methods. Sixty-eight drivers were invited to follow a leading car that varied its speed on a driving simulator, then they took a DI course on a PC computer, and finally they followed a fluctuating leader again on the driving simulator. The study adopted a pretest-intervention-posttest design with a control group. The experimental group took the full DI course (tutorials and then simulator practice). The control group had access to the DI simulator but not to the tutorials. Results. All participating drivers adopted DD as the default CF mode on the pre-test, yielding very similar results. But after taking the full DI course, the experimental group showed significantly less accelerations, decelerations, and speed variability than the control group, and required greater CF distance, that was dynamically adjusted, spending less fuel in the post-test. A group of 8 virtual cars adopting DD required less space on the road to follow the drivers that took the DI course.     

心理信息学:网络信息时代下的心理学新发展

在见证了社会计算与计算社会科学的勃兴之后,心理学和信息科学在网络信息时代相遇,催生出心理信息学这门新兴交叉学科。该学科旨在利用计算机与信息科学技术,通过开展网络调查和实验、开发和利用移动设备、建立数据库和分类体系、使用开源软件和数据挖掘技术等,获取、整理和分析心理学研究资料。其对大规模复杂数据的掌控能力,使心理学家可以从新的高度和视野,针对现实和虚拟网络中的个体与群体心理现象展开研究,重新检视已有理论假设并探索新发现。但是,心理信息学在展现巨大潜力的同时,也在技术和研究设计、伦理和研究规范、研究重心与人才培养等诸多方面面临挑战。     

Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data

Cocaine is a type of drug that functions to increase the availability of the neurotransmitter dopamine in the brain. However, cocaine dependence or abuse is highly related to an increased risk of psychiatric disorders and deficits in cognitive performance, attention, and decision-making abilities. Given the chronic and persistent features of drug addiction, the progression of abstaining from cocaine often evolves across several states, such as addiction to, moderate dependence on, and swearing off cocaine. Hidden Markov models (HMMs) are well suited to the characterization of longitudinal data in terms of a set of unobservable states, and have increasingly been used to uncover the dynamic heterogeneity in progressive diseases or activities. However, the existence of outliers or influential points may misidentify the hidden states and distort the associated inference. In this study, we develop a Bayesian local influence procedure for HMMs with latent variables in the presence of missing data. The proposed model enables us to investigate the dynamic heterogeneity of multivariate longitudinal data, reveal how the interrelationships among latent variables change from one state to another, and simultaneously conduct statistical diagnosis for the given data, model assumptions, and prior inputs. We apply the proposed procedure to analyze a dataset collected by the UCLA center for advancing longitudinal drug abuse research. Several outliers or influential points that seriously influence estimation results are identified and removed. The proposed procedure also discovers the effects of treatment and individuals’ psychological problems on cocaine use behavior and delineates their dynamic changes across the cocaine-addiction states.     

使用Bootstrap方法计算认知诊断评估中的信度

测验信度是衡量测验质量的一个重要指标,认知诊断评估中同样需要重视信度问题。现有认知诊断中计算信度的方法均有一个前提假设:被试在前后两次测验的后验概率分布和边际概率完全相同。该假设过强,未考虑两次测验间存在的随机误差。基于Bootstrap抽样,提出了两类属性信度和模式信度的指标,分别是积差相关法和修正的一致性法。通过模拟研究比较了新方法和现有方法在不同属性个数、属性间相关性和题目数量下的表现,并基于英语能力认证考试ECPE和分数减法的实证数据验证了新方法的可行性。最后,对信度估计的影响因素进行了讨论。