Participants' perceptions of smartphone travel surveys

Smartphone travel surveys are becoming of central importance in collecting detailed, accurate data of people’s travel activities. As with their conventional survey counterparts, the quality of data collected through these surveys is adversely affected by participants’ non-response and resulting biases. However, little is known about the factors affecting people’s perceptions and intentions to continue participating in such surveys. Although literature has investigated the associations between individuals’ socio-demographic attributes and their likelihood of survey participation, the impact of their subjective perceptions and attitudes on their survey participation intentions and behaviour is underexplored. Hence, through a model of participants’ perceptions of smartphone travel survey applications (survey apps), this study aims to reveal such impacts and how they affect the participants’ intentions to continue participating in these surveys. In this study, a survey is designed and used to collect data in a smartphone travel survey before the participants are asked about their personal perceptions and attitudes. Partial least squares path modelling (PLS-PM) is applied to analyse the data, as it allows simultaneous estimation of the relationships between multiple latent constructs as well as the indicators of each construct. The results showed a significant, positive impact of the perceived “ease of use” and “usefulness” of the survey app as the technological medium of data collection on the participants’ “satisfaction” and “intention” to continue participating in the corresponding survey. The study also found that participants’ perceived “risk” associated with privacy concerns did not have any significant impact on their intention to continue participating in the survey.     

Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks

Driving is a highly complex task that involves the execution of multiple cognitive tasks belonging to different levels of abstraction. Traffic emerges from the interaction of a big number of agents implementing those behaviours, but until recent years, modelling it by the interaction of these agents in the so called micro-simulators was a nearly impossible task as their number grows. However, with the growing computing power it is possible to model increasingly large quantities of individual vehicles according to their individual behaviours. These models are usually composed of two sub-models for two well-defined tasks: car-following and lane-change. In the case of lane-change the literature proposes many different models, but few of them use Computational Intelligence (CI) techniques, and much less use personalization for reaching individual granularity. This study explores one of the two aspects of the lane-change called lane-change acceptance, where the driver performs or not a lane-change given his intention and the vehicle environment. We demonstrate how the lane-change acceptance of a specific driver can be learned from his lane change intention and surrounding environment in an urban scenario using CI techniques such as feed-forward Artificial Neural Network (ANN). We work with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) architectures. How they perform one against the other and how the different topologies affect both to the generalization of the problem and the learning process are studied.     

The life tasks model: Enhancing psychological and spiritual growth in the aged

ABSTRACT

The life tasks model is an active, mutual, and potentially universal approach to the spiritual care of the aged. A life task is a responsibility, once undertaken, that lasts a lifetime. Three tasks are identified. Task 1 is the discovery of hidden learning. The process of discovery draws on implicit learning that comes into awareness. This leads to task 2, testing in which learning brought into awareness is tested by other life experiences. The final task is task 3, integration, in which a more aware and cohesive self is formed. Ideally, this leads to service or vocation. Progress on the life tasks is illustrated by responses in a qualitative study of older adults. Although this model can be applied to self-growth and ministry to all ages, it is well suited to the care of the aged.     

Meta-analysis of the effects of cognitive-behavioral therapy on the core eating disorder maintaining mechanisms: implications for mechanisms of therapeutic change

The original and enhanced cognitive model of eating disorders proposes that cognitive-behavioral therapy (CBT) "works" through modifying dietary restraint and dysfunctional attitudes towards shape and weight. However, evidence supporting the validity of this model is limited. This meta-analysis examined whether CBT can effectively modify these proposed maintaining mechanisms. Randomized controlled trials that compared CBT to control conditions or non-CBT interventions, and reported dietary restraint and shape and weight concern outcomes were searched. Twenty-nine trials were included. CBT was superior to control conditions in reducing shape (g=0.53) and weight (g=0.63) concerns, and dietary restraint (g=0.36). These effects occurred across all diagnoses and treatment formats. Improvements in shape and weight concerns and restraint were also greater in CBT than non-CBT interventions (g's=0.25, 0.24, 0.31, respectively) at post-treatment and follow-up. The magnitude of improvement in binge/purge symptoms was related to the magnitude of improvement in these maintaining mechanisms. Findings demonstrate that CBT has a specific effect in targeting the eating disorder maintaining mechanisms, and offers support to the underlying cognitive model. If changes in these variables during treatment are shown to be causal mechanisms, then these findings show that CBT, relative to non-CBT interventions, is better able to modify these mechanisms.     

等级反应多水平评分者漂移模型的构建

对于评定耗时较长的测验来说,时间因素对评分精确性的影响不容忽视,因此,评分者漂移方面的研究备受关注。研究基于康春花,孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型建构出可用于检测评分者漂移的等级反应多水平评分者漂移模型,并通过模拟研究对模型性能进行验证。结果表明：模型能够精确估计项目和能力参数;且与固定效应模型相比,评分者随机效应模型能更有效地检测出评分者漂移效应,随机效应模型的有效性和稳定性更佳。     

佛教五蕴系统——一种信息加工模型

作为佛教基础教义的“五蕴”是对心理机能尤其是认知过程的高度概括——人类信息加工包括色蕴、受蕴、想蕴、行蕴和识蕴五大机能系统。色蕴对感官信息进行初级加工;受蕴进行信息的情绪编码;随后在想蕴阶段进行高级整合,完成信息的概念语言表征;行蕴则为信息加工提供能量及方向导控;识蕴负责信息储存、监控调节及信息输出,最终形成完整的认知判断。佛教五蕴提供了一种信息加工的新模型。     

探析流行病防治的哲学思维

本文以现代医学模式为指导,阐述了辩证思维在流行病学研究领域的应用及其必要性。从系统论和具体问题具体分析论述了传染病和慢性病并重和区别对待的疾控观;通过消除病因和医防一体化以控制流行病。最后得出与哲学思维协调发展是流行病防治基本策略的结论。     

基于认知设计系统的几何类比推理测验的编制及认知模型分析

认知诊断是一种新的测量范式。本研究首先根据Mulholland,Pellegrino＆Glaser和Whitely＆Schneider关于类比推理的研究结果,采用认知设计系统编制了几何类比推理测试题,并对所编制的测试题的质量进行验证,然后采用口语报告法分析解决几何类比推理所涉及的认知模型,结果表明：1）编制的几何类比推理测试题的质量比较好;2）学生解决类比推理所涉及的认知属性主要为置换、翻转、旋转、数量、大小、颜色与条纹和形状。     

Dreaming and hallucinations - continuity or discontinuity? Perspectives from dementia with Lewy bodies

Comparing the phenomenology, neurochemical pathology, and psychopharmacology of hallucinations and dreaming is limited by the available data. Evidence to date reveals no simple correspondence between the two states. Differences in the phenomenology of visual hallucinations and the visual component of dreams may reflect variations in visual context acting on the same underlying mechanism – the minimal visual input during dreaming contrasts with the more substantial perceived context in hallucinations. Variations in cholinergic, dopaminergic and serotonergic neurotransmitter function during sleep and during hallucinations in Lewy body dementias, together with relevant drug effects suggest that, on the whole, different, potentially opposite, changes characterise the two states. A similar analysis of other psychotic features in Lewy body dementia and other disorders suggests that, in contrast to hallucinations, there may be more convincing parallels between dreaming and delusional states.