Communicating safety with drivers via dynamic message signs

In recent years numerous transportation agencies have begun displaying creative safety messages like “get your head out of your apps” on dynamic message signs (DMS). DMS have been used traditionally to provide real-time information about travel times, weather conditions, construction, and incidents. Applying emotional and consequential concepts from protection motivation theory, which has a track record of leading to behavioral changes from public awareness campaigns, this study evaluates how the use of safety messages on DMS impacts driver perceptions. We find that emotions, but not consequences, impact self-reported driving behavior. Further, while drivers reported negative emotions would change their driving habits, positive messages were recalled far more often. Cell phone related messages were recalled most frequently despite being rarely displayed along roadways in Michigan where the study is conducted. Thus, the results support the use of positively themed messages with consequences that drivers perceive as realistic.     

Testing Process Factor Analysis Models Using the Parametric Bootstrap

Process factor analysis (PFA) is a latent variable model for intensive longitudinal data. It combines P-technique factor analysis and time series analysis. The goodness-of-fit test in PFA is currently unavailable. In the paper, we propose a parametric bootstrap method for assessing model fit in PFA. We illustrate the test with an empirical data set in which 22 participants rated their effects everyday over a period of 90 days. We also explore Type I error and power of the parametric bootstrap test with simulated data.     

Deconstructing trait anxiety: a network perspective

Background and objectives: For decades, the dominant paradigm in trait anxiety research has regarded the construct as signifying the underlying cause of the thoughts, feelings, and behaviors that supposedly reflect its presence. Recently, a network theory of personality has appeared. According to this perspective, trait anxiety is a formative construct emerging from interactions among its constitutive features (e.g., thought, feelings, behaviors); it is not a latent cause of these features.

Design: In this study, we characterized trait anxiety as a network system of interacting elements.

Methods: To do so, we estimated a graphical gaussian model via the computation of a regularized partial correlation network in an unselected sample (N?=?611). We also implemented modularity-based community detection analysis to test whether the features of trait anxiety cohere as a single network system.

Results: We find that trait anxiety can indeed be conceptualized as a single, coherent network system of interacting elements.

Conclusions: This radically new approach to visualizing trait anxiety may offer an especially informative view of the interplay between its constitutive features. As prior research has implicated trait anxiety as a risk factor for the development of anxiety-related psychopathology, our findings also set the scene for novel research directions.     

What if they are right? Network approval,expectations, and relationship maintenance behaviors

This study drew on uncertainty reduction and decision‐making theories to investigate how perceptions of approval of romantic relationships from family and friends can influence romantic partners' dynamics. Using a dyadic approach, the authors examined whether expectations of a partner's behavior in the relationship mediated the associations between perceived social network approval and relationship maintenance behaviors in a sample of 137 couples. The actor–partner interdependence mediation model (APIMeM) was applied. Results showed that women's and men's perceptions of approval from their own and their partner's network were associated with their own level of expectations of their partner's behaviors. In turn, women's and men's expectations were associated with their own and their partner's maintenance behaviors.     

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.     

Enterprise credit risk evaluation based on neural network algorithm

To explore the enterprise credit risk evaluation, the application effect of several common neural network models in Chinese small and medium-sized enterprise data sets was compared and the optimal parameters for each model were determined. In addition, the classification accuracy and the applicability of the model were compared, and finally the common problem of optimization neural network algorithm based on population was solved: need to determine the dimensions in advance. The experimental results showed that the probabilistic neural network (PNN) had the minimum error rate and second types of errors, while the PNN model had the highest AUC value and was robust. To sum up, the algorithm makes some contributions to solve the financing problem of small and medium-sized enterprises in China.     

Attentional orienting and awareness: evidence from a discrimination task

We used several cue–target SOAs (100, 500, 1000 ms) and three different degrees of cue predictability (Non-predictive-50%, Predictive-75%, Counter-predictive-25%), to investigate the role of awareness of cue–target predictability on cueing effects. A group of participants received instructions about the informative value of the cue, while another group did not receive such instructions. Participants were able to extract the predictive value of a spatially peripheral cue and use it to orient attention, whether or not specific instructions about the predictive value of the cue were given, and no matter their ability to correctly report it in a post-test questionnaire. In the non-predictive block, bad estimators who received no instructions showed regular cueing effects, while good estimators exhibited smaller and non-significant facilitatory effects at the short SOA and an absence of significant IOR at longer SOAs. However, for the instructions group, the pattern of results reversed.     

Un regard structural sur l’inégalité des genres au travail : La causalité des différences « genrées » dans les caractéristiques de réseau de l’employé

This study aims at identifying gender differences in network structure and, hence, at understanding the underlying nature of gender inequalities in organisations. The hypotheses address four ego-network characteristics: network size and density, and ego’s centrality and brokerage. Contrary to the existing body of literature, none significant isolated gender difference in network structure is identified. Instead, those gender differences are triggered exclusively by organisational status, that is to say, they are due to the increased level of power. Thus, organisational status is considered as moderating the relationship between gender and network structure’s characteristics. Some further perspectives are suggested to clarify and generalise the phenomena observed.     

基于IRT模型的儿童图形推理能力动态评估研究

MRMLC模型是一种常见的动态评估项目反应理论模型。本研究结合小学儿童图形推理能力发展的特点,采用前测-干预-后测的动态评估模式,对来自江西省北部的四到六年级共177名儿童的图形推理能力进行了基于MRMLC模型的动态评估。研究结果表明通过动态评估我们不仅可以获知儿童图形推理能力的当前水平;而且,还可以进一步分析和比较儿童在各测量情景所测的潜在水平。