Effects of the range and frequency of vibrations on the momentary riding comfort evaluation of a railway vehicle

When trains pass level-crossings, turnouts, and rail joints, they are momentarily subjected to extreme vibrations. In railway engineering, evaluation of the riding comfort under such occasional vibrations is called the momentary riding comfort evaluation, as distinct from the long-term evaluation, which addresses the riding comfort of passengers for certain lengths of train operation. In order to identify the effective vibrational characteristics of the momentary evaluation, an experiment was performed with a riding comfort simulator. Ten adult subjects for each condition, 80 in total, participated in the experiment. The effects of differences in the range of stimuli, frequency of each stimulus, and scores on a rating scale of discomfort were studied. Differences in the range and frequency affected the evaluation such that subjects tended to make a relative judgment on discomfort. They made almost an absolute judgment when the rating scale was well defined, with a small number of categories.     

社会认知中印象形成的启动效应研究:频因,近因效应

本文以社会认知为出发点,考察印象形成的启动效应,它采用性质不同的成语作为实验材料,并操纵启动刺激的结构,系统探讨了印象形成的启动效应中原因与近因效应,结果发现：当呈现表示同一品质但性质相反的成语时,如果启动刺激和目标刺激之间不延迟,则近因在启动效应中起作用,但在延迟情况下,频因在启动效应中占优势;同时随着延迟的出现,启动效应明显变小。实验结果支持启动效应的突触模式说;     

The curvilinear effect of work engagement on employees' turnover intentions

Numerous studies have shown the positive consequences of work engagement for both organisations and employees experiencing it. For instance, research has demonstrated that work‐engaged employees have lower levels of turnover intentions than non‐engaged employees. However, in this research, we examined whether there is a dark side of work engagement. More precisely, we investigated whether the relationship between work engagement and employees' turnover intentions might be non‐linear. Based on two different samples, our results indicated that the relationship between work engagement and employees' turnover intentions is curvilinear. The theoretical and practical implications of these results are discussed.     

The absence of numbers to express the amount may affect delay discounting with humans

Human delay discounting is usually studied with experimental protocols that use symbols to express delay and amount. In order to further understand discounting, we evaluated whether the absence of numbers to represent reward amounts affects discount rate in general, and whether the magnitude effect is generalized to nonsymbolic situations in particular. In Experiment 1, human participants were exposed to a delay‐discounting task in which rewards were presented using dots to represent monetary rewards (nonsymbolic); under this condition the magnitude effect did not occur. Nevertheless, the magnitude effect was observed when equivalent reward amounts were presented using numbers (symbolic). Moreover, in estimation tasks, magnitude increments produced underestimation of large amounts. In Experiment 2, participants were exposed only to the nonsymbolic discounting task and were required to estimate reward amounts in each trial. Consistent with Experiment 1, the absence of numbers representing reward amounts produced similar discount rates of small and large rewards. These results suggest that value of nonsymbolic rewards is a nonlinear function of amount and that value attribution depends on perceived difference between the immediate and the delayed nonsymbolic rewards.     

A Test by Any Other Name: P Values,Bayes Factors,and Statistical Inference

Procedures used for statistical inference are receiving increased scrutiny as the scientific community studies the factors associated with insuring reproducible research. This note addresses recent negative attention directed at p values, the relationship of confidence intervals and tests, and the role of Bayesian inference and Bayes factors, with an eye toward better understanding these different strategies for statistical inference. We argue that researchers and data analysts too often resort to binary decisions (e.g., whether to reject or accept the null hypothesis) in settings where this may not be required.     

A Comparative Investigation of Confidence Intervals for IndependentVariables in Linear Regression

In linear regression, the most appropriate standardized effect size for individual independent variables having an arbitrary metric remains open to debate, despite researchers typically reporting a standardized regression coefficient. Alternative standardized measures include the semipartial correlation, the improvement in the squared multiple correlation, and the squared partial correlation. No arguments based on either theoretical or statistical grounds for preferring one of these standardized measures have been mounted in the literature. Using a Monte Carlo simulation, the performance of interval estimators for these effect-size measures was compared in a 5-way factorial design. Formal statistical design methods assessed both the accuracy and robustness of the four interval estimators. The coverage probability of a large-sample confidence interval for the semipartial correlation coefficient derived from Aloe and Becker was highly accurate and robust in 98% of instances. It was better in small samples than the Yuan-Chan large-sample confidence interval for a standardized regression coefficient. It was also consistently better than both a bootstrap confidence interval for the improvement in the squared multiple correlation and a noncentral interval for the squared partial correlation.     

Modeling Clustered Data with Very Few Clusters

Small-sample inference with clustered data has received increased attention recently in the methodological literature, with several simulation studies being presented on the small-sample behavior of many methods. However, nearly all previous studies focus on a single class of methods (e.g., only multilevel models, only corrections to sandwich estimators), and the differential performance of various methods that can be implemented to accommodate clustered data with very few clusters is largely unknown, potentially due to the rigid disciplinary preferences. Furthermore, a majority of these studies focus on scenarios with 15 or more clusters and feature unrealistically simple data-generation models with very few predictors. This article, motivated by an applied educational psychology cluster randomized trial, presents a simulation study that simultaneously addresses the extreme small sample and differential performance (estimation bias, Type I error rates, and relative power) of 12 methods to account for clustered data with a model that features a more realistic number of predictors. The motivating data are then modeled with each method, and results are compared. Results show that generalized estimating equations perform poorly; the choice of Bayesian prior distributions affects performance; and fixed effect models perform quite well. Limitations and implications for applications are also discussed.     

A single bout of meditation biases cognitive control but not attentional focusing: Evidence from the global–local task

Recent studies show that a single bout of meditation can impact information processing. We were interested to see whether this impact extends to attentional focusing and the top-down control over irrelevant information. Healthy adults underwent brief single bouts of either focused attention meditation (FAM), which is assumed to increase top-down control, or open monitoring meditation (OMM), which is assumed to weaken top-down control, before performing a global–local task. While the size of the global-precedence effect (reflecting attentional focusing) was unaffected by type of meditation, the congruency effect (indicating the failure to suppress task-irrelevant information) was considerably larger after OMM than after FAM. Our findings suggest that engaging in particular kinds of meditation creates particular cognitive-control states that bias the individual processing style toward either goal-persistence or cognitive flexibility.