Self‐oriented perfectionism and its relationship to selective attention: An experimental examination using social cognitive paradigm1

The cognitive biases associated with perfectionism include a selective attention to failure and the discounting of success. The present study experimentally focused on the relation between self‐oriented perfectionism (SOP) and selective attention using the social cognitive paradigm. Forty undergraduate students who were identified as either high or low in SOP were asked to perform modified versions of the Stroop task. The results suggest that participants with high SOP did not take longer to respond to failure words than to neutral words, but their reaction time to failure words was longer than that of participants with low SOP. These findings provided the basis for cognitive behavioral biases for perfectionism.     

What Causes the Mean Bias of the Likelihood Ratio Statistic with Many Variables?

Abstract

Survey data often contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. However, conventional SEM methods are not crafted to handle data with a large number of variables (p). A large p can cause T_ml, the most widely used likelihood ratio statistic, to depart drastically from the assumed chi-square distribution even with normally distributed data and a relatively large sample size N. A key element affecting this behavior of T_ml is its mean bias. The focus of this article is to determine the cause of the bias. To this end, empirical means of T_ml via Monte Carlo simulation are used to obtain the empirical bias. The most effective predictors of the mean bias are subsequently identified and their predictive utility examined. The results are further used to predict type I errors of T_ml. The article also illustrates how to use the obtained results to determine the required sample size for T_ml to behave reasonably well. A real data example is presented to show the effect of the mean bias on model inference as well as how to correct the bias in practice.     

子女越孝顺老年人越幸福吗?老年人孝道期待的作用

孝道是影响老年人幸福感的重要变量之一, 然而, 以往研究却得出相互矛盾的结论, 其中可能的重要原因之一是没有聚焦老年人孝道期待的实现程度。采用二次响应面回归分析探究老年人孝道期待和子女孝道支持行为之间的匹配度对老年人生活满意度和孤独感的影响。研究结果表明, 对于生活满意度而言, 和不匹配相比, 情感期待和情感支持以及联络期待和联络支持匹配时老年人生活满意度更高; 在期待和支持匹配的情况下, 生活满意度随信息期待和信息支持的增加而提高; 并且老年人的生活满意度仅随工具支持的增加而提高, 但与工具期待没有关系。对于孤独感而言, 在情感期待和情感支持二者不匹配时, 较于情感期待低于情感支持, 情感期待高于情感支持时老年人的孤独感更高; 孤独感仅随联络支持和信息支持的增加而降低, 与联络期待、信息期待、工具期待以及工具支持没有关系。该研究得出以下结论：老年人孝道期待的实现程度是提高其生活满意度的重要因素, 而情感期待高于情感支持是影响老年人孤独感的重要因素。     

选择性注意对行人觉察有声信号灯的影响

为考察选择性注意如何影响行人整合交通信号中的多感觉加工优势,研究采用线索—靶子范式,操控交通信号类型(视觉/传统视觉信号灯、听觉、视听觉/有声信号灯)和信号呈现位置(线索化位置、非线索化位置)两个变量,分析被试对视觉、听觉、视听觉三类刺激的反应时和正确率,并通过对相对多感觉反应增强值(rMRE)和竞争模型(race model)的计算,对多感觉加工优势效应进行量化。结果发现:有声信号灯能够产生多感觉加工优势,选择性注意会减弱有声信号灯的多感觉加工优势。该研究结果为有声信号灯的设置提供了理论依据,且提示应当避免在环境中设置容易引起行人选择性注意的设施。     

友善特质与信息准确性对3～5岁幼儿选择性信任的影响

幼儿选择性信任的发展对幼儿社会认知和社会行为的发展有重要的促进作用。本研究探讨当信息提供者的友善特质与所提供信息的准确性相冲突时,幼儿在认知信任问题与人际信任问题上的信任选择特点。以103名3～5岁幼儿为被试,研究结果发现3～4岁幼儿在与认知信任有关问题中做出的选择与随机水平相比无显著差异,只有5岁幼儿表现出了对准确命名但不友善的信息提供者的赞同和询问偏好; 而在人际信任判断中,3～5岁幼儿均选择与具有友善特质的人为友,并将物品交于其保管。研究结果说明不同年龄段的幼儿表现出不同的信任选择倾向,且幼儿能依据不同问题灵活做出信任选择。     

Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

Drivers overtaking cyclists on rural roads are a safety concern, as drivers need to handle the interaction with the cyclist and possibly an oncoming vehicle. Improving the maneuver’s outcome requires an understanding of not only the objective, measurable safety metrics, but also the subjective, perceived safety of each road user. Previous research has shown that the perceived safety of the cyclist is most at risk at the passing moment, when driver and cyclist are closest to each other. However, to develop safety measures, it is necessary to know how both road users perceive safety, by understanding the factors that influence their perceptions during the overtaking maneuver. This study measured the perceived safety of drivers in a test-track experiment in Sweden and the perceived safety of cyclists in a field test in Spain. For both drivers and cyclists, we developed Bayesian ordinal logistic regression models of perceived safety scores that take as input objective safety metrics representing the different crash risks at the passing moment. Our results show that while drivers’ perceived safety decreases when there is an oncoming vehicle with a low time-to-collision, cyclists’ perceived safety is reduced by a small lateral clearance and a high overtaking speed. Although our datasets are heterogeneous and limited, our results are in line with previous research. In addition, the Bayesian models presented in this paper are novel and may be improved in future studies once more naturalistic data become available. We discuss how our models may support infrastructure development and regulation, policymaking, driver coaching, the development of active safety systems, and automated driving by providing a possible method for predicting perceived safety.     

Why would people want to travel more with automated cars?

The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV.We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe).The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes.     

青少年抑郁与自伤关系的追踪研究：亲子沟通的调节作用

为考察青少年抑郁与自伤的双向关系及亲子沟通的调节效应,采用青少年自我伤害问卷、流动中心用抑郁量表和亲子沟通量表对595名初一学生进行为期10个月的追踪研究。结果发现,从初一到初二,抑郁呈增长趋势,且性别差异显著,而自伤的增长趋势不明显,但到初二后表现出了明显的性别差异;抑郁和自伤的同时性相关和继时性相关均显著;前测抑郁对后测自伤有明显的预测作用,但前测自伤对后测抑郁的预测作用不明显;父子沟通和母子沟通在前测抑郁与后测自伤水平之间有显著的负向调节作用。     

An Empirical Investigation of Partial Effect Sizes in Meta-Analysis of Correlational Data

ABSTRACT. The partial correlation and the semi-partial correlation can be seen as measures of partial effect sizes for the correlational family. Thus, both indices have been used in the meta-analysis literature to represent the relationship between an outcome and a predictor of interest, controlling for the effect of other variables in the model. This article evaluates the accuracy of synthesizing these two indices under different situations. Both partial correlation and the semi-partial correlation appear to behave as expected with respect to bias and root mean squared error (RMSE). However, the partial correlation seems to outperform the semi-partial correlation regarding Type I error of the homogeneity test (Q statistic). Although further investigation is needed to fully understand the impact of meta-analyzing partial effect sizes, the current study demonstrates the accuracy of both indices.     

Estimating Causal Impacts of School Counselors With Regression Discontinuity Designs

This article presents a causal regression discontinuity framework for quantifying the impact of high school counselors on students’ education outcomes. To demonstrate this method, the authors used data from the National Center for Education Statistics’ Schools and Staffing Survey (SASS). Using high school counselor staffing counts and 4‐year college‐going rates collected through the SASS, the authors found that an additional high school counselor is predicted to induce a 10 percentage point increase in 4‐year college enrollment.