定序变量回归模型在心理数据分析中的应用

定序变量在心理现象和心理数据中随处可见, 采用综合的定序变量回归分析模型可以对“镜像模式”和“漏斗模型”的心理现象做出合理的解释和预测。首先通过非参数检验对影响因素进行初步降维, 其次用Probit定序回归对降维后的影响因素贡献率进行判别, 从而进一步筛选具有显著性判断水平的有效指标, 最后用Logistic回归模型对某种特定的心理现象发生与否进行信息量足够大的解释和预测。大学毕业生工作生活质量满意度的预测对这种综合定序变量回归分析模型的实例拟合, 证实了综合定序变量回归分析模型在心理现象和心理数据分析中的应用价值。     

习得的语言范畴影响颜色知觉的机制:来自ERP的证据

本研究以两个实验探讨了习得的语言范畴影响颜色知觉的机制.实验1让被试接受短期“色-词”重组训练,使原先范畴内的两种颜色变为范畴间的颜色,训练前后完成视觉搜索任务.反应时结果显示,在训练后,与训练的两种颜色近似的另外两种颜色的知觉表现出边缘显著的偏侧化颜色范畴效应.实验2让被试接受与实验1相同的训练后,完成视觉Oddball任务.ERP结果显示,在训练后,与训练的两种颜色近似的另外两种颜色的偏差刺激在早期知觉阶段就表现出偏侧化vMMN效应.这些结果表明:习得的语言范畴能影响早期的、注意前的颜色知觉机能,且这一影响过程可以在短期内完成;习得的语言范畴会影响颜色知觉范畴,而非仅影响特定颜色点的知觉机能.     

面部表情的类别知觉及其个体差异

面部表情类别知觉效应是指连续变化的表情在人脑中按照类别表征, 且不同类别的表情之间存在明显的类别界线。介绍了面部表情类别知觉效应的行为和脑电研究范式及基础研究, 综述了攻击性、抑郁症、精神分裂症、社交焦虑等对面部表情类别知觉的影响, 以及面部表情类别知觉的发展研究。综述表明, 类别知觉研究能够揭示不同人群表情知觉类别界线及其偏移情况, 有助于解释相应的社会行为。未来研究应注重对面部表情类别知觉效应的个体差异和神经生理机制的探索。     

视觉空间关系判断的分离与协同

采用任务表征相互影响范式,通过三个实验探讨了类别空间关系判断和数量空间关系判断的加工特性和相互关系.结果表明:(1)先行类别关系启动有利于数量空间关系判断,对类别空间关系判断没有影响;先行数量关系启动对两个判断任务均无影响.(2)先行类别关系干扰降低两个空间关系判断的绩效,先行数量关系干扰对两个空间关系判断没有影响.(3)先行类别关系对空间关系判断的启动和干扰效应不局限于特定条件,具有普遍性.研究提示,右脑为优势半球的数量关系加工以左脑为优势半球的类别关系加工为基础,支持视觉空间认知加工既分离又协同的观点.     

Bad News for Moral Error Theorists: There Is No Master Argument Against Companions in Guilt Strategies

A ‘companions in guilt’ (CG) strategy against moral error theory aims to show that the latter proves too much: if sound, it supports an implausible error-theoretic conclusion in other areas such as epistemic or practical reasoning. Christopher Cowie [2016 Cowie, C. 2016. Good News for Moral Error Theorists: A Master Argument Against Companions in Guilt Strategies, Australasian Journal of Philosophy 94/1: 115–30.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] has recently produced what he claims is a ‘master argument’ against all such strategies. The essence of his argument is that CG arguments cannot work because they are afflicted by internal incoherence or inconsistency. I argue, first, that Cowie's master argument does not succeed. Beyond this, I argue that there is no good reason to think that any such argument—one that purports to identify an internal incoherence in CG arguments—can succeed. Second, I argue that the main substantive area of disagreement between error theorists and CG theorists essentially concerns the conceptual profile of epistemic reasons—specifically, whether they are strongly categorical—not the ontological question of whether such reasons exist (in some form or other). I then develop an argument in favour of the CG theorist's position by considering the moral error theorist's arguments in support of the conceptual claim that moral reasons are strongly categorical. These include, notably, criticisms made by Joyce [2011] and Olson [2014] of Finlay's [2008] ‘end relational’ view of morality, according to which moral reasons are relative to some end or standard, hence not strongly categorical. Examining these criticisms, I argue that, based on what moral error theorists have said regarding the conceptual profile of moral reasons, there is a strong case to be made that moral reasons are strongly categorical (hence, according to the moral error theorist, ontologically problematic) if and only if epistemic reasons are.     

Full Information Maximum Likelihood Estimation for Latent Variable Interactions With Incomplete Indicators

Researchers have developed missing data handling techniques for estimating interaction effects in multiple regression. Extending to latent variable interactions, we investigated full information maximum likelihood (FIML) estimation to handle incompletely observed indicators for product indicator (PI) and latent moderated structural equations (LMS) methods. Drawing on the analytic work on missing data handling techniques in multiple regression with interaction effects, we compared the performance of FIML for PI and LMS analytically. We performed a simulation study to compare FIML for PI and LMS. We recommend using FIML for LMS when the indicators are missing completely at random (MCAR) or missing at random (MAR) and when they are normally distributed. FIML for LMS produces unbiased parameter estimates with small variances, correct Type I error rates, and high statistical power of interaction effects. We illustrated the use of these methods by analyzing the interaction effect between advanced cancer patients’ depression and change of inner peace well-being on future hopelessness levels.     

A Comparison of Methods for Creating Multiple Imputations of Nominal Variables

Many variables that are analyzed by social scientists are nominal in nature. When missing data occur on these variables, optimal recovery of the analysis model's parameters is a challenging endeavor. One of the most popular methods to deal with missing nominal data is multiple imputation (MI). This study evaluated the capabilities of five MI methods that can be used to treat incomplete nominal variables: multiple imputation with chained equations (MICE) using polytomous regression as the elementary imputation method; MICE based on classification and regression trees (CART); MICE based on nested logistic regressions; the ranking procedure described by Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]); and a joint modeling approach based on the general location model. We first motivate our inquiry with an applied example and then present the results of a Monte Carlo simulation study that compared the performance of the five imputation methods under conditions of varying sample size, percentage of missing data, and number of nominal response categories. We found that MICE with polytomous regression was the strongest performer while the Allison (2002 Allison, P. D. (2002). Missing data. Thousand Oaks, CA: Sage Publications. https://doi.org/10.4135/9780857020994.n4[Crossref] , [Google Scholar]) ranking procedure and MICE with CART performed poorly in most conditions.     

Evaluation of Multi-parameter Test Statistics for Multiple Imputation

In Ordinary Least Square regression, researchers often are interested in knowing whether a set of parameters is different from zero. With complete data, this could be achieved using the gain in prediction test, hierarchical multiple regression, or an omnibus F test. However, in substantive research scenarios, missing data often exist. In the context of multiple imputation, one of the current state-of-art missing data strategies, there are several different analogous multi-parameter tests of the joint significance of a set of parameters, and these multi-parameter test statistics can be referenced to various distributions to make statistical inferences. However, little is known about the performance of these tests, and virtually no research study has compared the Type 1 error rates and statistical power of these tests in scenarios that are typical of behavioral science data (e.g., small to moderate samples, etc.). This paper uses Monte Carlo simulation techniques to examine the performance of these multi-parameter test statistics for multiple imputation under a variety of realistic conditions. We provide a number of practical recommendations for substantive researchers based on the simulation results, and illustrate the calculation of these test statistics with an empirical example.     

Behavioral landscapes and earth mover’s distance: A new approach for studying individual differences in density distributions

Contemporary views of personality highlight intraindividual variability. We forward a general method for quantifying individual differences in behavioral tendencies based on Earth Mover’s Distance. Using data from 150 individuals who reported on their and others’ interpersonal behavior in 64,112 social interactions, we illustrate how this new approach can advance notions of personality as density distributions. Results provide independent confirmation and establish validity of existing representations of individual differences in interpersonal behavior, and identify new dimensions and profiles of personality and well-being. Benefits of the EMD method include freedom from assumptions about the shape and form of density distributions, generality of application to n-dimensional behavior captured in experience sampling studies, and natural integration of personality structure and dynamics.     

How semantic organisation influences primary school children’s working memory

The present study focuses on the semantic organisation of material in working memory. We developed a new measure in which students memorised unrelated words from lists. In our study, we manipulated the association between words in the lists. The material was organised so as to elicit a semantic organisation (categorical and thematic). The task was then administered to a group of 6–10-year-old children. The semantic organisation of the material prompted a better recall, which depended on the type of semantic organisation. In the same vein, the number of intrusion errors was influenced by the semantic links between words and was higher when words in the list were associated categorically. These results seemed to depend partly on the participants’ age, being evident only in the younger children.