共同方法偏差检验：问题与建议

检验共同方法偏差(CMB)已经成为心理学实证研究中的一个环节。本文从数学模型角度分析方法变异(CMV)的影响,并讨论了CMB常用的检验法——Harman单因子法、控制未测量的潜在方法因子(ULMC)法、验证性因子分析(CFA)标签变量法的检验力。Harman单因子法检验力很低,ULMC法检验力中等,CFA标签变量法检验力虽然较高但问题也不少。提出一个好的检验法应当满足的三个特点：符合CMV的数学模型、评价标准不受非CMV来源的影响、对CMV、CMB的变化敏感。最后给出CMB检验的建议。     

防御性自尊大学生注意偏向对记忆偏向的影响

摘 要 为考查防御性自尊大学生注意偏向对记忆偏向的影响,本研究将被试分为训练组和控制组,训练组接受注意偏向训练,之后完成记忆任务。记忆任务中呈现中性和攻击性词汇,要求被试对词汇进行自由回忆。结果发现,控制组对攻击性信息存在着记忆偏向,而训练组对攻击性信息的记忆偏向消失。注意偏向训练可以即时性地改变防御性自尊大学生对攻击性信息的注意偏向,注意偏向可能是特质一致性记忆的原因之一。     

Gender-Typed Behavioral Examples of Histrionic Personality Disorder

Findings that clinicians diagnose Histrionic Personality Disorder more frequently in women may be due to the feminine gender weighting of the criteria or because the diagnostic label elicits a feminine stereotype. Using a method derived from the act-frequency approach, undergraduates generated behavioral examples of the DSM-IIIR and DSM-IV Histrionic criteria without regard to sex or according to sex role instructions that elicited masculine or feminine sex roles. A national sample of psychologists and psychiatrists rated the representativeness of the symptoms for the Histrionic criteria or for Histrionic Personality Disorder. Feminine behaviors were rated more representative of Histrionic Personality Disorder and somewhat more representative of the Histrionic criteria than masculine behaviors suggesting that the feminine sex role is more strongly associated with the label than the criteria. Masculine behaviors were also rated less representative than sex-unspecified examples. Results provide a possible explanation for the higher rates of diagnosis of Histrionic Personality Disorder in women.     

The asymptotic bias of minimum trace factor analysis, with applications to the greatest lower bound to reliability

In theory, the greatest lower bound (g.l.b.) to reliability is the best possible lower bound to the reliability based on single test administration. Yet the practical use of the g.l.b. has been severely hindered by sampling bias problems. It is well known that the g.l.b. based on small samples (even a sample of one thousand subjects is not generally enough) may severely overestimate the population value, and statistical treatment of the bias has been badly missing. The only results obtained so far are concerned with the asymptotic variance of the g.l.b. and of its numerator (the maximum possible error variance of a test), based on first order derivatives and the asumption of multivariate normality. The present paper extends these results by offering explicit expressions for the second order derivatives. This yields a closed form expression for the asymptotic bias of both the g.l.b. and its numerator, under the assumptions that the rank of the reduced covariance matrix is at or above the Ledermann bound, and that the nonnegativity constraints on the diagonal elements of the matrix of unique variances are inactive. It is also shown that, when the reduced rank is at its highest possible value (i.e., the number of variables minus one), the numerator of the g.l.b. is asymptotically unbiased, and the asymptotic bias of the g.l.b. is negative. The latter results are contrary to common belief, but apply only to cases where the number of variables is small. The asymptotic results are illustrated by numerical examples.This research was supported by grant DMI-9713878 from the National Science Foundation.     

元认知与面部认知的结合：切入点及方法

元认知是对认知的认知,包含认知的各个方面,近年来元认知与面部认知的结合开始得到关注。两者结合切入点包括元认知错觉(达克效应和自我中心偏见)在面部认知中的适用性,以及面部认知现象(异族效应和熟悉度优势)在元认知中的适用性。研究方法根据测量时间点和评估对象的选取而各有侧重。当前研究尚停留在元认知监测层面,未来可扩展至面部认知的元认知控制、与机器学习结合等方向,为理解面部认知提供新角度,扩展其应用价值。     

无法忍受不确定性与担忧：对记忆偏差与解释偏差的检验

本研究旨在探索记忆偏差与解释偏差在无法忍受不确定性(IU)与个体担忧之间的中介作用。实验1采用伴随学习任务范式,计算被试对中性词语和不确定词的回忆量。结果发现,IU对记忆偏差的预测不显著,记忆偏差在IU与担忧倾向之间的中介作用不显著。实验2采用情境评估任务范式收集被试在不确定情境下的担忧评分和解释倾向。结果发现,IU可以正向预测被试在不确定情境下的担忧评分,被试对情境的解释倾向在IU与担忧评分的关系中起到中介作用。概言之,高IU个体存在一定的信息加工偏差。     

What makes a word so attractive? Disclosing the urge to read while bisecting

Expert readers have been repeatedly reported to misperceive the centre of visual stimuli, shifting systematically to the left the bisection of any lines (pseudoneglect) while showing a cross-over effect while bisecting different types of orthographic strings (Arduino et al., 2010, Neuropsychologia, 48, 2140). This difference has been attributed to asymmetrical allocation of attention that visuo-verbal material receives when lexical access occurs (e.g., Fischer, 2004, Cognitive Brain Research, 4, 163). The aim of this study was to further examine which visual features guide recognition of potentially orthographic materials. To disentangle the role of orthography, heterogeneity, and visuo-perceptual discreteness, we presented Italian unimpaired adults with four experiments exploiting the bisection paradigm. The results showed that a cross-over effect emerges in most discrete strings, especially when their internal structure, that is being composed of heterogeneous elements, is suggestive of orthographically relevant material. Interestingly, the cross-over effect systematically characterized the processing of letter strings (Experiment 2) and words (Experiments 3 and 4), whether visually discrete or not. Overall, this pattern of results suggests that neither discreteness nor heterogeneity per se are responsible for activating visual scanning mechanisms implied in text exploration, although both contribute to increasing the chance of a visual stimulus undergoing a perceptual analysis dedicated to pre-lexical processing.     

对体胖者的社会性偏向：本土化视角的探索

通过三个研究从本土化视角探索人们对体胖者的社会性偏向特征。研究1采用访谈法和问卷法获得了被试对体胖者的刻板印象内容；研究2使用情感温度计和AMP范式对体胖者的外显与内隐偏见进行了测量；研究3通过情境测量对比人们在不同生活情境中对体胖者的行为反应。结果表明，中国被试对体胖者的刻板印象内容提名以高热情特质为主，刻板印象评价呈现低能力为主的特征；人们对体胖者没有外显偏见，但存在一定内隐偏见；社会生活情境中体胖者可能遭遇歧视，对女性体胖者的歧视程度更高。总体上，人们对体胖者的社会性偏向表现出复合性的本土化特征。     

Dyadic judgments based on conflicting samples: The failure to ignore invalid input

The present research demonstrates a so far unrecognized impediment of group performance, metacognitive myopia (Fiedler, 2012). Judges and decision-makers follow the given samples of information uncritically and neglect the metacognitive assessment of the samples' validity. Applying this notion to dyadic judgments, we instructed dyads to jointly estimate conditional probabilities p (Win|A) and p (Win|B) of Lotteries A and B. One person per dyad experienced a valid sample (winning rates conditional on lotteries). The other person experienced an invalid, reverse sample (lotteries conditional on winning). Whereas valid samples provide unbiased estimates of lotteries' winning probabilities, invalid samples can greatly misrepresent the association of winning and lotteries (depending on lottery base rates). Across three experiments, metacognitive myopia—both at the individual and at the dyadic group level—prevented participants from discriminating valid and invalid samples. Group judgments were biased toward erroneous implications of invalid samples, reflecting an equality bias among unequal group members.     

Causal graphical views of fixed effects and random effects models

Despite the long-standing discussion on fixed effects (FE) and random effects (RE) models, how and under what conditions both methods can eliminate unmeasured confounding bias has not yet been widely understood in practice. Using a simple pretest–posttest design in a linear setting, this paper translates the conventional algebraic formalization of FE and RE models into causal graphs and provides intuitively accessible graphical explanations about their data-generating and bias-removing processes. The proposed causal graphs highlight that FE and RE models consider different data-generating models. RE models presume a data-generating model that is identical to a randomized controlled trial, while FE models allow for unobserved time-invariant treatment–outcome confounding. Augmenting regular causal graphs that describe data-generating processes by adding the computational structures of FE and RE estimators, the paper visualizes how FE estimators (gain score and deviation score estimators) and RE estimators (quasi-deviation score estimators) offset unmeasured confounding bias. In contrast to standard regression or matching estimators that reduce confounding bias by blocking non-causal paths via conditioning, FE and RE estimators offset confounding bias by deliberately creating new non-causal paths and associations of opposite sign. Though FE and RE estimators are similar in their bias-offsetting mechanisms, the augmented graphs reveal their subtle differences that can result in different biases in observational studies.