A generalized method of image analysis from an intercorrelation matrix which may be singular

Following the works of Guttman (1953) and Kaiser (1976), we show that the image and anti-image covariance matrices can be derived from a singular correlation matrix, by making use of the orthogonal projector and a weaker generalized inverse matrix.     

Biases in social comparisons: Optimism or pessimism?

Social comparisons typically lead to two kinds of biases: A comparative optimism bias (i.e., a tendency for people to view themselves as more likely than others to be the beneficiaries of positive outcomes) or a comparative pessimism bias (i.e., a tendency for people to view themselves as less likely than others to be such beneficiaries); rarely are people fully calibrated in terms of how they compare to others. However, there is little systematic research on the factors that determine when a comparative optimism versus pessimism bias will occur, how they can be attenuated and whether such attenuation is always desirable. In this paper, we report four studies which demonstrate the following key results: First, we show that perceived level of control over the outcome drives whether a comparative optimism or pessimism bias will occur (Study 1). Second, an increase in perceived similarity between self and a comparison target person attenuates the comparative optimism bias in domains that people view as highly controllable (Study 2a) and attenuates the comparative pessimism bias in domains that people view as less controllable (Study 2b). Finally, we show that people are willing to work harder when they experience more comparative optimism in higher control scenarios and when they experience less comparative pessimism in lower control scenarios, illustrating that motivating people to strive harder for positive outcomes can result from exacerbated or attenuated bias, depending on the context (Study 3).     

Trait ratings of the in-group and out-group with minimal acquaintance: Differentiation and out-group favorability

Malloy et al. (2011) studied trait judgments and behavior of Black and White men during face-to-face interactions at zero-acquaintance and found that intergroup responses were asymmetric. The present research extends that work. In Study 1 Black and White men observed two dyadic interactions from the Malloy et al (2011) study and rated in-group targets’ traits. In Study 2, using the type generation paradigm, Black and White males and females generated types of persons from their racial in-group or out-group and rated their traits. Blacks differentiated the unique traits of Whites to a greater extent than Whites differentiated the unique traits of Blacks. Blacks and Whites judged out-group targets’ traits more positively than those of in-group members, and both differentiated the unique traits of in-group members more than out-group members.     

Triangulating Political Responsibility: The Motivated Formation of Responsibility Judgments

This article analyzes the process through which partisan bias arises during the formation of citizens' judgments of political responsibility. Informed by theories of motivated political reasoning, it argues that exposure to partisan cues motivates partisans to pursue directional goals, goals which bias the cognitive processing of information and, in turn, overall judgments of responsibility. It further argues that the nature of this biased processing will be such that partisans devalue information inconsistent with their partisan affect. Using a pair of experiments, I test these hypotheses by manipulating both objective evidence concerning gubernatorial responsibility for a state's fiscal imbalance and the presence of partisan cues. Findings support both sets of expectations. The results also suggest that the effects of partisan bias are greater in judgments tied to institutional actions than in those tied to institutional roles and expectations.     

社会判断偏差的分化研究：人物因素的影响

实验采用投射的方法,利用图片和问题材料,考察了128名本科生对物、事、人的社会判断偏差的分化现象。实验结果表明：1）社会判断内在的宽大效应存在局限性,它是有条件限制的,受到所介入的人物因素的影响而出现分化现象;2）对物与事的评价中,突出了对性别判断标准的差异;3）在社会人的对比中,容貌、性别、背景在对同一事件的判断中发挥着重要作用,使社会判断出现偏差分化。     

Was Life Better in the “Good Old Days”? Intertemporal Judgments of Life Satisfaction

Intertemporal judgments are paired comparisons between the present time and some other time (e.g. “How satisfied are you with your life these days compared to five years ago?”). These judgments can provide evidence on the question, “Is life satisfaction in developed nations increasing, decreasing, or remaining constant?” This paper provides the first review of intertemporal judgments of life satisfaction, and reports a meta-analysis of 71 such studies from 9 developed countries. Results show that in every survey that asks people how happy their own lives are now compared to some past time, a majority says they are happier now than in the past. The meta-analysis also shows that this question must be carefully distinguished from asking about quality of life of the average person, which shows a majority believing that life was better for the “average person” in the past. These two beliefs are logically inconsistent, since the average respondent actually says their satisfaction is higher now. We consider several sources of bias that may influence these results, including cognitive processing heuristics, self-appraisal (desirability) bias, and information bias in media news.     

Pseudoneglect: evidence for both perceptual and attentional factors

When neurologically normal individuals bisect a horizontal line as accurately as possible, they reliably show a slight leftward error. This leftward inaccuracy is called pseudoneglect because errors made by neurologically normal individuals are directionally opposite to those made by persons with visuospatial neglect (Jewell & McCourt, 2000). In the current study, normal right-handed observers bisected horizontal lines that were altered to bias line length judgments either toward the right or the left side of the line. Non-target dots were placed on or near the line stimuli using principles derived from a theory of visual illusions of length called centroid extraction (Morgan, Hole, & Glennerster, 1990). This theory argues that the position of a visual target is calculated as the mean position of all stimuli in close proximity to the target stimulus. We predicted that perceptual alterations that shifted the direction of centroid extraction would also shift the direction of line bisection errors. Our findings confirmed this prediction and support the idea that both perceptual and attentional factors contribute to the pseudoneglect effect.     

A hierarchical bayesian statistical framework for response time distributions

This paper provides a statistical framework for estimating higher-order characteristics of the response time distribution, such as the scale (variability) and shape. Consideration of these higher order characteristics often provides for more rigorous theory development in cognitive and perceptual psychology (e.g., Luce, 1986). RT distribution for a single participant depends on certain participant characteristics, which in turn can be thought of as arising from a distribution of latent variables. The present work focuses on the three-parameter Weibull distribution, with parameters for shape, scale, and shift (initial value). Bayesian estimation in a hierarchical framework is conceptually straightforward. Parameter estimates, both for participant quantities and population parameters, are obtained through Markov Chain Monte Carlo methods. The methods are illustrated with an application to response time data in an absolute identification task. The behavior of the Bayes estimates are compared to maximum likelihood (ML) estimates through Monte Carlo simulations. For small sample size, there is an occasional tendency for the ML estimates to be unreasonably extreme. In contrast, by borrowing strength across participants, Bayes estimation shrinks extreme estimates. The results are that the Bayes estimators are more accurate than the corresponding ML estimators.We are grateful to Michael Stadler who allowed us use of his data. This research is supported by (a) National Science Foundation Grant SES-0095919 to J. Rouder, D. Sun, and P. Speckman, (b) University of Missouri Research Board Grant 00-77 to J. Rouder, (c) National Science Foundation grant DMS-9972598 to Sun and Speckman, and (d) a grant from the Missouri Department of Conservation to D. Sun.     

Automatic optimism: Biased use of base rate information for positive and negative events

People generally judge that positive events will occur in their lives and negative events will not, even when both events have the same objective likelihood to occur. In four studies, we examined the possibility that this optimistic bias is the result of people’s automatic affective reactions to future events. Studies 1 and 2 demonstrate, in two different contexts, that people are consistently optimistic in their predictions, despite identical base rates for positive and negative events. In Study 2, optimistic bias was not influenced by incentives for motivated reasoning or rewards for accuracy, suggesting that bias was the result of automatic processes. Studies 3 and 4 showed that optimistic bias was more pronounced when predictions were speeded and when participants made predictions after exposure to affectively valenced words. Together, these findings suggest that people optimistically interpret base rates and that this optimism is due to an effortless affective process.     

Is there really an omission effect?

The omission effect, first described by Spranca and colleagues (Spranca, Minsk, & Baron, 1991), has since been extensively studied and repeatedly confirmed (Cushman, Murray, Gordon-McKeon, Wharton, & Greene, 2012). All else being equal, most people judge it to be morally worse to actively bring about a negative event than to passively allow that event to happen. In this paper, we provide new experimental data that challenges previous studies of the omission effect both methodologically and philosophically. We argue that previous studies have failed to control for the equivalence of rules that are violated by actions and omissions. Once equivalent norms are introduced, our results show that the omission effect is eliminated, even if the negative outcome of the behavior is foreseen and intended by the agent. We show that the omission effect does not constitute a basic, moral disposition but occurs exclusively in complex moral situations. Building on these empirical results, we cast doubt onto two influential explanations of the omission effect, the Causal Relevance Hypothesis and the Overgeneralization Hypothesis, and provide a novel explanation of the phenomenon. Furthermore, we discuss various ramifications of the interplay between our understanding of omissions and legal systems.     

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.     

Predictors of children's prosocial lie-telling: Motivation, socialization variables, and moral understanding

Children tell prosocial lies for self- and other-oriented reasons. However, it is unclear how motivational and socialization factors affect their lying. Furthermore, it is unclear whether children’s moral understanding and evaluations of prosocial lie scenarios (including perceptions of vignette characters’ feelings) predict their actual prosocial behaviors. These were explored in two studies. In Study 1, 72 children (36 second graders and 36 fourth graders) participated in a disappointing gift paradigm in either a high-cost condition (lost a good gift for a disappointing one) or a low-cost condition (received a disappointing gift). More children lied in the low-cost condition (94%) than in the high-cost condition (72%), with no age difference. In Study 2, 117 children (42 preschoolers, 41 early elementary school age, and 34 late elementary school age) participated in either a high- or low-cost disappointing gift paradigm and responded to prosocial vignette scenarios. Parents reported on their parenting practices and family emotional expressivity. Again, more children lied in the low-cost condition (68%) than in the high-cost condition (40%); however, there was an age effect among children in the high-cost condition. Preschoolers were less likely than older children to lie when there was a high personal cost. In addition, compared with truth-tellers, prosocial liars had parents who were more authoritative but expressed less positive emotion within the family. Finally, there was an interaction between children’s prosocial lie-telling behavior and their evaluations of the protagonist’s and recipient’s feelings. Findings contribute to understanding the trajectory of children’s prosocial lie-telling, their reasons for telling such lies, and their knowledge about interpersonal communication.     

关于成本沉没效应的实验研究

运用IAT 技术,通过实验,证明“损失厌恶”具有内隐性,“损失厌恶”存在内隐偏差,并且二者具有很强的稳定性,即不受当前情绪及刺激材料性质等的影响,为进一步认识成本沉没效应等现象的本质特点提供了证据。     

Self-selection biases in correlational studies based on questionnaires

It is commonly held that even where questionnaire response is poor, correlational studies are affected only by loss of degrees of freedom or precision. We show that this supposition is not true. If the decision to respond is correlated with a substantive variable of interest, then regression or analysis of variance methods based upon the questionnaire results may be adversely affected by self-selection bias. Moreover such bias may arise even where response is 100%. The problem in both cases arises where selection information is passed to the score indirectly via the disturbance or individual effects, rather than entirely via the observable explanatory variables. We suggest tests for the ensuing self-selection bias and possible ways of handling the ensuing problems of inference.The author would like to thank members of the Psychology Department at UWA for references on the illustrative example, namely the effects of sex-stereotyping on achievements in mathematics; thanks are also due to the anonymous reviewers whose comments resulted in material improvements to the paper.