Mechanisms of moral disengagement and their differential use by right‐wing authoritarianism and social dominance orientation in support of war

Right‐wing authoritarianism (RWA) and social dominance orientation (SDO) are associated with the approval of war as a political intervention [McFarland, 2005]. We examined whether the effects of RWA and SDO on war support are mediated by moral‐disengagement mechanisms [i.e., responsibility reduction, moral justification, minimizing consequences, and dehumanizing–blaming victims; Bandura, 1999] and whether the ideologies use the mechanisms differently. Our data were consistent with the possibility that minimizing consequences (Study 1) and moral justification (Study 2) mediate the effects of RWA and SDO on approval of war. Both ideologies were positively associated with all moral‐disengagement mechanism though more strongly so for RWA. Comparisons within ideologies suggest that RWA was most strongly associated with moral justification and SDO was most strongly associated with dehumanizing–blaming victims. We discuss implications and limitations. Aggr. Behav. 36:238–250, 2010. © 2010 Wiley‐Liss, Inc.     

An improved stochastic EM algorithm for large-scale full-information item factor analysis

In this paper, we explore the use of the stochastic EM algorithm (Celeux & Diebolt (1985) Computational Statistics Quarterly, 2, 73) for large-scale full-information item factor analysis. Innovations have been made on its implementation, including an adaptive-rejection-based Gibbs sampler for the stochastic E step, a proximal gradient descent algorithm for the optimization in the M step, and diagnostic procedures for determining the burn-in size and the stopping of the algorithm. These developments are based on the theoretical results of Nielsen (2000, Bernoulli, 6, 457), as well as advanced sampling and optimization techniques. The proposed algorithm is computationally efficient and virtually tuning-free, making it scalable to large-scale data with many latent traits (e.g. more than five latent traits) and easy to use for practitioners. Standard errors of parameter estimation are also obtained based on the missing-information identity (Louis, 1982, Journal of the Royal Statistical Society, Series B, 44, 226). The performance of the algorithm is evaluated through simulation studies and an application to the analysis of the IPIP-NEO personality inventory. Extensions of the proposed algorithm to other latent variable models are discussed.     

Nonverbal signals speak up: Association between perceptual nonverbal dominance and emotional intelligence

Emotional communication uses verbal and nonverbal means. In case of conflicting signals, nonverbal information is assumed to have a stronger impact. It is unclear, however, whether perceptual nonverbal dominance varies between individuals and whether it is linked to emotional intelligence. Using audiovisual stimulus material comprising verbal and nonverbal emotional cues that were varied independently, perceptual nonverbal dominance profiles and their relations to emotional intelligence were examined. Nonverbal dominance was found in every participant, ranging from 55 to 100%. Moreover, emotional intelligence, particularly the ability to understand emotions, correlated positively with nonverbal dominance. Furthermore, higher overall emotional intelligence as well as a higher ability to understand emotions were linked to smaller reaction time differences between emotionally incongruent and congruent stimuli. The association between perceptual nonverbal dominance and emotional intelligence, and more specifically the ability to understand emotions, might reflect an adaptive process driven by the experience of higher authenticity in nonverbal cues.     

From eye movement to cognition: Toward a general framework of inference comment on Liechty et al., 2003

Liechty, Pieters & Wedel (2003) developed a hidden Markov Model (HMM) to identify the states of an attentional process in an advertisement viewing task. This work is significant because it demonstrates the benefits of stochastic modeling and Bayesian estimation in making inferences about cognitive processes based on eye movement data. One limitation of the proposed approach is that attention is conceptualized as an autonomous random process that is affected neither by the overall layout of the stimulus nor by the visual information perceived during the current fixation. An alternative model based on the input-output hidden Markov model (IOHMM; Bengio, 1999) is suggested as an extension of the HMM. The need for further studies that validate the HMM classification results is also discussed.     

Who Gets What and Why: Determinants of Social Allocations

Four experiments were conducted to explore how people allocate social resources between meritorious and needy parties. As predicted, participants with high social dominance orientation (SDO), who prefer hierarchical relationships in society, favored meritorious parties. In contrast, participants with low SDO, who value social equality, favored needy parties. Participants appealed to merit- or need-based ideologies to justify their allocations. Apparently, the notion of fairness denotes merit-based reasoning to people with high SDO, whereas it denotes need-based reasoning for those with low SDO. Among the participants, merit- and need-based ideologies were not simply post hoc rationalizations, but rather mediated the allocation decisions. Implications for the study of ideology and for theories of distributive justice are discussed.     

Strategies of semantic categorization in the cerebral hemispheres

Strategies of semantic categorization in intact cerebral hemispheres were studied in two experiments by presenting names of typical and atypical category instances to the left visual field (LVF) (right hemisphere) or to the right visual field (RVF) (left hemisphere). The results revealed that the typicality of instances had a large effect on categorization times in the LVF in both experiments, suggesting that the right hemisphere relies strongly on a holistic, similarity-based comparison strategy. In Experiment 1, the typicality effect was weaker in the RVF than in the LVF. In Experiment 2, a typicality effect in the RVF was observed for the "four-footed animal" category but not for the "bird" category. The hypothesis that the left hemisphere employs a strategy based on defining or necessary features is not supported by the observed typicality effect in the "four-footed animal" category. Instead, it is suggested that the left hemisphere may be able to categorize on the basis of prestored instance-category knowledge. When such knowledge is not available (e.g., as for four-footed animals), a similarity-based comparison strategy is employed by the left hemisphere.     

Reward dominance among children with disruptive behavior disorders

Investigated was a deficit in avoidance learning in situations of competing rewards and punishments in boys with behavior disorders. This learning style has been found to differentiate adults with psychopathy and controls and has been labeled reward dominance. The present study investigated the reward dominance paradigm in a group of 9- to 13-year-old boys with disruptive behavior disorders (N=21) and a normal control group (N=22). Subjects played four computer games programmed to provide the subject with a steadily decreasing ratio of rewards to punishments. Although the groups did not differ initially on the number of trials played, significant group differences emerged when measures of anxiety were included in the analysis. That is, boys with disruptive behavior disorders played more trials (reward dominance) only when the effect of anxiety was controlled. These findings are consistent with Gray's biobehavioral theory of personality and are also consistent with research indicating that anxiety is an important marker for a distinct subgroup of children with behavior problems.     

A note on dual scaling of successive categories data

Three methods for dual scaling of successive categories data are formulated. They are mathematically equivalent, and provide an appropriate technique to determine stimulus values and category boundaries on a joint scale, a problem that Nishisato's method (1980a) failed to handle. This study was supported by the Natural Sciences and Engineering Research Council of Canada (Grant No. A7942) to S. Nishisato. Comments on the earlier draft from anonymous reviewers were most helpful and much appreciated.     

Local homogeneity in latent trait models. A characterization of the homogeneous monotone irt model

The stochastic subject formulation of latent trait models contends that, within a given subject, the event of obtaining a certain response pattern may be probabilistic. Ordinary latent trait models do not imply that these within-subject probabilities are identical to the conditional probabilities specified by the model. The latter condition is called local homogeneity. It is shown that local homgeneity is equivalent to subpopulation invariance of the model. In case of the monotone IRT model, local homogeneity implies absence of item bias, absence of item specific traits, and the possibility to join overlapping subtests. The following characterization theorem is proved: the homogeneous monotone IRT model holds for a finite or countable item pool if and only if the pool is experimentally independent and pairwise nonnegative association holds in every positive subpopulation.This research was supported by the Dutch Interuniversity Graduate School of Psychometrics and Sociometrics. The authors wish to thank two reviewers for their thorough comments.     

EM and beyond

The basic theme of the EM algorithm, to repeatedly use complete-data methods to solve incomplete data problems, is also a theme of several more recent statistical techniques. These techniques—multiple imputation, data augmentation, stochastic relaxation, and sampling importance resampling—combine simulation techniques with complete-data methods to attack problems that are difficult or impossible for EM.A preliminary version of this article was the Keynote Address at the 1987 European Meeting of the Psychometric Society June 24–26, 1987 in Enschede, The Netherlands. The author wishes to thank the editor and reviewers for helpful comments.