Social identity,attribution, and emotion: Comparisons of Americans,Korean Americans,and Koreans

The social identity of another person, in addition to the social identity of self, can be an important factor affecting the types of attribution judgments and emotions that individuals indicate for the other person. In April 2007, the perpetrator of the shooting incident on the Virginia Tech University campus was identified as a person who emigrated to the USA from Korea at a young age. The current study compared non‐Korean Americans, Korean Americans, Koreans in the USA, and Koreans in Korea in terms of their attributions and emotions concerning the perpetrator and the shooting incident. Participants were asked to indicate (1) the extent to which they attributed the cause of the incident to either American society or the perpetrator, (2) their emotions (e.g., upset), and (3) the extent to which they categorized the perpetrator as an American, a Korean American, or a Korean. The results indicated that non‐Korean Americans were most likely to attribute the cause of the incident to the perpetrator as opposed to American society. Non‐Korean Americans, Korean Americans, and Koreans in the United States had more negative emotions (e.g., unhappy, sad, and upset) about the incident than Koreans in Korea did. The results also indicated that individuals differed in their attributions and emotions depending on how they categorized the perpetrator. For example, categorizing the perpetrator as being a Korean was positively related to Americans’ tendency to hold the perpetrator responsible, while categorizing the perpetrator as being an American was negatively related to the tendency to hold the perpetrator responsible among Koreans in Korea. The findings may imply that social identity theory, intergroup emotion theory, and cultural orientations (e.g., individualism and collectivism) can provide insights into people's reactions to a tragic incident.     

Abnormal diurnal pattern of cortisol secretion in patients after aneurysmal subarachnoid hemorrhage

Substantial evidence suggests that impairment of the hypothalamus?pituitary system can occur following an aneurysmal subarachnoid hemorrhage (aSAH). Given that the diurnal cortisol rhythm is primarily controlled by the hypothalamus?pituitary system, this study examined whether changes in diurnal cortisol rhythm occurred after aSAH. Cortisol concentrations were measured in the saliva samples collected from patients after aSAH and other types of cerebral hemorrhage (non-aSAH) in the post-awakening period and at night (21:00?h), and the cortisol awakening response (CAR) and diurnal cortisol decline were determined. The area under the cortisol curve from immediately after to 45?min after awakening (CARauc) in the aSAH patient group was comparable to that in the non-aSAH or healthy control groups. However, an obvious cortisol peak was not found after the awakening period, and the morning/nighttime cortisol ratio in the aSAH patient group was significantly lower than that in other examined groups due to higher nighttime cortisol concentrations. In aSAH patients, the CARauc and nighttime cortisol concentrations were negatively correlated with the Fisher CT grade. These results indicate that the diurnal cortisol rhythm is not regulated normally after aSAH, and cortisol secretory activity decreases as the volume of subarachnoid bleeding increases. Our findings will be helpful to understand altered hypothalamus?pituitary?adrenal axis function after aSAH.     

Use of the estimated intraclass correlation for correcting differences in effect size by level

In a meta-analysis of intervention or group comparison studies, researchers often encounter the circumstance in which the standardized mean differences (d-effect sizes) are computed at multiple levels (e.g., individual vs. cluster). Cluster-level d-effect sizes may be inflated and, thus, may need to be corrected using the intraclass correlation (ICC) before being combined with individual-level d-effect sizes. The ICC value, however, is seldom reported in primary studies and, thus, may need to be computed from other sources. This article proposes a method for estimating the ICC value from the reported standard deviations within a particular meta-analysis (i.e., estimated ICC) when an appropriate default ICC value (Hedges, 2009b) is unavailable. A series of simulations provided evidence that the proposed method yields an accurate and precise estimated ICC value, which can then be used for correct estimation of a d-effect size. The effects of other pertinent factors (e.g., number of studies) were also examined, followed by discussion of related limitations and future research in this area.     

Gender and the self-presentation of academic achievement

Two experiments were conducted in which first-year male and female subjects (75% white; 25% minorities) at a highly selective liberal arts college predicted their first-semester grade point averages (GPAs). The first experiment varied whether predictions were given publicly or privately when an incentive for accuracy was either present or absent. Whereas the actual GPAs obtained by women did not differ from those obtained by men, women predicted that they would receive lower GPAs than men predicted, but only in the public condition. Whether incentive was present or absent did not affect predictions. The second experiment varied whether predictions were given publicly or privately as well as the perceived achievement of the experimenter. Once again, women's actual GPAs did not differ from men's. However, women predicted that they would receive lower GPAs than men predicted, but only when their estimates were given publicly to a low-ability experimenter. Although previous research has concluded that gender differences on performance estimates is attributable to a lack of confidence among women, these results suggest that the gender difference may be (at least partly) attributable to self-presentational style. It appears that women in this study adopted a modest self-presentational style that was motivated by attempts to protect the self-esteem of the experimenter.We are especially grateful to Rob Abel, David McGill, and Melinda Varn for their help in data collection.     

Effect of causal structure on category construction

In four experiments, the question of how the causal structure of features affects the creation of new categories was examined. Features of exemplars to be sorted were related in a single causal chain (causal chain), were caused by the same factor (common cause), or caused the same effect (common effect). The results showed that people are more likely to rely on common-cause or common-effect background knowledge than on causal-chain background knowledge in category construction. Such preferences suggest that the common-cause or the common-effect structures are considered more natural conceptual structures.     

Feature centrality: Naming versus imagining

Being white is central to whether we call an animal a "polar bear," but it is fairly peripheral to our concept of what a polar bear is. We propose that a feature is central to category naming in proportion to the feature's category validity--the probability of the feature, given the category. In contrast, a feature is conceptually central in a representation of the object to the extent that the feature is depended on by other features. Further, we propose that naming and conceptual centrality are more likely to disagree for features that hold at more specific levels (such as is white, which holds only for the specific category of polar bear) than for features that hold at intermediate levels of abstraction (such as has claws, which holds for all bears). In support of these hypotheses, we report evidence that increasing the abstractness of category features has a greater effect on judgments of conceptual centrality than on judgments of name centrality and that other category features depend more on intermediate-level category features than on specific ones.     

Comparison of decision learning models using the generalization criterion method

It is a hallmark of a good model to make accurate a priori predictions to new conditions ( Busemeyer & Wang, 2000 ). This study compared 8 decision learning models with respect to their generalizability. Participants performed 2 tasks (the Iowa Gambling Task and the Soochow Gambling Task), and each model made a priori predictions by estimating the parameters for each participant from 1 task and using those same parameters to predict on the other task. Three methods were used to evaluate the models at the individual level of analysis. The first method used a post hoc fit criterion, the second method used a generalization criterion for short-term predictions, and the third method again used a generalization criterion for long-term predictions. The results suggest that the models with the prospect utility function can make generalizable predictions to new conditions, and different learning models are needed for making short-versus long-term predictions on simple gambling tasks.     

The effects of Web site structure: the role of personal difference.

This study examined the effects of Web site structures in terms of advertising effectiveness- memory, attitude, and behavioral intentions. The primary research question for this study is, What type of Web site (Web ad) structure is most effective? In the pilot study, we tested the difference between two Web site structures, linear and interactive, in terms of traditional advertising effectiveness. Results from the pilot study did not support our research expectations. However, differences in terms of memory were noted between the two structures. After re-creating the Web site based on subjects' comments, in the final experiment, we examined the differences between the two structures and the moderating role of personality difference on the effects of Web site structure. The results confirm that participants' attitude, memory, and behavioral intentions were affected differently by the different Web site structures. However, some research hypotheses were not supported by the current data.     

Data-driven type checking in open domain question answering

Many open domain question answering systems answer questions by first harvesting a large number of candidate answers, and then picking the most promising one from the list. One criterion for this answer selection is type checking: deciding whether the candidate answer is of the semantic type expected by the question. We define a general strategy for building redundancy-based type checkers, built around the notions of comparison set and scoring method, where the former provide a set of potential answer types and the latter are meant to capture the relation between a candidate answer and an answer type. Our focus is on scoring methods. We discuss nine such methods, provide a detailed experimental comparison and analysis of these methods, and find that the best performing scoring method performs at the same level as knowledge-intensive methods, although our experiments do not reveal a clear-cut answer on the question whether any of the scoring methods we consider should be preferred over the others.