ObjectiveThe extensive use of two diverging personality taxonomies (the Big Five and HEXACO models) in contemporary research creates a need for understanding how traits connect to each other across taxonomies. Previous research has approached this at both a highly general (domain‐) level as well as at a highly specific (facet‐) level. The present report is the first to use the intermediate (aspect‐) level of the Big Five Aspect Scales (BFAS) to understand the connections between the two models.MethodWe explored these associations in a meta‐analysis of four samples drawn from three countries (total N = 1,586).ResultsWe observed that each HEXACO domain correlated ≥|0.51| with one or more BFAS aspects. Half of the aspects were more strongly associated with HEXACO facets than with HEXACO domains, sometimes markedly so.ConclusionAlthough many domains, aspects, and facets are similarly represented across the two models, this was not always the case. Researchers seeking to use one model to extend findings built primarily off the other should carefully consider how well represented their traits of interest are in the other assessment. Psychology instructors are encouraged to use the BFAS to illustrate the subtler distinctions between the Big Five and HEXACO models.

Using the HEXACO Model of Personality, we explored two kinds of ideal partner preferences regarding personality traits, namely, to what extent people prefer partners similar to themselves (similarity preference) and to what extent people prefer partners with more desirable trait levels than themselves (aspirational assortative preference). We conducted five studies (overall N > 900) across four countries (China, Denmark, Germany, and the USA), looking at both HEXACO factors and facets, using both self‐report questionnaires and real‐life data (personal profiles from a dating website), and comprising both student and more heterogeneous samples. The results provided support for both kinds of ideal partner preferences, with important differences across traits. Specifically, similarity preference was supported by all studies concerning all HEXACO traits, and aspirational assortative preference was supported by all four self‐report studies (though not the dating website study) concerning all HEXACO traits except for Openness to Experience. Concerning differences in preferences among the HEXACO traits, similarity preference was particularly pronounced for Honesty–Humility and Openness to Experience, moderate for Agreeableness and Conscientiousness, and less pronounced for Emotionality and Extraversion. Aspirational assortative preference, by contrast, was particularly pronounced for Emotionality, Extraversion, and Agreeableness, moderate for Honesty–Humility, and inconsistent for Conscientiousness. © 2018 European Association of Personality Psychology

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Abstract. In this study, we investigated the interaction effects between honesty-humility and two contextual perception variables (perceptions of organizational politics and perceptions of interactional justice) on two dimensions of job performance (task performance and organizational citizenship behavior). In a multiple rater design, we dissociated the assessments of the contextual perception variables (rated by target employees), personality traits (rated by colleagues), and job performance (rated by supervisors) from each other. We expected employees lower in honesty-humility to adapt their behavior according to the perceived context, whereas employees higher in honesty-humility were expected to perform equally well irrespective of the perceived environment. Results supported the hypothesized interactions in general.

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This paper focuses on the social function of painful experience as revealed by recent studies on social decision-making. Observing others suffering from physical pain evokes empathic reactions that can lead to prosocial behavior (e.g., helping others at a cost to oneself), which might be regarded as the social value of pain derived from evolution. Feelings of guilt may also be elicited when one takes responsibility for another’s pain. These social emotions play a significant role in various cognitive processes and may affect behavioral preferences. In addition, the influence of others’ pain on decision-making is highly sensitive to social context. Combining neuroimaging techniques with a novel decision paradigm, we found that when asking participants to trade-off personal benefits against providing help to other people, verbally describing the causal relationship between their decision and other people’s pain (i.e., framing) significantly changed participants’ preferences. This social framing effect was associated with neural activation in the temporoparietal junction (TPJ), which is a brain area that is important in social cognition and in social emotions. Further, transcranial direct current stimulation (tDCS) on this region successfully modulated the magnitude of the social framing effect. These findings add to the knowledge about the role of perception of others’ pain in our social life.

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In the field of process mining, it is worth noting that process mining techniques assume that the resulting event logs can not only continuously record the occurrence of events but also contain all event data. However, like in IoT systems, data transmission may fail due to weak signal or resource competition, which causes the company's information system to be unable to keep a complete event log. Based on a incomplete event log, the process model obtained by using existing process mining technologies is deviated from actual business process to a certain degree. In this paper, we propose a method for repairing missing activities based on succession relation of activities from event logs. We use an activity relation matrix to represent the event log and cluster it. The number of traces in the cluster is used as a measure of similarity calculation between incomplete traces and cluster results. Parallel activities in selecting pre-occurrence and post-occurrence activities of missing activities from incomplete traces are considered. Experimental results on real-life event logs show that our approach performs better than previous method in repairing missing activities.