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Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of ‘givers’ and ‘receivers’ and discuss how the behavior of the same individual changes depending on his role in the handover. [ABSTRACT FROM AUTHOR]

Current literature presents conflicting findings concerning the effect of religiosity on attitudes towards redistribution. This paper attempts to reconcile these findings by arguing that the belief and social behavior dimensions of religiosity affect support for redistribution via different mechanisms, and that these effects are moderated by state welfare generosity. Using multilevel path analysis models on data from the World Values Survey, we show that the effect of the religious belief on attitudes towards redistribution is mediated by competing personal orientations—prosocial values and conservative identification—while the religious social behavior dimension significantly decreases support for redistribution via increased levels of happiness. Lower levels of welfare generosity increase the positive effect of prosocial orientations and weaken the negative effect conservative identification, leading to positive or null indirect effect of religiosity. These findings show the importance of taking into account the multiple dimensions of religiosity and institutional context when studying the relationship between religion and redistribution attitudes. [ABSTRACT FROM AUTHOR]

Our personalities (who we are) influence our social relationships (how we relate to people around us), and our social relationships influence our personalities. However, little is known about the specific processes underlying the complex interplay of personality and social relationships. According to the PERSOC framework, the identification of underlying social interaction processes promotes the understanding of how personality and social relationships are expressed, develop, and influence each other over time. The aim of the present paper is twofold: First, we outline and discuss four methodological challenges that arise when trying to empirically realize a process approach to the personality-relationship interplay. Second, we describe two data sets that are designed to meet these challenges and that are open for collaborative investigations: a laboratory-based process approach (Personality Interaction Laboratory Study; PILS) and a field-based process approach (CONNECT). We provide detailed information on the samples (two student samples; PILS: N = 311; CONNECT: N = 131), procedures (longitudinal and multimethodological), and measures (personality and social relationships, appearance and behavior, interpersonal perceptions), for which we present descriptive information, reliabilities, and intercorrelations. We summarize how these studies’ designs targeted the introduced methodological challenges, discuss the advantages and limitations of laboratory- and field-based process approaches, and call for their combination. We close by outlining an open research policy, aimed at accelerated collaborative efforts to further open the process black box, ultimately leading to a better understanding of the expression, development, and complex interplay of personality and social relationships. [ABSTRACT FROM AUTHOR]

Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks. In the paper, by using a type of self-loop rescaling strategy, we introduced a set of global modularity functions and a set of local modularity functions for community detection in networks, which are optimized by a kind of the self-consistent method. We carefully compared and analyzed the behaviors of the modularity-based methods in community detection, and confirmed the superiority of the local modularity for detecting community structures on large-size and heterogeneous networks. The local modularity can more quickly eliminate the first-type limit of modularity, and can eliminate or alleviate the second-type limit of modularity in networks, because of the use of the local information in networks. Moreover, we tested the methods in real networks. Finally, we expect the research can provide useful insight into the problem of community detection in complex networks. [ABSTRACT FROM AUTHOR]

This paper addresses the experimental trade-off between the exercise of control over the actions of the experimental participants and the potential to provide understanding about human behavior. Control is a requirement of the experimental method to produce pertinent and intelligible results for scientific inquiry. But the more control is exercised the more the experimental results are the outcome of economists' actions. Economic experiments must therefore achieve a difficult balance. They must elicit intelligible behavior while ensuring that the actions of those taking part in the experiment are not determined by the design set-up and the rules of the experiment. The paper puts forward criteria for the analysis of the level of participation of experimental subjects in economics experiments and applies them to several experiments to illustrate the relevance of assessing the agency of experimental participants when deriving inferences about human behavior. [ABSTRACT FROM AUTHOR]

Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model. [ABSTRACT FROM AUTHOR]