We report the results of an intervention that targeted anti-Roma sentiment in Hungary using an online perspective-taking game. We evaluated the impact of this intervention using a randomized experiment in which a sample of young adults played this perspective-taking game, or an unrelated online game. Participation in the perspective-taking game markedly reduced prejudice, with an effect-size equivalent to half the difference between voters of the far-right and the center-right party. The effects persisted for at least a month, and, as a byproduct, the intervention also reduced antipathy toward refugees, another stigmatized group in Hungary, and decreased vote intentions for Hungary's overtly racist, far-right party by 10%. Our study offers a proof-of-concept for a general class of interventions that could be adapted to different settings and implemented at low costs.

a b s t r a c tLiving in large groups and maintaining extensive social relationships, as humans do, requires special social capabilities. Past research has shown that social cognitive abilities predict people's social network size. To extend these findings we explored the role of a social emotional ability, and investigated how empathic abilities shape people's social network. In line with the social brain hypothesis the findings show that dispositional empathic abilities (IRI), and empathic concern specifically, predict how many close relationships people maintain. The study also found that emphatic abilities are strategically used in people's social network, with more empathy exercised in the support group with closer relationships. The findings further demonstrate the social function of empathy and highlight the importance of understanding empathy in terms of its strategic exercise among various social relationships.

Most research on threat documents its negative consequences. Similarly, most research on intergroup contexts has emphasized their negative behavioral effects. Drawing on the Meaning Maintenance Model and recent perspectives on the potential for positivity in intergroup conflict, we predicted that meaning threat can produce both antisocial and prosocial responses to intergroup conflict, depending on people's preexisting meaning frameworks. Studies 1 and 2 demonstrated that under meaning threat, low ingroup glorifiers strengthened their support for peaceful conflict resolution, whereas high ingroup glorifiers strengthened their support for military-based conflict resolution. In the context of the Israel-Palestinian conflict, Study 3 found that low glorification was associated with greater support for peace during "hot" (but not "cold") conflict, because hot conflict reduced their meaning in life. These findings are consistent with the notion that when meaning is threatened, people affirm their preexisting values-whether prosocial or antisocial-even in the context of intergroup conflict.

In this work we present a new thinning scheme for reducing the noise sensitivity of 3D thinning algorithms. It uses iteration-by-iteration smoothing that removes some border points that are considered as extremities. The proposed smoothing algorithm is composed of two parallel topology preserving reduction operators. An efficient implementation of our algorithm is sketched and its topological correctness for (26, 6) pictures is proved.

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