Manipulating attention facilitates cooperation.

Claire Lugrin, Arkady Konovalov, Christian C Ruff
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Abstract

Cooperation is essential for human societies, but not all individuals cooperate to the same degree. This is typically attributed to individual motives - for example, to be prosocial or to avoid risks. Here, we investigate whether cooperative behavior can, in addition, reflect what people pay attention to and whether cooperation may therefore be influenced by manipulations that direct attention. We first analyze the attentional patterns of participants playing one-shot Prisoner's Dilemma games and find that choices indeed relate systematically to attention to specific social outcomes, as well as to individual eye movement patterns reflecting attentional strategies. To test for the causal impact of attention independently of participants' prosocial and risk attitudes, we manipulate the task display and find that cooperation is enhanced when displays facilitate attention to others' outcomes. Machine learning classifiers trained on these attentional patterns confirm that attentional strategies measured using eye-tracking can accurately predict cooperation out-of-sample. Our findings demonstrate that theories of cooperation can benefit from incorporating attention and that attentional interventions can improve cooperative outcomes.

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合作对人类社会至关重要,但并非所有个体的合作程度都相同。这通常归因于个人动机--例如,亲社会或规避风险。在这里,我们将研究合作行为是否还能反映人们的注意力,以及合作是否会因此受到引导注意力的操作的影响。我们首先分析了参与者在玩一局囚徒困境游戏时的注意模式,发现选择确实与对特定社会结果的注意以及反映注意策略的个体眼球运动模式有系统的关系。为了检验注意力的因果影响是否与参与者的亲社会态度和风险态度无关,我们对任务显示进行了操作,结果发现,当任务显示促进了对他人结果的注意力时,合作就会增强。根据这些注意力模式训练的机器学习分类器证实,使用眼动追踪测量的注意力策略可以准确预测样本外的合作情况。我们的研究结果表明,合作理论可以受益于注意力,而注意力干预可以改善合作结果。
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