The Annenberg School for Communication, The University of Pennsylvania, Philadelphia, PA 19104.
The Annenberg School for Communication, The University of Pennsylvania, Philadelphia, PA 19104;
Proc Natl Acad Sci U S A. 2018 Sep 25;115(39):9714-9719. doi: 10.1073/pnas.1722664115. Epub 2018 Sep 4.
Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon's Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects' interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.
由于动机推理的影响，大众经常错误地理解重要的科学信息，动机推理是指人们错误地解释数据以符合他们的政治和心理偏见。在气候变化的情况下，一些人被发现会系统地曲解与气候科学家原意相冲突的气候数据。虽然先前的研究试图通过两党沟通网络来减少动机推理，但这些网络也被发现加剧了偏见。流行的理论认为，两党网络通过让人们接触到对立的观点来放大偏见。这些理论与集体智慧研究相冲突，后者表明在社交网络中交流信仰可以促进社会学习，从而提高个人和群体的判断。然而，先前的集体智慧实验几乎完全依赖于不涉及动机推理的中性问题。我们使用亚马逊的 Mechanical Turk 进行了一项在线实验，以测试两党社交网络如何影响参与者对美国宇航局发布的气候变化信息的解读。在这里，我们表明，在结构化的两党社交网络中接触对立观点，极大地提高了保守派和自由派参与者判断的准确性，消除了信仰两极化。然而，我们还发现，由于党派偏见的存在，社会学习可能会减少，信仰两极化也可能会持续。我们发现，在交流过程中提高党派意识的显著性，无论是通过暴露于政党标志还是通过暴露于网络伙伴的政治身份，都可以显著减少社会学习。
