Original
Beyond Belief Change: The Persuasive Returns of Targeting Attitude–Relevant Beliefs
Abstract
A persistent puzzle in the study of public opinion is why political information often produces minimal attitude change despite reliably influencing beliefs. We argue that this duality reflects belief relevance—the extent to which specific beliefs bear on attitudes. Using conversations with large language models (LLMs), we elicit deeply held issue attitudes and the “focal beliefs” people use to justify those attitudes. We then randomly assign participants to receive an LLM-generated counterargument targeting either their focal belief, an issue-relevant but unmentioned belief (“distal belief”), or a placebo. In experiments with two large online convenience samples, counterarguments targeting the aforementioned beliefs successfully decrease belief strength, with effects persisting after 10 days. More importantly, focal belief counterarguments produce larger and more durable attitude change than distal counterarguments. These findings suggest that political information can successfully shift political attitudes and provide new evidence for the role of belief relevance in persuasion.
中文
超越信念改变：针对态度相关信念的说服收益
摘要
公共舆论研究中一个长期存在的谜题是：政治信息为何在可靠地影响信念的同时，却往往只带来极小的态度改变。我们认为，这种二元性反映了信念相关性——即特定信念与态度之间的关联程度。借助与大语言模型（LLMs）的对话，我们引出人们深持的议题态度以及他们用来为这些态度辩护的“焦点信念”。随后，我们将参与者随机分配到接收由LLM生成的反驳论证的条件中，这些论证分别针对其焦点信念、一个与议题相关但未被提及的信念（“远端信念”），或安慰剂。在两项使用大型在线便利样本的实验中，针对上述信念的反驳论证成功降低了信念强度，且效应在10天后仍然存在。更重要的是，针对焦点信念的反驳论证比针对远端信念的反驳论证产生了更大且更持久的态度改变。这些发现表明，政治信息能够成功改变政治态度，并为信念相关性在说服中的作用提供了新证据。
关键词
公共舆论、政治说服、态度改变、信念相关性、焦点信念、远端信念、大语言模型、实验研究