人类修正矛盾信念时更倾向用解释驱动的调整，而非最小化修改。
How Do People Revise Inconsistent Beliefs? Examining Belief Revision in Humans with User Studies
- 通过用户实验发现，人们偏好基于解释的信念修正方式。
- 即使结果非最小化，人们仍坚持用解释来调整信念系统。
- 适合开发能理解人类推理逻辑的AI交互系统。
理解人类如何根据新信息修正信念，对开发能有效模拟并契合人类推理的AI系统至关重要。尽管理论上的信念修正框架依赖于一套原则来规定操作方式，但认知心理学的实证研究显示，当面对冲突信息时，人们可能采用不同模式。本文通过三项综合用户研究发现，人们始终倾向于基于解释的信念修正——即由解释引导的调整，其结果未必符合经典信念变化理论。实验系统性地考察了在提供解释或自主生成解释的情况下，人们如何修正信念不一致，揭示出在多种情境下对看似非最小化修正的稳定偏好。这些发现对旨在模拟人类推理或与人类互动的AI系统具有启示意义，提示应支持基于解释、可能非最小化的信念修正算子，以更好地匹配人类认知过程。
原文摘要 · Abstract (English)
Understanding how humans revise their beliefs in light of new information is crucial for developing AI systems which can effectively model, and thus align with, human reasoning. While theoretical belief revision frameworks rely on a set of principles that establish how these operations are performed, empirical evidence from cognitive psychology suggests that people may follow different patterns when presented with conflicting information. In this paper, we present three comprehensive user studies showing that people consistently prefer explanation-based revisions, i.e., those which are guided by explanations, that result in changes to their belief systems that are not necessarily captured by classical belief change theory. Our experiments systematically investigate how people revise their beliefs with explanations for inconsistencies, whether they are provided with them or left to formulate them themselves, demonstrating a robust preference for what may seem non-minimal revisions across different types of scenarios. These findings have implications for AI systems designed to model human reasoning or interact with humans, suggesting that such systems should accommodate explanation-based, potentially non-minimal belief revision operators to better align with human cognitive processes.
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