As Large Language Models (LLMs) are increasingly deployed as social agents and trained to produce humor and irony, a question emerges: when encountering witty AI remarks, do people interpret these as intentional communication or mere computational output? This study investigates whether people adopt the intentional stance, attributing mental states to explain behavior,toward AI during irony comprehension. Irony provides an ideal paradigm because it requires distinguishing intentional contradictions from unintended errors through effortful semantic reanalysis. We compared behavioral and neural responses to ironic statements from AI versus human sources using established ERP components: P200 reflecting early incongruity detection and P600 indexing cognitive efforts in reinterpreting incongruity as deliberate irony. Results demonstrate that people do not fully adopt the intentional stance toward AI-generated irony. Behaviorally, participants attributed incongruity to deliberate communication for both sources, though significantly less for AI than human, showing greater tendency to interpret AI incongruities as computational errors. Neural data revealed attenuated P200 and P600 effects for AI-generated irony, suggesting reduced effortful detection and reanalysis consistent with diminished attribution of communicative intent. Notably, people who perceived AI as more sincere showed larger P200 and P600 effects for AI-generated irony, suggesting that intentional stance adoption is calibrated by specific mental models of artificial agents. These findings reveal that source attribution shapes neural processing of social-communicative phenomena. Despite current LLMs' linguistic sophistication, achieving genuine social agency requires more than linguistic competence, it necessitates a shift in how humans perceive and attribute intentionality to artificial agents.

翻译：随着大型语言模型日益作为社交代理被部署，并被训练用于生成幽默与讽刺，一个重要问题随之浮现：当面对诙谐的AI言论时，人们会将其理解为有意图的交流，还是仅仅视为计算输出？本研究探讨了人们在理解讽刺时，是否会对AI采取意向立场，即通过归因心理状态来解释其行为。讽刺为此提供了一个理想范式，因为它需要通过费力的语义再分析来区分有意的矛盾与无意的错误。我们使用已确立的事件相关电位成分——反映早期不一致性检测的P200和指示将不一致性重新解释为刻意讽刺所需认知努力的P600——比较了人们对来自AI与人类来源的讽刺语句的行为和神经反应。结果表明，人们并未完全对AI生成的讽刺采取意向立场。在行为上，参与者将不一致性归因于有意的交流，尽管对AI的归因显著少于对人类，显示出更倾向于将AI的不一致性解释为计算错误。神经数据显示，对于AI生成的讽刺，P200和P600效应均减弱，这表明费力的检测和再分析减少，与对交流意图的归因减弱相一致。值得注意的是，那些认为AI更真诚的个体对AI生成的讽刺表现出更大的P200和P600效应，这表明意向立场的采纳受到对人工代理特定心智模型的校准。这些发现揭示了来源归因塑造了社会交流现象的神经处理过程。尽管当前大型语言模型具有语言上的复杂性，但要实现真正的社会能动性，仅凭语言能力是不够的，还需要人类对人工代理的感知和意向性归因方式发生转变。