人类不把AI的讽刺话当有意为之,神经反应也更弱。
Neural Dynamics of AI-attributed Irony Reveal a Partial Intentional Stance
- 用脑电图对比人与AI说反话时的反应差异
- 对AI的讽刺理解率下降,脑波响应减弱
- 越觉得AI真诚,越容易理解其讽刺
随着大语言模型被用作社交代理并学习幽默和讽刺表达,一个关键问题浮现:当面对带有讽刺意味的AI话语时,人们是否会将其视为有意识的沟通行为?本研究通过脑电图(EEG)测量事件相关电位(ERPs),比较了人们对相同讽刺语句分别归因于AI伙伴或人类时的行为与神经反应。结果发现,人们并未完全对AI通信采取意图立场:当讽刺话语被归因于AI时,参与者识别为讽刺的比例显著低于归因于人类的情况。相应地,对AI来源的讽刺,在初始语义处理(P200)和语用重构(P600)阶段均表现出较弱的神经响应。此外,个体对AI真诚度和可信度的感知调节了这些神经反应,正面评价可降低认知负担,促进语用理解。这表明,对AI是否赋予意图是灵活且动态调整的过程,受人们对人工代理的心理模型影响。尽管人工智能沟通能力提升,但其社会主体性仍受限于人类对其意图性的低度归因。这一发现对理解人类社会认知及设计社交型AI系统具有重要意义。
原文摘要 · Abstract (English)
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 them as deliberate communicative acts? This study investigated whether people adopt an intentional stance, ascribing mental states to explain behavior, when comprehending AI-attributed irony. Irony provides a testbed because understanding it requires distinguishing intentional contradictions from unintended errors through pragmatic reanalysis. Using electroencephalography (EEG) to measure event-related potentials (ERPs), we compared behavioral and neural responses to identical ironic utterances attributed to either an AI companion or a human. We found that people do not fully adopt an intentional stance towards AI communication. Participants interpreted contextually incongruent utterances as irony significantly less often when attributed to an AI than when attributed to a human. Correspondingly, we observed attenuated neural responses to AI-attributed irony compared to human-attributed irony during both initial semantic processing (P200) and pragmatic reanalysis (P600). In addition, these neural responses were modulated by individual perceptions of AI sincerity and trustworthiness, with positive perceptions facilitating pragmatic comprehension by reducing cognitive effort. This suggests that adopting an intentional stance toward AI is a flexible and adaptive process, dynamically shaped by people's mental models of artificial agents. These findings reveal that despite advances in communicative competence, AI systems face a barrier to social agency: humans process input from artificial interlocutors with reduced ascription of intentionality. This has important implications for understanding human social cognition and for designing AI systems intended for social interaction.
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