mlsanityData to 14 Oct 2026

A funny companion: Distinct neural responses to AI- versus human-attributed humor

arXiv 2509.10847 ↗First posted 13 Sep 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

As artificial intelligence (AI) companions become capable of human-like communication, including telling jokes, understanding how people cognitively and affectively respond to AI-attributed humor becomes increasingly important. This study used EEG to compare how people process puns versus controls attributed to either an AI agent or a human. In an interaction task, participants guessed punchlines based on joke setups before they were disclosed by the interlocutor. Behavioral analysis revealed that participants rated AI- and human-attributed humor as comparably funny. However, neurophysiological data showed that AI-attributed humor elicited a reduced and sustained N400 effect compared to human-attributed humor, suggesting reduced cognitive effort in semantic conflict detection and resolution, or attenuated feedback-related processing. This was also accompanied by a larger late positive potential (LPP), reflecting intensified late-stage affective and evaluative processing. This likely reflects a process where the brain reconciles AI's delivery of culturally embedded puns with prior low expectations, leading to intensified affective engagement and interlocutor model updating. Individual differences in social perceptions further influenced neural responses. Higher perceived AI sincerity and trustworthiness were associated with a globally reduced N400, facilitating semantic integration for both humorous and non-humorous AI language. Additionally, increased AI trustworthiness predicted an enhanced LPP, indicating a more intensified updating of the interlocutor model during humor comprehension. These findings indicate that the brain's neural sensitivity to AI-attributed humor bypasses biases like algorithm aversion. This highlights the brain's adaptation to humor from a novel source and underscores humor's potential for fostering genuine engagement in human-AI social interaction.

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References · 5

  1. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  2. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  3. 2023Do large language models resemble humans in language use?cited by 34
  4. 2024A review on the use of large language models as virtual tutorscited by 13
  5. 2019What's in an accent? The impact of accented synthetic speech on lexical choice in human-machine dialoguecited by 4

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