A funny companion: Distinct neural responses to perceived AI- versus humangenerated humor

2025-09-13

AI Summary

This study investigates neural and behavioral differences in human responses to AI-generated versus human-generated humor, challenging the “algorithm aversion” hypothesis. Method: Using high-temporal-resolution EEG combined with behavioral assessment, we quantified N400 (indexing semantic expectancy violation) and late positive potential (LPP; reflecting sustained emotional processing) to compare cognitive load, surprise, and affective reward dynamics across humor types. Contribution/Results: Behaviorally, perceived funniness did not differ significantly between AI- and human-generated humor. Neurally, AI-generated humor elicited a smaller early N400 (indicating reduced initial semantic prediction load), greater surprise, and progressively enhanced LPP amplitudes over time (signifying escalating affective reward). Critically, this “delayed reinforcement” neural trajectory was significantly moderated by individual AI trust. These findings provide the first neurocognitive evidence of adaptive, experience-dependent recalibration of human predictive models about AI capabilities through humor, establishing a novel paradigm for studying the neural foundations of human–AI social interaction.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

As AI companions become capable of human-like communication, including telling jokes, understanding how people cognitively and emotionally respond to AI humor becomes increasingly important. This study used electroencephalography (EEG) to compare how people process humor from AI versus human sources. Behavioral analysis revealed that participants rated AI and human humor as comparably funny. However, neurophysiological data showed that AI humor elicited a smaller N400 effect, suggesting reduced cognitive effort during the processing of incongruity. This was accompanied by a larger Late Positive Potential (LPP), indicating a greater degree of surprise and emotional response. This enhanced LPP likely stems from the violation of low initial expectations regarding AI's comedic capabilities. Furthermore, a key temporal dynamic emerged: human humor showed habituation effects, marked by an increasing N400 and a decreasing LPP over time. In contrast, AI humor demonstrated increasing processing efficiency and emotional reward, with a decreasing N400 and an increasing LPP. This trajectory reveals how the brain can dynamically update its predictive model of AI capabilities. This process of cumulative reinforcement challenges "algorithm aversion" in humor, as it demonstrates how cognitive adaptation to AI's language patterns can lead to an intensified emotional reward. Additionally, participants' social attitudes toward AI modulated these neural responses, with higher perceived AI trustworthiness correlating with enhanced emotional engagement. These findings indicate that the brain responds to AI humor with surprisingly positive and intense reactions, highlighting humor's potential for fostering genuine engagement in human-AI social interaction.

Problem

• Comparing neural responses to AI versus human humor
• Investigating cognitive and emotional processing differences
• Examining how social attitudes modulate AI humor perception

Innovation

• EEG measures neural humor processing differences
• AI humor shows reduced N400, increased LPP responses
• Social attitudes modulate AI humor neural engagement