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<p>As robots increasingly participate in social contexts requiring fairness judgments, understanding how humans neurally process inequity with robots becomes crucial. This study combined social inequity aversion theory with event-related potentials (ERPs) to examine how co-experiencing unfairness with a robot might modulate neurocognitive responses compared with solo experiences. The participants completed a money distribution task involving disadvantageous and advantageous unfair allocations, either alone or jointly with an iCub robot, while EEG recordings tracked the N1 (indicating&#xa0;early attention) and LPP (reflecting&#xa0;late emotional processing) components. The results revealed that co-experience with the robot attenuated N1 amplitudes relative to solo experience, suggesting a shift or dispersion of early attentional resources in response to inequity in these social contexts. Notably, disadvantageous unfairness elicited stronger LPPs than advantageous unfairness during co-experience only, indicating enhanced emotional discrimination when sharing unfair outcomes with a social agent. Collectively, these findings demonstrate that human–robot social contexts qualitatively reshape fairness processing dynamics, suppressing initial attentional responses while amplifying later emotional differentiation. This study extends social neuroscience frameworks to AI-mediated social environments, revealing context-dependent neural plasticity in equity evaluation and offering new perspectives on how cognitive-affective systems adapt to human–robot relationships.</p>

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Neural correlates of inequity perception in human–robot relationships: An ERP study of solo and joint experiences

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Yiming Qian,
  • Xiyu Guo,
  • Minghu Jiang,
  • Xiaoang Wan

摘要

As robots increasingly participate in social contexts requiring fairness judgments, understanding how humans neurally process inequity with robots becomes crucial. This study combined social inequity aversion theory with event-related potentials (ERPs) to examine how co-experiencing unfairness with a robot might modulate neurocognitive responses compared with solo experiences. The participants completed a money distribution task involving disadvantageous and advantageous unfair allocations, either alone or jointly with an iCub robot, while EEG recordings tracked the N1 (indicating early attention) and LPP (reflecting late emotional processing) components. The results revealed that co-experience with the robot attenuated N1 amplitudes relative to solo experience, suggesting a shift or dispersion of early attentional resources in response to inequity in these social contexts. Notably, disadvantageous unfairness elicited stronger LPPs than advantageous unfairness during co-experience only, indicating enhanced emotional discrimination when sharing unfair outcomes with a social agent. Collectively, these findings demonstrate that human–robot social contexts qualitatively reshape fairness processing dynamics, suppressing initial attentional responses while amplifying later emotional differentiation. This study extends social neuroscience frameworks to AI-mediated social environments, revealing context-dependent neural plasticity in equity evaluation and offering new perspectives on how cognitive-affective systems adapt to human–robot relationships.