The Paradox of Authority and Trust in AI-Generated Content: Cognitive Decision Mechanisms Based on the Drift Diffusion Model
1.School of Psychology, Zhejiang Normal University, Jinhua 321004, China; 2.Zhejiang Philosophy and Social Science Laboratory for the Mental Health and Crisis Intervention of Children and Adolescents, Zhejiang Normal University, Jinhua, 321004, China
摘要本研究旨在采用2×2的混合实验探讨新闻生成形式与信源权威性对信任决策的影响，并构建漂移扩散模型探索这一影响的认知机制。结果发现：相比非权威信源，权威信源下个体对 AI 生成新闻的信任度降低、决策反应时延长；构建漂移扩散模型显示，此时信任决策的证据累积速度（v）和起始点偏差（z）均较低。研究从行为与认知层面共同揭示了权威新闻媒体在智能化转型的过程中可能面临的信任风险。
Abstract：This study employs a 2×2 mixed experimental design to investigate how news formats and source authority influence trust decisions, while constructing a drift diffusion model to explore the cognitive mechanisms underlying these effects. The findings demonstrate that individuals exhibit reduced trust in AI-generated news and prolonged decision response times when trusting authoritative sources compared to non-authoritative ones. The drift diffusion model reveals that both the evidence accumulation rate (v) and initial point deviation (z) in trust decision-making are lower under such conditions. The research collectively addresses trust risks faced by authoritative news media during their intelligent transformation process, providing insights from both behavioral and cognitive perspectives.
杨娟，苏胜，康春花. 权威性与AI生成内容的信任悖论：基于漂移扩散模型的认知决策机制[J]. 应用心理学, 0, (): 1-.
YANG Juan，SU Sheng， KANG Chunhua. The Paradox of Authority and Trust in AI-Generated Content: Cognitive Decision Mechanisms Based on the Drift Diffusion Model. 应用心理学, 0, (): 1-.
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