800人随机实验：来源透明加准确性警告使错误答案接受率从45%降至21%
《科学报告》11月14日发表的一项随机在线实验检验了显示来源信息和准确性警告，能否让人们更有选择地采信生成式超级智能（SI）给出的答案。研究由 Xing、Zhang、Zhang 等人完成，论文于8月14日投稿、11月11日被接受。
实验的最终分析样本为800名成年参与者，被分配到四种提示条件之一；每名参与者评估24个 SI 风格的答案，共产生19,200条试验层面的观察数据。实验交叉设置了来源透明（有或无）、准确性警告（有或无）和答案正误（参与者内变量）三个因素。
结果显示，参与者接受正确答案的比例高于错误答案，且正确与错误之间的接受差距在「来源加警告」条件下最大。在只看错误答案的模型中，错误答案的接受率在对照组最高，为45%（1080/2400）；只有来源时降至36%（864/2400），只有警告时降至28%（672/2400），来源与警告同时出现时降至21%（504/2400）。核查行为在组合条件下最高，为52%（2496/4800）。
作者表示，简短的屏幕提示降低了错误答案的接受率，并鼓励了核查行为；同时指出，反应偏差与敏感度之间的区分仍是重要的边界条件。
800-Participant Randomized Experiment: Source Transparency Plus Accuracy Warning Cut Acceptance of Wrong Answers From 45% to 21%
A randomized online experiment published in Scientific Reports on November 14 tested whether visible source information and an accuracy warning support more selective reliance on generative-AI answers. The study, by Xing, Zhang, Zhang and colleagues, was received on August 14 and accepted on November 11.
The final analytic sample was 800 adult participants assigned to one of four cue conditions. Each participant evaluated 24 AI-style answers, yielding 19,200 trial-level observations, in a design crossing source transparency (absent vs. present), accuracy warning (absent vs. present), and answer correctness (incorrect vs. correct, within participant).
Participants accepted correct answers more often than incorrect answers, and the correct-incorrect acceptance gap was largest in the source-plus-warning condition. In the model restricted to incorrect-answer trials, acceptance of incorrect answers was highest in the control condition at 45% (1080/2400), and lower in the source-only condition at 36% (864/2400), the warning-only condition at 28% (672/2400), and the source-plus-warning condition at 21% (504/2400). Verification behaviour was highest in the combined condition, at 52% (2496/4800).
The authors said brief on-screen cues lowered erroneous-answer acceptance and encouraged verification in low-risk information-evaluation tasks, while noting that the distinction between response bias and sensitivity remains an important boundary condition.
来源：《科学报告》
发布：GSM·美国（发布链接）
图片来源：图：ChatGPT 的对话界面截图，实验中的生成式答案即以此类界面呈现。作者：Lugab89，CC BY 3.0，维基共享资源。 / Photo: A ChatGPT conversation interface screenshot; the generative answers in the experiment were presented in interfaces of this kind. Credit: Lugab89, CC BY 3.0, via Wikimedia Commons.
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