Synthetic participants represent a methodologically concerning concept that threatens the integrity of UX research. Findings from previous experiments specify how AI outputs are misaligned with the behaviors and thoughts of real humans in various ways. However, industry voices keep underestimating their severity, advocating for practical compromises where good-enough data does not need to be perfect, and all issues will be solved by future tuning. Our study tackles the lack of systematic understanding of the practical issues that arise with synthetic behavior and its use for steering decisions within real contexts. Within twelve diverse first click tests (n = 3431) obtained from real UX practice, we examine the ability of GPT to predict where humans click and how they reason about their behavior. Results (e.g., significantly different distribution from real data in 53% of tasks) demonstrate critical failures to reflect the patterns in which users click on visual elements and the underlying cognitive processes. Participant personas, chain-of-thought reasoning in GPT, and different sampling parameters fail to create sensible fidelity improvements apart from inflating believability. We expose a multitude of nuanced distortions in synthetic responses that reduce their overall analytical usefulness as a decision-making resource, compared with real data. Observed distortions can be theoretically linked to the properties categorically inherent to LLMs: their statistical nature and encoding of semantic heuristics dependent on their training on linguistic data.

翻译：合成参与者代表了一种方法论上令人担忧的概念，威胁着用户体验（UX）研究的完整性。先前实验的发现揭示了AI输出与真实人类的行为和思想之间存在多种方式的错位。然而，业界声音持续低估其严重性，主张实用主义妥协——认为足够好的数据无需完美，且所有问题都将通过未来调优解决。本研究旨在解决对合成行为及其在真实情境中用于指导决策时产生的实际问题缺乏系统性认知的现状。通过来自真实UX实践的十二项不同的首次点击测试（n=3431），我们考察了GPT预测人类点击位置及其行为推理方式的能力。结果（例如，在53%的任务中与真实数据存在显著分布差异）表明，GPT在反映用户点击视觉元素的模式及其底层认知过程方面存在关键性失效。用户角色、GPT中的思维链推理以及不同采样参数除了提升可信度外，未能带来合理的保真度改进。我们揭示了合成响应中大量细微的扭曲现象，这些扭曲使其作为决策资源的整体分析实用性相比真实数据显著降低。观察到的扭曲在理论上可归结为LLM固有的类别属性：其统计本质以及依赖语言数据训练而编码的语义启发式方法。