As artificial intelligence (AI) becomes increasingly integrated into workflows, humans must decide when to rely on AI advice. These decisions depend on general efficacy beliefs, i.e., humans' confidence in their own abilities and their perceptions of AI competence. While prior work has examined factors influencing AI reliance, the role of efficacy beliefs in shaping collaboration remains underexplored. Through a controlled experiment (N=240) where participants made repeated delegation decisions, we investigate how efficacy beliefs translate into instance-wise efficacy judgments under varying contextual information. Our explorative findings reveal efficacy beliefs as persistent cognitive anchors, leading to systematic "AI optimism". Contextual information operates asymmetrically: while AI performance information selectively eliminates the AI optimism bias, data or AI information amplify how efficacy discrepancies influence delegation decisions. Although efficacy discrepancies influence delegation behavior, they show weaker effects on human-AI team performance. As these findings challenge transparency-focused approaches, we propose design guidelines for effective collaborative settings.

翻译：随着人工智能日益融入工作流程，人类必须决定何时采纳AI建议。这些决策取决于广义的效能信念，即人类对自身能力的信心以及对AI胜任力的认知。尽管已有研究探讨影响AI依赖度的因素，但效能信念在塑造协作关系中的作用仍未得到充分探索。通过一项受控实验（N=240），我们让参与者进行多轮委托决策，研究在不同情境信息下效能信念如何转化为具体情境的效能判断。探索性研究发现：效能信念作为持久存在的认知锚点，会导致系统性的“AI乐观主义”倾向；情境信息呈现非对称影响——AI性能信息能选择性地消除AI乐观偏差，而数据或AI信息则会放大效能差异对委托决策的影响。尽管效能差异会影响委托行为，但其对人机协作团队绩效的影响较弱。鉴于这些发现对以透明度为核心的设计思路构成挑战，我们提出了适用于高效协作场景的设计准则。