Demographic description of the sample.

Modeling decision-making under uncertainty typically relies on quantitative outcomes. Many decisions, however, are qualitative in nature, posing problems for traditional models. Here, we aimed to model uncertainty attitudes in decisions with qualitative outcomes. Participants made choices between certain outcomes and the chance for more favorable outcomes in quantitative (monetary) and qualitative (medical) modalities. Using computational modeling, we estimated the values participants assigned to qualitative outcomes and compared uncertainty attitudes across domains. Our model provided a good fit for the data, including quantitative estimates for qualitative outcomes. The model outperformed a utility function in quantitative decisions. Additionally, we found an association between ambiguity attitudes across domains. Results were replicated in an independent sample. We demonstrate the ability to extract quantitative measures from qualitative outcomes, leading to better estimation of subjective values. This allows for the characterization of individual behavior traits under a wide range of conditions.不确定性情境下的决策建模通常依赖于量化结果。然而，许多决策本质上属于质性范畴，这给传统模型带来了挑战。本研究旨在针对带有质性结果的决策场景，构建不确定性态度的建模框架。受试者需在量化（货币）与质性（医疗）两种模态下，于确定结果与获取更优结果的概率选项之间做出选择。本研究通过计算建模方法，估算受试者为质性结果赋予的主观价值，并对比了不同领域下的不确定性态度。我们的模型对实验数据拟合效果良好，其中包括针对质性结果的量化估算结果。在量化决策场景中，该模型的表现优于效用函数。此外，本研究还发现不同领域间的模糊性态度存在关联。研究结果在独立样本中得到了重复验证。本研究证实了从质性结果中提取量化指标的可行性，该方法可实现主观价值的更精准估算。这使得在多种条件下刻画个体行为特质成为可能。