Real-world decisions are often open ended, with goals, choice options, or evaluation criteria conceived by decision-makers themselves. Critically, the quality of decisions may heavily rely on the generation of options, as failure to generate promising options limits, or even eliminates, the opportunity for choosing them. This core aspect of problem structuring, however, is largely absent from classical models of decision-making, thereby restricting their predictive scope. Here, we take a step toward addressing this issue by developing a neurally inspired cognitive model of a class of ill-structured decisions in which choice options must be self-generated. Specifically, using a model in which semantic memory retrieval is assumed to constrain the set of options available during valuation, we generate highly accurate out-of-sample predictions of choices across multiple categories of goods. Our model significantly and substantially outperforms models that only account for valuation or retrieval in isolation or those that make alternative mechanistic assumptions regarding their interaction. Furthermore, using neuroimaging, we confirm our core assumption regarding the engagement of, and interaction between, semantic memory retrieval and valuation processes. Together, these results provide a neurally grounded and mechanistic account of decisions with self-generated options, representing a step toward unraveling cognitive mechanisms underlying adaptive decision-making in the real world.

现实世界的决策通常是开放式的，目标、选择选项或评估标准都是由决策者自己设想的。至关重要的是，决策的质量可能在很大程度上依赖于选项的生成，因为未能生成有希望的选项会限制甚至消除选择它们的机会。然而，问题结构的这一核心方面在经典决策模型中很大程度上不存在，从而限制了它们的预测范围。在这里，我们通过开发一类结构不良的决策的神经启发认知模型来解决这个问题，其中选择选项必须是自我生成的。具体来说，使用假设语义记忆检索来约束评估期间可用选项集的模型，我们可以对多个商品类别的选择生成高度准确的样本外预测。我们的模型明显优于仅考虑孤立评估或检索的模型或那些对其相互作用做出替代机械假设的模型。此外，利用神经影像学，我们证实了关于语义记忆检索和评估过程的参与和相互作用的核心假设。总之，这些结果提供了对具有自我生成选项的决策的神经基础和机械解释，代表着向揭示现实世界中自适应决策背后的认知机制迈出了一步。