Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this article we present a general framework that can address the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to naturalistic binary choices from domains such as risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and social judgment. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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记忆中的决策过程和内容。

存储在记忆中的信息会影响大多数日常决策任务中偏好和信念的形成。这些信息的丰富性，以及交互记忆和决策过程中固有的复杂性，使得对此类决策的定量模型驱动分析非常困难。在本文中，我们提出了一个通用框架，可以解决构建基于自然记忆的决策的正式模型的理论和方法障碍。我们的框架在单个集成认知系统内实现了已建立的记忆搜索和决策理论，并使用计算语言模型来量化记忆和决策过程所涉及的思想。因此它既可以描述内容从内存中采样的信息，以及过程参与检索和评估此信息以做出决定。此外，我们的框架易于处理，表征基于记忆的决策的参数可以使用来自现有实验任务的想法列表和选择数据来恢复，进而用于对选择概率、审议时间、检索到的想法进行定量预测，以及决策背景的影响。我们通过将其应用于风险感知、消费者行为、财务决策、道德决策、法律决策、食物选择和社会判断等领域的自然二元选择来展示我们框架的力量和普遍性。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）