发表机构
The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有逆决策建模未覆盖言语化认知任务响应动态的问题，提出FIDM模型，在400名老年人的杂货购物对话任务数据上验证其可选择性估计因子、保留动作差异，且能提升认知状态分类性能。
AI 中文摘要
逆决策建模可从观测行为推断决策过程的潜在属性，但现有方法主要依赖动作轨迹。在言语化认知任务中，任务执行还会产生仅用动作的建模方式未覆盖的响应动态，如言语产生、交互与犹豫。我们提出因子化解码策模型（FIDM），将个体的任务执行似然分解为动作因子与努力因子，由独立的个体特定参数控制。从原始言语记录中，语言模型生成结构化的任务执行轨迹用于因子化推理。针对400名执行认知筛查用杂货购物对话任务的老年人数据，控制恢复实验显示可选择性估计目标因子；匹配的半合成条件表明，即使聚合行为摘要匹配，FIDM仍能保留动作执行的差异。动作证据可进一步定位参与者间任务定义的偏差。在认知状态分类中，FIDM提供与临床评分、轨迹摘要及冻结语言表示互补的信息，在二元设置下对所有评估基线均实现一致提升。
英文摘要
Inverse decision modeling infers latent properties of decision processes from observed behavior, but existing formulations rely primarily on action trajectories. In verbalized cognitive tasks, task execution also produces response dynamics that action-only formulations leave unmodeled, such as verbal production, interaction, and hesitation. We propose a factorized inverse decision model (FIDM) that decomposes each individual's task-execution likelihood into an action factor and an effort factor, governed by separate individual-specific parameters. From raw verbal transcripts, a language model produces structured task-execution traces for factorized inference. On data from 400 older adults performing a grocery-shopping dialog task for cognitive screening, controlled recovery shows selective estimation of the intended factors, while matched semi-synthetic conditions show that FIDM preserves action-execution distinctions even when aggregate behavioral summaries are matched. Action evidence further localizes task-defined deviations across participants. In cognitive-status classification, FIDM provides information complementary to clinical scores, trajectory summaries, and frozen language representations, with consistent gains across all evaluated baselines in the binary setting.
