Modelling human cognition can provide key insights into behavioral dynamics under changing conditions. This enables synthetic data generation and guides adaptive interventions for cognitive regulation. Challenges arise when environments are highly dynamic, obscuring stimulus-behavior relationships. We propose a cognitive agent integrating drift-diffusion with deep reinforcement learning to simulate granular stress effects on logical reasoning process. Leveraging a large dataset of 21,157 logical responses, we investigate performance impacts of dynamic stress. This prior knowledge informed model design and evaluation. Quantitatively, the framework improves cognition modelling by capturing both subject-specific and stimuli-specific behavioural differences. Qualitatively, it captures general trends in human logical reasoning under stress. Our approach is extensible to examining diverse environmental influences on cognition and behavior. Overall, this work demonstrates a powerful, data-driven methodology to simulate and understand the vagaries of human logical reasoning process in dynamic contexts.

翻译：对人类认知的建模能够揭示动态条件下行为动力学的关键见解。这有助于生成合成数据，并指导认知调节的自适应干预。当环境高度动态、刺激-行为关系模糊时，挑战随之涌现。我们提出一种融合漂移扩散与深度强化学习的认知智能体，用于模拟压力对逻辑推理过程的细粒度影响。利用包含21,157个逻辑响应的庞大数据集，我们研究了动态压力对性能的影响。该先验知识指导了模型设计与评估。在定量层面，该框架通过捕捉受试者特异性与刺激特异性的行为差异，改进了认知建模。在定性层面，它捕捉了压力下人类逻辑推理的总体趋势。我们的方法可扩展至研究不同环境因素对认知与行为的影响。总体而言，这项工作展示了一种强大的、数据驱动的方法论，用于模拟并理解动态情境中人类逻辑推理过程的变化无常。