CogReact：一个受动态干预调制的人类认知反应建模的强化学习框架
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention
AI总结：
提出CogReact框架，结合漂移扩散与深度强化学习，模拟动态环境刺激对人类认知过程的影响，定量和定性上均优于基线，为动态情境下认知反应建模提供了数据驱动方法。
摘要：
使用深度神经网络作为计算模型来模拟认知过程，可以为人类行为动力学提供关键见解。当环境高度动态时，挑战随之而来，这模糊了刺激-行为关系。然而，当前大多数研究侧重于在理想条件下模拟人类认知行为，忽视了环境干扰的影响。我们提出了CogReact，它将漂移扩散模型与深度强化学习相结合，以模拟动态环境刺激对人类认知过程的细粒度影响。定量上，它通过考虑环境刺激对认知过程的时间效应来改进认知建模，并捕捉受试者特定和刺激特定的行为差异。定性上，它捕捉了刺激下人类认知过程的总体趋势，优于基线方法。我们的方法在各种认知任务的不同环境影响下进行了检验。总体而言，它展示了一种强大的、数据驱动的方法，用于模拟、对齐和理解动态情境中人类认知反应的变幻莫测。
英文摘要：
Using deep neural networks as computational models to simulate cognitive process can provide key insights into human behavioral dynamics. Challenges arise when environments are highly dynamic, obscuring stimulus-behavior relationships. However, the majority of current research focuses on simulating human cognitive behaviors under ideal conditions, neglecting the influence of environmental disturbances. We propose CogReact, integrating drift-diffusion with deep reinforcement learning to simulate granular effects of dynamic environmental stimuli on human cognitive process. Quantitatively, it improves cognition modelling by considering temporal effect of environmental stimuli on cognitive process and captures both subject-specific and stimuli-specific behavioural differences. Qualitatively, it captures general trends in human cognitive process under stimuli, better than baselines. Our approach is examined in diverse environmental influences on various cognitive tasks. Overall, it demonstrates a powerful, data-driven methodology to simulate, align with, and understand the vagaries of human cognitive response in dynamic contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
