CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention
This addresses the challenge of modeling human cognitive reactions under environmental disturbances for researchers in cognitive science and AI, though it appears incremental as it builds on existing methods like drift-diffusion and reinforcement learning.
The paper tackled the problem of simulating human cognitive processes in dynamic environments by proposing CogReact, which integrates drift-diffusion with deep reinforcement learning, resulting in improved modeling that captures subject-specific and stimuli-specific behavioral differences and aligns better with human trends than baselines.
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.