CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention

TL;DR

CogReact is a novel deep learning framework combining drift-diffusion models with reinforcement learning to simulate human cognition under dynamic environmental stimuli, capturing individual differences and behavioral trends.

Contribution

It introduces a new integrated model that considers environmental dynamics and individual variability, advancing cognitive simulation accuracy.

Findings

Improves modeling of temporal effects of stimuli on cognition

Captures subject-specific and stimuli-specific behavioral differences

Outperforms baseline models in dynamic environments

Abstract

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.