CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention
Songlin Xu, Xinyu Zhang

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
Taxonomy
TopicsMental Health Research Topics
