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

Songlin Xu, Xinyu Zhang
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:69878-69907, 2025.

Abstract

Using deep neural networks as computational models to simulate cognitive processes 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, which integrates drift-diffusion with deep reinforcement learning to simulate granular effects of dynamic environmental stimuli on the human cognitive process. Quantitatively, it improves cognition modeling by considering the temporal effect of environmental stimuli on the cognitive process and captures both subject-specific and stimuli-specific behavioral differences. Qualitatively, it captures general trends in the human cognitive process under stimuli. We examine our approach under diverse environmental influences across 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-xu25aj, title = {{C}og{R}eact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention}, author = {Xu, Songlin and Zhang, Xinyu}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {69878--69907}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/xu25aj/xu25aj.pdf}, url = {https://proceedings.mlr.press/v267/xu25aj.html}, abstract = {Using deep neural networks as computational models to simulate cognitive processes 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, which integrates drift-diffusion with deep reinforcement learning to simulate granular effects of dynamic environmental stimuli on the human cognitive process. Quantitatively, it improves cognition modeling by considering the temporal effect of environmental stimuli on the cognitive process and captures both subject-specific and stimuli-specific behavioral differences. Qualitatively, it captures general trends in the human cognitive process under stimuli. We examine our approach under diverse environmental influences across 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.} }
Endnote
%0 Conference Paper %T CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention %A Songlin Xu %A Xinyu Zhang %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-xu25aj %I PMLR %P 69878--69907 %U https://proceedings.mlr.press/v267/xu25aj.html %V 267 %X Using deep neural networks as computational models to simulate cognitive processes 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, which integrates drift-diffusion with deep reinforcement learning to simulate granular effects of dynamic environmental stimuli on the human cognitive process. Quantitatively, it improves cognition modeling by considering the temporal effect of environmental stimuli on the cognitive process and captures both subject-specific and stimuli-specific behavioral differences. Qualitatively, it captures general trends in the human cognitive process under stimuli. We examine our approach under diverse environmental influences across 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.
APA
Xu, S. & Zhang, X.. (2025). CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:69878-69907 Available from https://proceedings.mlr.press/v267/xu25aj.html.

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