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

arXiv: https://arxiv.org/abs/2301.06216 · HTML: https://mlsanity.com/paper/2301.06216/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 77 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 20 of 20)

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- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
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- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Learning to Execute](https://mlsanity.com/paper/1410.4615/) (2014)
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- [Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study](https://mlsanity.com/paper/1706.08606/) (2017)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [Cognitive Model Priors for Predicting Human Decisions](https://mlsanity.com/paper/1905.09397/) (2019)
- [Modeling Human Exploration Through Resource-Rational Reinforcement Learning](https://mlsanity.com/paper/2201.11817/) (2022)
- [A neural network walks into a lab: towards using deep nets as models for human behavior](https://mlsanity.com/paper/2005.02181/) (2020)
- [End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior](https://mlsanity.com/paper/2007.08723/) (2020)
- [Behavior-Based Machine-Learning: A Hybrid Approach for Predicting Human Decision Making](https://mlsanity.com/paper/1611.10228/) (2016)
- [Anytime Prediction as a Model of Human Reaction Time](https://mlsanity.com/paper/2011.12859/) (2020)
- [Modeling Human Categorization of Natural Images Using Deep Feature Representations](https://mlsanity.com/paper/1711.04855/) (2017)

## Abstract (arXiv)

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.