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Modelling human cognition can provide key insights into behavioral dynamicsunder changing conditions. This enables synthetic data generation and guidesadaptive interventions for cognitive regulation. Challenges arise whenenvironments are highly dynamic, obscuring stimulus-behavior relationships. Wepropose a cognitive agent integrating drift-diffusion with deep reinforcementlearning to simulate granular stress effects on logical reasoning process.Leveraging a large dataset of 21,157 logical responses, we investigateperformance impacts of dynamic stress. This prior knowledge informed modeldesign and evaluation. Quantitatively, the framework improves cognitionmodelling by capturing both subject-specific and stimuli-specific behaviouraldifferences. Qualitatively, it captures general trends in human logicalreasoning under stress. Our approach is extensible to examining diverseenvironmental influences on cognition and behavior. Overall, this workdemonstrates a powerful, data-driven methodology to simulate and understand thevagaries of human logical reasoning process in dynamic contexts.

Modelling human logical reasoning process in dynamic environmental stress with cognitive agents