The Marine Corps utilizes virtual simulations as a training tool for ground combat operations. Currently, the artificial intelligence of the entities within these simulations do not exhibit appropriate performance degradation due to environmental conditions such as heat and humidity. These gaps impact training fidelity and can adversely impact transfer of training. To address these gaps, this paper reviews existing approaches to modeling the influence of environmental factors, specifically heat and humidity, on human performance in vigilance and attention tasks. We also explore existing environmental modeling and path finding behaviors within relevant military simulations in order to refine the scope of the problem. We present a novel agent behavior model which incorporates a modified A* search pathfinding algorithm based on empirical evidence of human information processing under the specified environmental conditions. Next, an implementation of the agent behavior model is presented in a military relevant virtual game environment. We then outline a quantitative approach to testing the agent behavior model within the virtual environment. Results show that our human information processing-based agent behavior model demonstrates plausible agent performance degradation in hot, humid temperature environments when compared to paths around the danger area in mild temperature environments. We also present a technique for demonstrating to adjacent agents the environmental temperature condition currently felt by agents in the environment. Doing so will allow for trainees to recognize a potential source of negative performance from members of their unit, and allow for better training on how to operate in spite of these challenges. The results of this research provide an approach for implementing an agent behavior model that accounts for environmental impacts on cognitive performance. We recommend future work to validate the model in a human subjects experiment to facilitate improving the realism of simulation training.