Current threat modeling methods focus on understanding the protected network from the perspective of the owners of those networks rather than on comprehensively understanding and integrating the methodology and intent of the threat. We argue that layering the human factors of the adversary over the existing threat models increases the ability of cybersecurity practitioners to truly understand possible threats. Therefore, we need to expand existing adversary and threat modeling approaches in cyberspace to include the representation of human factors of threats, specifically motivations, biases, and perceptions. This additional layer of modeling should be informed by an analysis of cyber threat intelligence reporting. By creating and adopting this expanded modeling, cybersecurity practitioners would have an understanding of how an adversary views their network, which would expand their ability to understand how their network is most likely to be attacked.