Belief revision, a fundamental cognitive process involving the updating of internal representations of the world in response to new information, exhibits significant divergence between human cognition and artificial intelligence (AI) systems. This disparity primarily stems from the influence of psychological constructs, specifically cognitive dissonance, on human belief updating. Human agents frequently experience psychological discomfort when confronted with information that contradicts existing beliefs or behaviors, motivating a range of non-normative strategies to reduce this dissonance. Conversely, AI systems are designed to integrate information and revise their internal models through computational mechanisms that operate without the subjective experience of cognitive dissonance or an inherent drive for internal consistency beyond what is computationally optimal. This fundamental difference in processing mechanisms leads to distinct approaches to information integration, model revision, and the handling of contradictory evidence, with profound implications for understanding cognitive biases and optimizing human-AI collaboration.