Distortion between the operating field and preoperative images increases as image-guided surgery progresses. Retraction is a typical early-stage event that causes significant tissue deformation, which can be modeled as an intraoperative compensation strategy. This study compares the predictive power of incremental versus single-step retraction models in the porcine brain. In vivo porcine experiments were conducted that involved implanting markers in the brain whose trajectories were tracked in CT scans following known incremental deformations induced by a retractor blade placed interhemispherically. Studies were performed using a 3-D consolidation model of brain deformation to investigate the relative predictive benefits of incremental versus single-step retraction simulations. The results show that both models capture greater than 75% of tissue loading due to retraction. We have found that the incremental approach outperforms the single-step method with an average improvement of 1.5% -3%. More importantly it also preferentially recovers the directionality of movement, providing better correspondence to intraoperative surgical events. A new incremental approach to tissue retraction has been developed and shown to improve data-model match in retraction experiments in the porcine brain. Incremental retraction modeling is an improvement over previous single-step models, which does not incur additional computational overhead. Results in the porcine brain show that even when the overall displacement magnitudes between the two models are similar, directional trends of the displacement field are often significantly improved with the incremental method.