Iterative decision-making has been widely studied in human cognition and is recognized for its energy efficiency and suitability for biological computations. In contrast, instance segmentation models adopt strategies that diverge from human vision, each presenting unique strengths and limitations. In this paper, we examine the grouping problem in segmentation models and demonstrate that iterative recurrent processing facilitates the identification of diverse solutions and can enhance grouping capabilities. Our experiments further reveal that recurrent processing accelerates convergence and can generate diverse solutions that can help mitigate suboptimal spurious minima. Our work focuses on confounding cases, which have become increasingly relevant as systems are increasingly deployed in safety-critical environments.