Visual Object Tracking is a challenging task that involves identifying and following an object in a video stream. Traditional tracking algorithms are often limited in their ability to adapt to changes in the environment, such as occlusion or changes in lighting conditions. To address this limitation, online learning algorithms have been developed, which enable the tracker to continuously learn and adapt to the environment as it tracks the object. In this work, the use of Perceptual Learning for Online Learning is investigated in combination with a visual object tracking model with explainable architecture for learning multiple sub-tasks. The effectiveness of this approach is valued on various benchmark datasets and compared to existing state-of-the-art tracking algorithms. Experimental results demonstrate that the approach outperforms known tracking algorithms in terms of accuracy and robustness, particularly in challenging scenarios such as occlusion and lighting changes. We conclude that the use of Perceptual Learning for Online Learning in combination with tracking transformers is a promising direction for future research in object tracking.