The field of robotics and computer vision is shifting towards more human-inspired visual processing methods. Researchers are exploring ways to incorporate human-like active gaze and attention mechanisms into robotic systems, which has led to improved efficiency and performance in various tasks. The use of foveated vision transformers and gaze imitation models has shown significant promise in reducing computational overhead and enhancing robustness to distractions. Additionally, the integration of neuroscience theories, such as binding by synchrony, has led to the development of novel mechanisms for addressing the visual binding problem in neural networks. Noteworthy papers include: Look, Focus, Act, which introduces a framework for incorporating human gaze into robotic policies using foveated vision transformers. GASPnet proposes a novel mechanism that combines aspects of Transformer attentional operations with the neuroscience theory of binding by synchrony, leading to improved noise robustness and generalization abilities.