This article describes neural models of attention. Since attention is not a disembodied process, the article explains how brain processes of consciousness, learning, expectation, attention, resonance, and synchrony interact. These processes show how attention plays a critical role in dynamically stabilizing perceptual and cognitive learning throughout our lives. Classical concepts of object and spatial attention are replaced by mechanistically precise processes of prototype, boundary, and surface attention. Adaptive resonances trigger learning of bottom-up recognition categories and top-down expectations that help to classify our experiences, and focus prototype attention upon the patterns of critical features that predict behavioral success. These feature-category resonances also maintain the stability of these learned memories. Different types of resonances induce functionally distinct conscious experiences during seeing, hearing, feeling, and knowing that are described and explained, along with their different attentional and anatomical correlates within different parts of the cerebral cortex. All parts of the cerebral cortex are organized into layered circuits. Laminar computing models show how attention is embodied within a canonical laminar neocortical circuit design that integrates bottom-up filtering, horizontal grouping, and top-down attentive matching. Spatial and motor processes obey matching and learning laws that are computationally complementary to those obeyed by perceptual and cognitive processes. Their laws adapt to bodily changes throughout life, and do not support attention or conscious states.